Abstract

There are two main challenges in the diagnosis of blood cancer. The first is to diagnose cancer from healthy control, and the second is to identify the types of blood cancer. The chemometrics method combined with laser-induced breakdown spectroscopy (LIBS) can be used for cancer detection. However, chemometrics methods were easily influenced by the spectral feature redundancy and noise, resulting in low accuracy rate because of their simple structure. We proposed an approach using LIBS combined with the ensemble learning based on the random subspace method (RSM). The serum samples were dripped onto a boric acid substrate for LIBS spectrum collection. The complete blood cancer sample set include leukemia [acute myeloid leukemia (AML) and chronic myelogenous leukemia (CML)], multiple myeloma (MM), and lymphoma. The results showed that the accuracy rates using k nearest neighbors (kNN) and linear discriminant analysis (LDA) only were 88.14% and 94.45%, respectively, while using RSM with LDA (RSM-LDA), the average accuracy rate was improved from 94.45% to 98.34%. Furthermore, the variable importance of spectral lines (Na, K, Mg, Ca, H, O, N, C-N) were evaluated by the RSM-LDA model, which can improve the recognition ability of blood cancer types. Comparing the RSM-LDA model and only with LDA, the results showed that the average accuracy rate for cancer type identification was improved from 80.4% to 91.0%. These results demonstrate that LIBS combined with the RSM-LDA model can discriminate the blood cancer from the health control, as well as the recognition the types for blood cancers.

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

1. Introduction

Blood cancer is a form of cancer that attacks the blood, bone marrow, or lymphatic system. Nowadays blood cancer detection still remains a significant challenge in clinical medicine [13]. The three most common types of blood cancer are leukemia, multiple myeloma (MM), and malignant lymphoma [4]. Leukemia is the most common hematological malignancy [5], and acute myeloid leukemia (AML) and chronic myelogenous leukemia (CML) are the different types of leukemia. Lymphoma is a cancer disease that mainly affects the human lymph and hematopoietic system [6]. MM is a malignant disease in which clonal plasma cells proliferate abnormally [7]. The diagnosis method is the pathological biopsy of the bone marrow [810]. Although the accuracy rate (the rate of the number of samples correctly classified to the total number of samples) of pathological biopsy is high, the disadvantages of pathological biopsy, such as complex operation, high cost, and need for professionals restrict its application. Other blood cancer diagnostic methods include positron emission computed tomography [11], magnetic resonance imaging [12,13], and tumor marker detection [14,15]. However, image diagnostic techniques are not suitable for blood cancer detection at early stage because of unobvious image features. Hence, it is significant to find a fast, economical, and robust technology in blood cancer identification.

Laser-induced breakdown spectroscopy (LIBS), a proven optical emission spectroscopy (OES) analytical technique, has advantages that include rapid detection, simple pretreatment, and real-time analysis [6,1618]. LIBS has been applied to bacterial identification [19,20], geological exploration [21], and industrial monitoring [22,23]. In recent years, LIBS technology has been employed in tissue imaging and cancer detection for clinical medicine [2426]. LIBS has also been investigated for the diagnosis of tumors. The most common method is to diagnose tumors based on the element content difference between normal and tumor tissue. Han et al. discriminated against the cutaneous melanoma from the surrounding skin using LIBS combined with principal component analysis (PCA) and linear discriminant analysis (LDA) [27]. The sensitivity and specificity of the diagnosis were over 95%, but a drawback of tissue detection is the need for surgical sampling. The other method to diagnose cancer is by comparing the element difference between the serum of cancer patients and health controls. Chen et al. diagnosed lymphoma and multiple myeloma patients and healthy control by detecting their sera using LIBS combined with chemometric methods [28]. Their work indicated that kNN model had the best performance, and the discrimination accuracy rate was 96%. Tameze et al. calculated the differences between LIBS image of ovarian cancer mice blood and the health control [29]. Their work showed that in the positive specimens the plasma state is richer and the intensity is augmented. The references above have made significant contributions to tumor detection using LIBS. However, few researches [6,28] conducted on blood cancer types diagnosis using LIBS have been reported. Specially, the accuracy rates are limited by only using the chemometrics method such as LDA or kNN combined with LIBS, because the simple structure of identification models were easily influenced by the spectral feature redundancy and noise. Therefore, to develop an identification model is an important step for rapid, efficient and steady blood cancer detection.

In this study, to overcome the drawbacks of a single model combined with LIBS, we proposed an approach using the ensemble learning based on the random subspace method (RSM) such as RSM-LDA combined with LIBS. The complete blood cancer sample set include leukemia (AML and CML), MM, and lymphoma. The variable importance (VI) of selected lines was calculated to evaluate the importance of the element. The RSM-LDA model was used for identifying the type of blood cancers. A more stringent evaluation index was proposed for model evaluation.

2. Experiments and methods

2.1 Experimental setup

For experimental setup, a laser beam from a Q-switched Nd:YAG laser (wavelength: 532 nm; pulse energy: 30 mJ; repetition rate: 10 Hz; pulse width: 8 ns; French Quantel, Brilliant B) passed the reflector and focusing mirror (focal length: 150 mm) onto the sample surface. The plasma emission enters the spectrometer via a light collector (Ocean Optics, 84-UV-25, wavelength range: 200-2000 nm) and UV-enhanced fiber optic with 50 µm core. The LIBS signal was obtained by an echelle spectrometer (resolution: λ/Δλ = 5000; spectral range: 200 −950 nm; United Kingdom, Andor Tech., Mechelle 5000) coupled with an intensified charge-coupled device (ICCD) (United Kingdom, Andor Tech., iStar DH-334T). For high spectral intensity and signal to noise (SNR), the gate delay and gate width of ICCD were set to be 1 and 9 µs, respectively. The diameter of spot size was about 100 µm, and the experiment was conducted in an air environment.

2.2 Sample pretreatment

Serum samples were collected from six healthy controls, six AML patients, six CML patients, six MM patients and eight lymphoma patients in the Institute of Hematology and Blood Diseases Hospital. The blood cancer patients were previously diagnosed by bone marrow biopsy and pathological examination of biopsies, which are the gold standard for diagnosis of blood cancer. the liquid serum sample was transformed to solid form, which prevents liquid splashes leading to spectral instability. The pretreatment takes about 15 minutes, and the detail steps are described below:

  • (1) Serum samples were obtained by blood sample centrifugation, which lasted less than 10 minutes. The centrifugation can be performed for a batch of serum samples.
  • (2) The pellets were obtained by pressing boric acid powder, purchased from Sinopharm Chemical Reagent Co., Ltd, at 20 Mpa, and the diameter of the pellet is 40 mm. 50 µl of serum was poured onto the pellet using a pipette.
  • (3) Each pellet serum sample was air-dried for less than 5 mins.
The fluctuation of the spectral lines caused by the coffee ring effect leads to a low accuracy rate for blood cancer diagnosis. To improve the stability of the spectra, the laser beam scanned the sample surface in a rectangular area which covered the whole “coffee ring”. Then we accumulate all the pulses in the rectangle area to get one LIBS spectrum.

2.3 Algorithm description

The LDA model can be used to find a linear combination of features that characterizes or separates two or more classes of objects or events [30]. With the advantage of simplicity and rapidity, LDA is widely applied in tumor detection of LIBS [6,27,28].

The RSM-LDA model, the combination of LDA models by the RSM model, is an ensemble learning method [31]. The ensemble learning model obtains better predictive performance than weak classifiers using any of the constituent learning algorithms alone. The commonly used weak classifier is LDA and kNN, which were also widely used in LIBS for cancer diagnosis [6,27,28]. In our work, we adopted the RSM to improve the accuracy rates of LDA and kNN. The RSM performs the following steps [31]:

  • (1) Choose m spectral lines randomly from whole spectral lines.
  • (2) Train a weak learner using the m spectral lines.
  • (3) Repeat steps (1) and (2) until there are n weak learners, and predict by taking an average of the score prediction of the weak learners, and classify the category with the highest average score.

3 Results and discussion

3.1 Spectral analysis

With the experimental set-up described and the optimal spectrum acquisition parameters, the observed elements included magnesium (Mg), sodium (Na), potassium (K) oxygen (O), nitrogen (N), Calcium (Ca), and molecular bands C-N. The five kinds of LIBS spectra for serum samples and one substrate sample range from 200 to 850 nm and are shown in Fig. 1.

 

Fig. 1. The whole spectra of (a) the substrate, (b) healthy control serum sample (c) AML serum sample, (d) CML serum sample, (e) MM serum samples, and (f) lymphoma serum samples.

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The difference spectra between the sample and the substrate were used to eliminate the effect of the substrate. 23 lines were selected from the whole spectrum for the discrimination analysis, including lines of Ca, Na, K, Mg N, O, and molecular bands C-N. The details of the selected lines are listed in Table 1.

Tables Icon

Table 1. Analytical lines used for discrimination analysis.

3.2 Diagnosis between the blood cancer and healthy control

3.2.1 Discrimination of blood cancer with the kNN model and the LDA model

For each kind of serum sample, ten spectra were obtained. In total, 320 spectra were collected for discrimination analysis. For each kind of blood cancer, the cancer spectra and the health control spectra were randomly chosen for a training set and a test set with the ratio of 1:1. The random sample selection was repeated 500 times. The average identification accuracy rate [accuracy rate=(TN + TP)/(TN + TP + FN + FP)], average specificity [specificity = TN/(TN + FP)], average sensitivity [sensitivity = TP/(TP + FN)], average misdiagnosis rate [misdiagnosis rate = FP/(FP + TN)], average omission diagnostic rate [omission diagnostic rate = FN/(TP + FN)], and average area under the curve (AUC) was used to evaluate the classifier. TP, TN, FP, and FN stand for true positive, true negative, false positive, and false negative, respectively.

The kNN model and the LDA model are commonly used in the blood cancer detection using LIBS [6,28]. The classification results are shown in Figs. 2(a)–2(b), the accuracy rates of the LDA model were listed in Table 2. The LDA model obtained a poor classification performance because it is a linear classifier.

 

Fig. 2. The classification results for AML versus (vs) healthy control (HC), CML vs HC, MM vs HC, and Lymphoma vs HC using (a) LDA, (b) KNN.

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Tables Icon

Table 2. The accuracy rate of four classifiers for identification between cancerous sample and healthy control (HC).

The accuracy rates of the kNN model were listed in Table 2. There are two main reasons for the poor performance of kNN classification. One is that the kNN model were easily influenced by the spectral feature redundancy and noise [16,32]. The other is that the dependence on the training set samples leads to a low accuracy rate on its test set. In addition, the kNN model is affected by sample imbalance. Healthy control samples are easier to collect than tumor samples, which can cause sample imbalance. Therefore, we need to use a method of more powerful identification model for discriminant analysis of blood cancer.

3.2.2 Random subspace method for LDA and kNN promotion

The RSM model can improve the performance of the classifier and obtain a higher classification accuracy rate. RSM-LDA and RSM with kNN (RSM-KNN) are based on LDA and kNN classifiers, respectively. The specificity and sensitivity of the RSM-LDA model and the RSM-KNN model for AML, CML, MM, and lymphoma are shown in Figs. 3(a)–3(b), respectively. The RSM-LDA model had the best performance among the four kinds of classifiers. For AML, CML, MM, and lymphoma, the accuracy rates of the RSM-LDA model were listed in Table 2.

 

Fig. 3. The classification results for AML vs healthy control (HC), CML vs HC, MM vs HC, and Lymphoma vs HC using (a) RSM-LDA, (b) RSM-KNN

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3.3 Model evaluation

3.3.1 Receiver operating characteristic (ROC) curve evaluation

For a more comprehensive evaluation to the models, the ROC curve for AML, CML, MM, and lymphoma are shown in Figs. 4(a)–4(d), respectively. The RSM-LDA model had the biggest AUC, which means it had the strongest classification performance. The AUC can evaluate the performance of the model in the case of the imbalanced sample. Due to the random feature selection to generate multiple classifiers, the RSM-LDA model and the RSM-KNN model were more resistant to noise and requires less stability in the spectrum.

 

Fig. 4. The comparison of four kinds of model using ROC curve for (a) AML vs healthy control (HC), (b) CML vs HC, (c) MM vs HC, and (d) lymphoma vs HC.

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3.3.2 Detectable rate evaluation

In medical diagnosis, misdiagnosis have serious consequences. When the determination coefficient is close to the threshold, the misidentification happens easily. We proposed a more effective and suitable evaluation index for ensemble learning method. When more than 80% of the weak learners gave the correct results, we determined that the classification was valid for the sample. The ratio of the number of effectively classified samples based on the total samples was used as the evaluation index, which was called the detectable rate. The detectable rate can be calculated by the following formula:

$$Detectable\;rate = \sum\limits_i^S {(\frac{{{R_i}}}{N} > threshold)} /S$$
where S is the number of samples, N is the number of weak learners, Ri is the number of weak learners with correct classification. The threshold was set as 80% in this work. For the RSM-LDA model, the optimal number of weak learners was 100, and the optimal feature number of each learner was 12. The results of the votes for AML vs healthy control, CML vs healthy control, MM vs healthy control, and lymphoma vs healthy control using RSM-LDA for randomly sampling are shown in Fig. 5. When the randomly sampled votes sit the farther away from the center, the probability of model misclassification was smaller. From the figure, there are two points that indicate that the model has strong generalization performance. The first is that most of the points with correct predictions (the blue points are on the upper left and the red points are on the lower right) are not in the “Untrusted region”. The second is that all points with incorrect predictions (the blue points are on the lower right and the red points are on the upper left) fall in the “Untrusted region”. In addition, according to the prediction label of the RSM-LDA model and Eq. (1). The average detectable rates of AML, CML, MM, and lymphoma were 93.33%, 89.17%, 92.94%, and 89.86%, respectively. For the RSM-LDA model, the detectable rate is about 90% for four kinds of blood cancer, which also means its generalization performance is strong.

 

Fig. 5. The results of the votes using RSM-LDA for a randomly sampling. (a) AML vs healthy control (HC), (b) CML vs HC, (c) MM vs HC, (d) lymphoma vs HC.

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3.3.3 Variable importance (VI) analysis for selected element

To analyze the differences and commonality of the selected spectral lines, the VI of selected spectral lines was calculated to evaluate the element importance. The VI of whole selected lines in the RSM-LDA models which is used to classify the AML vs healthy control, CML vs healthy control, MM vs healthy control, lymphoma vs healthy control is shown in Figs. 6(a)–6(c), 6(d)–6(f), 6(g)–6(i), and 6(j)–6(l), respectively. For those four types of blood cancer, the metal elements are more important than non-metal elements and molecular bonds. In blood cancer detection, the VI of selected lines whose VI value higher than average VI value for four kinds of blood cancer are ranked in Table 3. The results of VI analysis indicate that in the RSM-LDA model, spectral lines have different VI value in those four types of classifications. This also further shows that those four types of blood cancer samples can be classified.

 

Fig. 6. The VI of selected lines for (a-c) AML vs healthy control classification, (d-f) CML vs healthy control classification, (g-i) MM vs healthy control classification, and (j-l) lymphoma vs healthy control classification.

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Tables Icon

Table 3. The ranking of VI for those four types of classification.

In summary, the RSM-LDA model has the highest average accuracy rate and AUC, which means it has the best classification performance. For the VI analysis, the spectral lines have different VI value in the RSM-LDA model which means the RSM-LDA model can be further used in the identification of blood cancer types. Blood type identification is a multi-classification problem. The accuracy rate and the detectable rate also can be used for multi-classification model evaluation.

3.4 Cancer serum type identification

The types of blood cancer are further classified after discrimination between cancer sera and health controls. LDA, kNN, RSM-LDA, and RSM-KNN were used to classify the type of blood cancer. the accuracy rates of these four kinds of blood cancer are shown in Fig. 7(a). The average accuracy rates of LDA, kNN, RSM-LDA, and RSM-KNN for blood cancer identification were 80.4%, 75.0%, 91.0%, and 70.8%. The RSM-LDA model has the best classification performance. The confusion matrix of RSM-LDA for AML, CML, MM, and lymphoma is shown in Fig. 7(b). As can be seen from the figure, MM sample has the lowest recognition rate. 5% and 8% of the MM samples were incorrectly identified as AML and CML, respectively. The results demonstrate that LIBS combined only with RSM-LDA can identify the four types of the blood cancer with accuracy rates over 90%. For the identification of those four kinds of blood cancer spectra, the accuracy rate of kNN model is lower than 80%.

 

Fig. 7. (a) The accuracy rates of four kinds of methods. (b) The confusion matrix of RSM-LDA.

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To analyze the similarity of the blood cancer spectra and calculate the average detectable rates, the votes of each weak classifier were counted. The vote results of AML, CML MM, and lymphoma are shown in Figs. 8(a)–8(d). For the result of AML as shown in Fig. 8(a), The X axis represents the AML sample index, and Y represents the number of votes for AML, CML, MM, and Lymphoma. The AML has biggest area in Fig. 8(a), which means good classification performance for AML samples. the area of MM was larger than CML and lymphoma, which means the AML sample and the MM sample have a high similarity at the spectral level. Further, the AML sample is easily recognized as MM incorrectly. Based on the results of votes and Eq. (1), when the detection threshold was set as 80%, the average detectable rates of AML, CML, MM, and lymphoma were 93.33%, 86.67%, 90.00%, and 93.75%, respectively. The result indicated that the RSM-LDA model has great potential to identify blood cancer types.

 

Fig. 8. The vote of the RSM-LDA model for (a) AML samples, (b) CML samples, (c) MM samples, and (d) lymphoma samples.

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4. Conclusions

The aim of this work was to diagnose the blood cancer serum and identify the cancer types using the RSM-LDA model combined with LIBS. The complete blood cancer sample set include leukemia (AML and CML), MM, and lymphoma. Compare with LDA and kNN, the RSM-LDA model has the highest average accuracy rate and AUC, which means the RSM-LDA model has the best classification performance. With the RSM-LDA model, the average accuracy rates for AML vs healthy control (HC), CML vs HC, MM vs HC, and lymphoma vs HC were from 94.33%, 94.49%, 94.61%, and 94.38% to 98.77%, 96.54%, 98.78%, and 96.62%, respectively. For cancerous samples classification using the RSM-LDA model, the detectable rate was proposed to evaluate the classification performance, and the average detectable rates of AML vs HC, CML vs HC, MM, vs HC and lymphoma vs HC were 93.33%, 89.17%, 92.94%, and 89.86%, respectively. Furthermore, the variable importance of selected lines was calculated by the RSM-LDA model. The average accuracy rate was improved to 91.00%, and 8% of MM spectra and 6% of lymphoma spectra were misidentified to CML. For blood cancer types identification, the detectable rates of AML, CML, MM, and lymphoma were 93.33%, 86.67%, 90.00%, and 93.75%, respectively, which means the RSM-LDA model can improve the diagnostic performance. The results showed that the proposed method can be a practical tool for rapid preliminary screening of blood cancer. Therefore, the RSM-LDA model is an effective pattern recognition method for LIBS analysis in blood cancer discrimination.

Funding

Huazhong University of Science and Technology (2020kfyXGYJ105).

Disclosures

The authors declare that there are no conflicts of interest related to this article.

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References

  • View by:
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  • |

  1. A. Sharma, G. Sumana, S. Sapra, and B. D. Malhotra, “Quantum Dots Self Assembly Based Interface for Blood Cancer Detection,” Langmuir 29(27), 8753–8762 (2013).
    [Crossref]
  2. R. L. Siegel, K. D. Miller, and A. Jemal, “Cancer statistics, 2015,” Ca-Cancer J. Clin. 65(1), 5–29 (2015).
    [Crossref]
  3. R. Nagulapalli, K. Hayatleh, S. Barker, S. Raparthy, and F. J. Lidgey, “A CMOS blood cancer detection sensor based on frequency deviation detection,” Analog. Integr. Circ. Sig. Process. 92(3), 437–442 (2017).
    [Crossref]
  4. M. Saritha, B. B. Prakash, K. Sukesh, and B. Shrinivas, “Detection of blood cancer in microscopic images of human blood samples: A review,” in 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), 596–600 (2016).
  5. C. Dong, M. Ji, and C. Ji, “Micro-RNAs and their potential target genes in leukemia pathogenesis,” Cancer Biol. Ther. 8(3), 200–205 (2009).
    [Crossref]
  6. X. Chen, X. Li, S. Yang, X. Yu, and A. Liu, “Discrimination of lymphoma using laser-induced breakdown spectroscopy conducted on whole blood samples,” Biomed. Opt. Express 9(3), 1057–1068 (2018).
    [Crossref]
  7. R. Mansurabadi, S. Abroun, A. Hajifathali, A. Asri, A. Atashi, and M. Haghighi, “Expression ofhsa-MIR-204, RUNX2, PPARγ,andBCL2in Bone Marrow Derived Mesenchymal Stem Cells from Multiple Myeloma Patients and Normal Individuals,” Cell Journal 19, 27–36 (2017).
    [Crossref]
  8. K. Kurihara, W. Hill, R. Burkhardt, G. Kettner, and N. Hashimoto, “Bone marrow changes following chemotherapy for multiple myeloma — A clinicopathological study of biopsy cases,” Nihon Ketsueki Gakkai Zasshi 50, 876–889 (1987).
  9. D. J. Grossklaus, C. S. Coffey, S. B. Shappell, G. S. Jack, and M. S. Cookson, “Percent Of Cancer in the Biopsy Set Predicts Pathological Findings After Prostatectomy,” J. Urol. 167(5), 2032–2036 (2002).
    [Crossref]
  10. M. D. Truesdale, P. J. Cheetham, A. T. Turk, S. Sartori, and K. K. Badani, “Gleason score concordance on biopsy-confirmed prostate cancer: Is pathological re-evaluation necessary prior to radical prostatectomy?” BJU Int. 107(5), 749–754 (2011).
    [Crossref]
  11. R. Ferraro, A. Agarwal, E. L. Martin-Macintosh, P. J. Peller, and R. M. Subramaniam, “MR Imaging and PET/CT in Diagnosis and Management of Multiple Myeloma,” Radiographics 35(2), 438–454 (2015).
    [Crossref]
  12. N. Houssami and D. F. Hayes, “Review of Preoperative Magnetic Resonance Imaging (MRI) in Breast Cancer: Should MRI Be Performed on All Women with Newly Diagnosed, Early Stage Breast Cancer?” Ca-Cancer J. Clin. 59(5), 290–302 (2009).
    [Crossref]
  13. D. N. Friedman, E. Lis, C. A. Sklar, K. C. Oeffinger, and I. J. Dunkel, “Whole-body magnetic resonance imaging (WB-MRI) as surveillance for subsequent malignancies in survivors of hereditary retinoblastoma: A pilot study,” Pediatr. Blood Cancer 61(8), 1440–1444 (2014).
    [Crossref]
  14. I. Cheung and N. K. V. Cheung, “Molecular detection of GAGE expression in peripheral blood and bone marrow: Utility as a tumor marker for neuroblastoma,” Clin. Cancer Res. 3, 821–826 (1997).
  15. S. Hideshima, R. Sato, S. Inoue, S. Kuroiwa, and T. Osaka, “Detection of tumor marker in blood serum using antibody-modified field effect transistor with optimized BSA blocking,” Sens. Actuators, B 161(1), 146–150 (2012).
    [Crossref]
  16. Y. W. Chu, S. S. Tang, S. X. Ma, Y. Y. Ma, Z. Q. Hao, Y. M. Guo, L. B. Guo, Y. F. Lu, and X. Y. Zeng, “Accuracy and stability improvement for meat species identification using multiplicative scatter correction and laser-induced breakdown spectroscopy,” Opt. Express 26(8), 10119–10127 (2018).
    [Crossref]
  17. Y. Ma, Z. Hu, Y. Tang, S. Ma, and Y. Lu, “Laser opto-ultrasonic dual detection for simultaneous compositional, structural, and stress analyses for wire + arc additive manufacturing,” Addit. Manuf. 31, 100956 (2020).
    [Crossref]
  18. Yuan Rui, Tang Yun, Zhu Zhihao, Hao Zhongqi, Jiaming, and Li, “Accuracy improvement of quantitative analysis for major elements in laser-induced breakdown spectroscopy using single-sample calibration,” Anal. Chim. Acta 1064, 11–16 (2019).
    [Crossref]
  19. S. J. Rehse, N. Jeyasingham, J. Diedrich, and S. Palchaudhuri, “A membrane basis for bacterial identification and discrimination using laser-induced breakdown spectroscopy,” J. Appl. Phys. 105(10), 102034 (2009).
    [Crossref]
  20. M. U. Farid, S. Jeong, D. H. Seo, C. Lau, R. Ahmed, N. K. Gali, Z. Ning, and A. K. An, “Mechanistic insight on the in vitro toxicity of graphene oxide against biofilm forming bacteria using laser-induced breakdown spectroscopy,” Nanoscale 10(9), 4475–4487 (2018).
    [Crossref]
  21. M. D. Dyar, J. M. Tucker, S. Humphries, S. M. Clegg, R. C. Wiens, and M. D. Lane, “Strategies for Mars remote Laser-Induced Breakdown Spectroscopy analysis of sulfur in geological samples,” Spectrochim. Acta, Part B 66(1), 39–56 (2011).
    [Crossref]
  22. H. Balzer, S. Hölters, V. Sturm, and R. Noll, “Systematic line selection for online coating thickness measurements of galvanised sheet steel using LIBS,” Anal. Bioanal. Chem. 385(2), 234–239 (2006).
    [Crossref]
  23. A. Tortschanoff, M. Baumgart, and G. Kroupa, “Compact DPSS-laser source for LIBS analysis of steel,” in Society of Photo-optical Instrumentation Engineers (2017).
  24. B. Busser, S. Moncayo, J. L. Coll, L. Sancey, and V. Motto-Ros, “Elemental imaging using laser-induced breakdown spectroscopy: A new and promising approach for biological and medical applications,” Coord. Chem. Rev. 358, 70–79 (2018).
    [Crossref]
  25. A. Elhussein, A. K. Kassem, H. Ismail, and M. A. Harith, “Exploiting LIBS as a spectrochemical analytical technique in diagnosis of some types of human malignancies,” Talanta 82(2), 495–501 (2010).
    [Crossref]
  26. R. Gaudiuso, E. Ewusiannan, N. Melikechi, X. Sun, B. Liu, L. F. Campesato, and T. Merghoub, “Using LIBS to diagnose melanoma in biomedical fluids deposited on solid substrates: Limits of direct spectral analysis and capability of machine learning,” Spectrochim. Acta, Part B 146, 106–114 (2018).
    [Crossref]
  27. J. H. Han, Y. Moon, J. J. Lee, S. Choi, Y. C. Kim, and S. Jeong, “Differentiation of cutaneous melanoma from surrounding skin using laser-induced breakdown spectroscopy,” Biomed. Opt. Express 7(1), 57–66 (2016).
    [Crossref]
  28. X. Chen, X. Li, X. Yu, D. Chen, and A. Liu, “Diagnosis of human malignancies using laser-induced breakdown spectroscopy in combination with chemometric methods,” Spectrochim. Acta, Part B 139, 63–69 (2018).
    [Crossref]
  29. C. Tameze, R. Vincelette, N. Melikechi, V. Zeljkovic, and E. Izquierdo, “Empirical Analysis of Libs Images for Ovarian Cancer Detection,” in workshop on image analysis for multimedia interactive services76 (2007).
  30. X. Zou, H. Zhao, J. Shi, S. Wang, X. Zhai, and X. Hu, “Texture analysis and grade discriminant of sausages based on ultrasound imaging,” Transactions of the Chinese Society of Agricultural Engineering 33, 284–290 (2017).
    [Crossref]
  31. M. Skurichina and R. P. W. Duin, “Bagging, Boosting and the Random Subspace Method for Linear Classifiers,” Pattern Analysis & Applications 5(2), 121–135 (2002).
    [Crossref]
  32. C. Li, S. Zhang, H. Zhang, L. Pang, K. K. Lam, C. Hui, and S. Zhang, “Using the K-Nearest Neighbor Algorithm for the Classification of Lymph Node Metastasis in Gastric Cancer,” Computational and Mathematical Methods in Medicine 2012, 876545 (2012).
    [Crossref]

2020 (1)

Y. Ma, Z. Hu, Y. Tang, S. Ma, and Y. Lu, “Laser opto-ultrasonic dual detection for simultaneous compositional, structural, and stress analyses for wire + arc additive manufacturing,” Addit. Manuf. 31, 100956 (2020).
[Crossref]

2019 (1)

Yuan Rui, Tang Yun, Zhu Zhihao, Hao Zhongqi, Jiaming, and Li, “Accuracy improvement of quantitative analysis for major elements in laser-induced breakdown spectroscopy using single-sample calibration,” Anal. Chim. Acta 1064, 11–16 (2019).
[Crossref]

2018 (6)

M. U. Farid, S. Jeong, D. H. Seo, C. Lau, R. Ahmed, N. K. Gali, Z. Ning, and A. K. An, “Mechanistic insight on the in vitro toxicity of graphene oxide against biofilm forming bacteria using laser-induced breakdown spectroscopy,” Nanoscale 10(9), 4475–4487 (2018).
[Crossref]

Y. W. Chu, S. S. Tang, S. X. Ma, Y. Y. Ma, Z. Q. Hao, Y. M. Guo, L. B. Guo, Y. F. Lu, and X. Y. Zeng, “Accuracy and stability improvement for meat species identification using multiplicative scatter correction and laser-induced breakdown spectroscopy,” Opt. Express 26(8), 10119–10127 (2018).
[Crossref]

X. Chen, X. Li, X. Yu, D. Chen, and A. Liu, “Diagnosis of human malignancies using laser-induced breakdown spectroscopy in combination with chemometric methods,” Spectrochim. Acta, Part B 139, 63–69 (2018).
[Crossref]

X. Chen, X. Li, S. Yang, X. Yu, and A. Liu, “Discrimination of lymphoma using laser-induced breakdown spectroscopy conducted on whole blood samples,” Biomed. Opt. Express 9(3), 1057–1068 (2018).
[Crossref]

B. Busser, S. Moncayo, J. L. Coll, L. Sancey, and V. Motto-Ros, “Elemental imaging using laser-induced breakdown spectroscopy: A new and promising approach for biological and medical applications,” Coord. Chem. Rev. 358, 70–79 (2018).
[Crossref]

R. Gaudiuso, E. Ewusiannan, N. Melikechi, X. Sun, B. Liu, L. F. Campesato, and T. Merghoub, “Using LIBS to diagnose melanoma in biomedical fluids deposited on solid substrates: Limits of direct spectral analysis and capability of machine learning,” Spectrochim. Acta, Part B 146, 106–114 (2018).
[Crossref]

2017 (3)

X. Zou, H. Zhao, J. Shi, S. Wang, X. Zhai, and X. Hu, “Texture analysis and grade discriminant of sausages based on ultrasound imaging,” Transactions of the Chinese Society of Agricultural Engineering 33, 284–290 (2017).
[Crossref]

R. Mansurabadi, S. Abroun, A. Hajifathali, A. Asri, A. Atashi, and M. Haghighi, “Expression ofhsa-MIR-204, RUNX2, PPARγ,andBCL2in Bone Marrow Derived Mesenchymal Stem Cells from Multiple Myeloma Patients and Normal Individuals,” Cell Journal 19, 27–36 (2017).
[Crossref]

R. Nagulapalli, K. Hayatleh, S. Barker, S. Raparthy, and F. J. Lidgey, “A CMOS blood cancer detection sensor based on frequency deviation detection,” Analog. Integr. Circ. Sig. Process. 92(3), 437–442 (2017).
[Crossref]

2016 (1)

2015 (2)

R. Ferraro, A. Agarwal, E. L. Martin-Macintosh, P. J. Peller, and R. M. Subramaniam, “MR Imaging and PET/CT in Diagnosis and Management of Multiple Myeloma,” Radiographics 35(2), 438–454 (2015).
[Crossref]

R. L. Siegel, K. D. Miller, and A. Jemal, “Cancer statistics, 2015,” Ca-Cancer J. Clin. 65(1), 5–29 (2015).
[Crossref]

2014 (1)

D. N. Friedman, E. Lis, C. A. Sklar, K. C. Oeffinger, and I. J. Dunkel, “Whole-body magnetic resonance imaging (WB-MRI) as surveillance for subsequent malignancies in survivors of hereditary retinoblastoma: A pilot study,” Pediatr. Blood Cancer 61(8), 1440–1444 (2014).
[Crossref]

2013 (1)

A. Sharma, G. Sumana, S. Sapra, and B. D. Malhotra, “Quantum Dots Self Assembly Based Interface for Blood Cancer Detection,” Langmuir 29(27), 8753–8762 (2013).
[Crossref]

2012 (2)

S. Hideshima, R. Sato, S. Inoue, S. Kuroiwa, and T. Osaka, “Detection of tumor marker in blood serum using antibody-modified field effect transistor with optimized BSA blocking,” Sens. Actuators, B 161(1), 146–150 (2012).
[Crossref]

C. Li, S. Zhang, H. Zhang, L. Pang, K. K. Lam, C. Hui, and S. Zhang, “Using the K-Nearest Neighbor Algorithm for the Classification of Lymph Node Metastasis in Gastric Cancer,” Computational and Mathematical Methods in Medicine 2012, 876545 (2012).
[Crossref]

2011 (2)

M. D. Truesdale, P. J. Cheetham, A. T. Turk, S. Sartori, and K. K. Badani, “Gleason score concordance on biopsy-confirmed prostate cancer: Is pathological re-evaluation necessary prior to radical prostatectomy?” BJU Int. 107(5), 749–754 (2011).
[Crossref]

M. D. Dyar, J. M. Tucker, S. Humphries, S. M. Clegg, R. C. Wiens, and M. D. Lane, “Strategies for Mars remote Laser-Induced Breakdown Spectroscopy analysis of sulfur in geological samples,” Spectrochim. Acta, Part B 66(1), 39–56 (2011).
[Crossref]

2010 (1)

A. Elhussein, A. K. Kassem, H. Ismail, and M. A. Harith, “Exploiting LIBS as a spectrochemical analytical technique in diagnosis of some types of human malignancies,” Talanta 82(2), 495–501 (2010).
[Crossref]

2009 (3)

S. J. Rehse, N. Jeyasingham, J. Diedrich, and S. Palchaudhuri, “A membrane basis for bacterial identification and discrimination using laser-induced breakdown spectroscopy,” J. Appl. Phys. 105(10), 102034 (2009).
[Crossref]

N. Houssami and D. F. Hayes, “Review of Preoperative Magnetic Resonance Imaging (MRI) in Breast Cancer: Should MRI Be Performed on All Women with Newly Diagnosed, Early Stage Breast Cancer?” Ca-Cancer J. Clin. 59(5), 290–302 (2009).
[Crossref]

C. Dong, M. Ji, and C. Ji, “Micro-RNAs and their potential target genes in leukemia pathogenesis,” Cancer Biol. Ther. 8(3), 200–205 (2009).
[Crossref]

2006 (1)

H. Balzer, S. Hölters, V. Sturm, and R. Noll, “Systematic line selection for online coating thickness measurements of galvanised sheet steel using LIBS,” Anal. Bioanal. Chem. 385(2), 234–239 (2006).
[Crossref]

2002 (2)

D. J. Grossklaus, C. S. Coffey, S. B. Shappell, G. S. Jack, and M. S. Cookson, “Percent Of Cancer in the Biopsy Set Predicts Pathological Findings After Prostatectomy,” J. Urol. 167(5), 2032–2036 (2002).
[Crossref]

M. Skurichina and R. P. W. Duin, “Bagging, Boosting and the Random Subspace Method for Linear Classifiers,” Pattern Analysis & Applications 5(2), 121–135 (2002).
[Crossref]

1997 (1)

I. Cheung and N. K. V. Cheung, “Molecular detection of GAGE expression in peripheral blood and bone marrow: Utility as a tumor marker for neuroblastoma,” Clin. Cancer Res. 3, 821–826 (1997).

1987 (1)

K. Kurihara, W. Hill, R. Burkhardt, G. Kettner, and N. Hashimoto, “Bone marrow changes following chemotherapy for multiple myeloma — A clinicopathological study of biopsy cases,” Nihon Ketsueki Gakkai Zasshi 50, 876–889 (1987).

Abroun, S.

R. Mansurabadi, S. Abroun, A. Hajifathali, A. Asri, A. Atashi, and M. Haghighi, “Expression ofhsa-MIR-204, RUNX2, PPARγ,andBCL2in Bone Marrow Derived Mesenchymal Stem Cells from Multiple Myeloma Patients and Normal Individuals,” Cell Journal 19, 27–36 (2017).
[Crossref]

Agarwal, A.

R. Ferraro, A. Agarwal, E. L. Martin-Macintosh, P. J. Peller, and R. M. Subramaniam, “MR Imaging and PET/CT in Diagnosis and Management of Multiple Myeloma,” Radiographics 35(2), 438–454 (2015).
[Crossref]

Ahmed, R.

M. U. Farid, S. Jeong, D. H. Seo, C. Lau, R. Ahmed, N. K. Gali, Z. Ning, and A. K. An, “Mechanistic insight on the in vitro toxicity of graphene oxide against biofilm forming bacteria using laser-induced breakdown spectroscopy,” Nanoscale 10(9), 4475–4487 (2018).
[Crossref]

An, A. K.

M. U. Farid, S. Jeong, D. H. Seo, C. Lau, R. Ahmed, N. K. Gali, Z. Ning, and A. K. An, “Mechanistic insight on the in vitro toxicity of graphene oxide against biofilm forming bacteria using laser-induced breakdown spectroscopy,” Nanoscale 10(9), 4475–4487 (2018).
[Crossref]

Asri, A.

R. Mansurabadi, S. Abroun, A. Hajifathali, A. Asri, A. Atashi, and M. Haghighi, “Expression ofhsa-MIR-204, RUNX2, PPARγ,andBCL2in Bone Marrow Derived Mesenchymal Stem Cells from Multiple Myeloma Patients and Normal Individuals,” Cell Journal 19, 27–36 (2017).
[Crossref]

Atashi, A.

R. Mansurabadi, S. Abroun, A. Hajifathali, A. Asri, A. Atashi, and M. Haghighi, “Expression ofhsa-MIR-204, RUNX2, PPARγ,andBCL2in Bone Marrow Derived Mesenchymal Stem Cells from Multiple Myeloma Patients and Normal Individuals,” Cell Journal 19, 27–36 (2017).
[Crossref]

Badani, K. K.

M. D. Truesdale, P. J. Cheetham, A. T. Turk, S. Sartori, and K. K. Badani, “Gleason score concordance on biopsy-confirmed prostate cancer: Is pathological re-evaluation necessary prior to radical prostatectomy?” BJU Int. 107(5), 749–754 (2011).
[Crossref]

Balzer, H.

H. Balzer, S. Hölters, V. Sturm, and R. Noll, “Systematic line selection for online coating thickness measurements of galvanised sheet steel using LIBS,” Anal. Bioanal. Chem. 385(2), 234–239 (2006).
[Crossref]

Barker, S.

R. Nagulapalli, K. Hayatleh, S. Barker, S. Raparthy, and F. J. Lidgey, “A CMOS blood cancer detection sensor based on frequency deviation detection,” Analog. Integr. Circ. Sig. Process. 92(3), 437–442 (2017).
[Crossref]

Baumgart, M.

A. Tortschanoff, M. Baumgart, and G. Kroupa, “Compact DPSS-laser source for LIBS analysis of steel,” in Society of Photo-optical Instrumentation Engineers (2017).

Burkhardt, R.

K. Kurihara, W. Hill, R. Burkhardt, G. Kettner, and N. Hashimoto, “Bone marrow changes following chemotherapy for multiple myeloma — A clinicopathological study of biopsy cases,” Nihon Ketsueki Gakkai Zasshi 50, 876–889 (1987).

Busser, B.

B. Busser, S. Moncayo, J. L. Coll, L. Sancey, and V. Motto-Ros, “Elemental imaging using laser-induced breakdown spectroscopy: A new and promising approach for biological and medical applications,” Coord. Chem. Rev. 358, 70–79 (2018).
[Crossref]

Campesato, L. F.

R. Gaudiuso, E. Ewusiannan, N. Melikechi, X. Sun, B. Liu, L. F. Campesato, and T. Merghoub, “Using LIBS to diagnose melanoma in biomedical fluids deposited on solid substrates: Limits of direct spectral analysis and capability of machine learning,” Spectrochim. Acta, Part B 146, 106–114 (2018).
[Crossref]

Cheetham, P. J.

M. D. Truesdale, P. J. Cheetham, A. T. Turk, S. Sartori, and K. K. Badani, “Gleason score concordance on biopsy-confirmed prostate cancer: Is pathological re-evaluation necessary prior to radical prostatectomy?” BJU Int. 107(5), 749–754 (2011).
[Crossref]

Chen, D.

X. Chen, X. Li, X. Yu, D. Chen, and A. Liu, “Diagnosis of human malignancies using laser-induced breakdown spectroscopy in combination with chemometric methods,” Spectrochim. Acta, Part B 139, 63–69 (2018).
[Crossref]

Chen, X.

X. Chen, X. Li, X. Yu, D. Chen, and A. Liu, “Diagnosis of human malignancies using laser-induced breakdown spectroscopy in combination with chemometric methods,” Spectrochim. Acta, Part B 139, 63–69 (2018).
[Crossref]

X. Chen, X. Li, S. Yang, X. Yu, and A. Liu, “Discrimination of lymphoma using laser-induced breakdown spectroscopy conducted on whole blood samples,” Biomed. Opt. Express 9(3), 1057–1068 (2018).
[Crossref]

Cheung, I.

I. Cheung and N. K. V. Cheung, “Molecular detection of GAGE expression in peripheral blood and bone marrow: Utility as a tumor marker for neuroblastoma,” Clin. Cancer Res. 3, 821–826 (1997).

Cheung, N. K. V.

I. Cheung and N. K. V. Cheung, “Molecular detection of GAGE expression in peripheral blood and bone marrow: Utility as a tumor marker for neuroblastoma,” Clin. Cancer Res. 3, 821–826 (1997).

Choi, S.

Chu, Y. W.

Clegg, S. M.

M. D. Dyar, J. M. Tucker, S. Humphries, S. M. Clegg, R. C. Wiens, and M. D. Lane, “Strategies for Mars remote Laser-Induced Breakdown Spectroscopy analysis of sulfur in geological samples,” Spectrochim. Acta, Part B 66(1), 39–56 (2011).
[Crossref]

Coffey, C. S.

D. J. Grossklaus, C. S. Coffey, S. B. Shappell, G. S. Jack, and M. S. Cookson, “Percent Of Cancer in the Biopsy Set Predicts Pathological Findings After Prostatectomy,” J. Urol. 167(5), 2032–2036 (2002).
[Crossref]

Coll, J. L.

B. Busser, S. Moncayo, J. L. Coll, L. Sancey, and V. Motto-Ros, “Elemental imaging using laser-induced breakdown spectroscopy: A new and promising approach for biological and medical applications,” Coord. Chem. Rev. 358, 70–79 (2018).
[Crossref]

Cookson, M. S.

D. J. Grossklaus, C. S. Coffey, S. B. Shappell, G. S. Jack, and M. S. Cookson, “Percent Of Cancer in the Biopsy Set Predicts Pathological Findings After Prostatectomy,” J. Urol. 167(5), 2032–2036 (2002).
[Crossref]

Diedrich, J.

S. J. Rehse, N. Jeyasingham, J. Diedrich, and S. Palchaudhuri, “A membrane basis for bacterial identification and discrimination using laser-induced breakdown spectroscopy,” J. Appl. Phys. 105(10), 102034 (2009).
[Crossref]

Dong, C.

C. Dong, M. Ji, and C. Ji, “Micro-RNAs and their potential target genes in leukemia pathogenesis,” Cancer Biol. Ther. 8(3), 200–205 (2009).
[Crossref]

Duin, R. P. W.

M. Skurichina and R. P. W. Duin, “Bagging, Boosting and the Random Subspace Method for Linear Classifiers,” Pattern Analysis & Applications 5(2), 121–135 (2002).
[Crossref]

Dunkel, I. J.

D. N. Friedman, E. Lis, C. A. Sklar, K. C. Oeffinger, and I. J. Dunkel, “Whole-body magnetic resonance imaging (WB-MRI) as surveillance for subsequent malignancies in survivors of hereditary retinoblastoma: A pilot study,” Pediatr. Blood Cancer 61(8), 1440–1444 (2014).
[Crossref]

Dyar, M. D.

M. D. Dyar, J. M. Tucker, S. Humphries, S. M. Clegg, R. C. Wiens, and M. D. Lane, “Strategies for Mars remote Laser-Induced Breakdown Spectroscopy analysis of sulfur in geological samples,” Spectrochim. Acta, Part B 66(1), 39–56 (2011).
[Crossref]

Elhussein, A.

A. Elhussein, A. K. Kassem, H. Ismail, and M. A. Harith, “Exploiting LIBS as a spectrochemical analytical technique in diagnosis of some types of human malignancies,” Talanta 82(2), 495–501 (2010).
[Crossref]

Ewusiannan, E.

R. Gaudiuso, E. Ewusiannan, N. Melikechi, X. Sun, B. Liu, L. F. Campesato, and T. Merghoub, “Using LIBS to diagnose melanoma in biomedical fluids deposited on solid substrates: Limits of direct spectral analysis and capability of machine learning,” Spectrochim. Acta, Part B 146, 106–114 (2018).
[Crossref]

Farid, M. U.

M. U. Farid, S. Jeong, D. H. Seo, C. Lau, R. Ahmed, N. K. Gali, Z. Ning, and A. K. An, “Mechanistic insight on the in vitro toxicity of graphene oxide against biofilm forming bacteria using laser-induced breakdown spectroscopy,” Nanoscale 10(9), 4475–4487 (2018).
[Crossref]

Ferraro, R.

R. Ferraro, A. Agarwal, E. L. Martin-Macintosh, P. J. Peller, and R. M. Subramaniam, “MR Imaging and PET/CT in Diagnosis and Management of Multiple Myeloma,” Radiographics 35(2), 438–454 (2015).
[Crossref]

Friedman, D. N.

D. N. Friedman, E. Lis, C. A. Sklar, K. C. Oeffinger, and I. J. Dunkel, “Whole-body magnetic resonance imaging (WB-MRI) as surveillance for subsequent malignancies in survivors of hereditary retinoblastoma: A pilot study,” Pediatr. Blood Cancer 61(8), 1440–1444 (2014).
[Crossref]

Gali, N. K.

M. U. Farid, S. Jeong, D. H. Seo, C. Lau, R. Ahmed, N. K. Gali, Z. Ning, and A. K. An, “Mechanistic insight on the in vitro toxicity of graphene oxide against biofilm forming bacteria using laser-induced breakdown spectroscopy,” Nanoscale 10(9), 4475–4487 (2018).
[Crossref]

Gaudiuso, R.

R. Gaudiuso, E. Ewusiannan, N. Melikechi, X. Sun, B. Liu, L. F. Campesato, and T. Merghoub, “Using LIBS to diagnose melanoma in biomedical fluids deposited on solid substrates: Limits of direct spectral analysis and capability of machine learning,” Spectrochim. Acta, Part B 146, 106–114 (2018).
[Crossref]

Grossklaus, D. J.

D. J. Grossklaus, C. S. Coffey, S. B. Shappell, G. S. Jack, and M. S. Cookson, “Percent Of Cancer in the Biopsy Set Predicts Pathological Findings After Prostatectomy,” J. Urol. 167(5), 2032–2036 (2002).
[Crossref]

Guo, L. B.

Guo, Y. M.

Haghighi, M.

R. Mansurabadi, S. Abroun, A. Hajifathali, A. Asri, A. Atashi, and M. Haghighi, “Expression ofhsa-MIR-204, RUNX2, PPARγ,andBCL2in Bone Marrow Derived Mesenchymal Stem Cells from Multiple Myeloma Patients and Normal Individuals,” Cell Journal 19, 27–36 (2017).
[Crossref]

Hajifathali, A.

R. Mansurabadi, S. Abroun, A. Hajifathali, A. Asri, A. Atashi, and M. Haghighi, “Expression ofhsa-MIR-204, RUNX2, PPARγ,andBCL2in Bone Marrow Derived Mesenchymal Stem Cells from Multiple Myeloma Patients and Normal Individuals,” Cell Journal 19, 27–36 (2017).
[Crossref]

Han, J. H.

Hao, Z. Q.

Harith, M. A.

A. Elhussein, A. K. Kassem, H. Ismail, and M. A. Harith, “Exploiting LIBS as a spectrochemical analytical technique in diagnosis of some types of human malignancies,” Talanta 82(2), 495–501 (2010).
[Crossref]

Hashimoto, N.

K. Kurihara, W. Hill, R. Burkhardt, G. Kettner, and N. Hashimoto, “Bone marrow changes following chemotherapy for multiple myeloma — A clinicopathological study of biopsy cases,” Nihon Ketsueki Gakkai Zasshi 50, 876–889 (1987).

Hayatleh, K.

R. Nagulapalli, K. Hayatleh, S. Barker, S. Raparthy, and F. J. Lidgey, “A CMOS blood cancer detection sensor based on frequency deviation detection,” Analog. Integr. Circ. Sig. Process. 92(3), 437–442 (2017).
[Crossref]

Hayes, D. F.

N. Houssami and D. F. Hayes, “Review of Preoperative Magnetic Resonance Imaging (MRI) in Breast Cancer: Should MRI Be Performed on All Women with Newly Diagnosed, Early Stage Breast Cancer?” Ca-Cancer J. Clin. 59(5), 290–302 (2009).
[Crossref]

Hideshima, S.

S. Hideshima, R. Sato, S. Inoue, S. Kuroiwa, and T. Osaka, “Detection of tumor marker in blood serum using antibody-modified field effect transistor with optimized BSA blocking,” Sens. Actuators, B 161(1), 146–150 (2012).
[Crossref]

Hill, W.

K. Kurihara, W. Hill, R. Burkhardt, G. Kettner, and N. Hashimoto, “Bone marrow changes following chemotherapy for multiple myeloma — A clinicopathological study of biopsy cases,” Nihon Ketsueki Gakkai Zasshi 50, 876–889 (1987).

Hölters, S.

H. Balzer, S. Hölters, V. Sturm, and R. Noll, “Systematic line selection for online coating thickness measurements of galvanised sheet steel using LIBS,” Anal. Bioanal. Chem. 385(2), 234–239 (2006).
[Crossref]

Houssami, N.

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D. J. Grossklaus, C. S. Coffey, S. B. Shappell, G. S. Jack, and M. S. Cookson, “Percent Of Cancer in the Biopsy Set Predicts Pathological Findings After Prostatectomy,” J. Urol. 167(5), 2032–2036 (2002).
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Li,

Yuan Rui, Tang Yun, Zhu Zhihao, Hao Zhongqi, Jiaming, and Li, “Accuracy improvement of quantitative analysis for major elements in laser-induced breakdown spectroscopy using single-sample calibration,” Anal. Chim. Acta 1064, 11–16 (2019).
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X. Chen, X. Li, X. Yu, D. Chen, and A. Liu, “Diagnosis of human malignancies using laser-induced breakdown spectroscopy in combination with chemometric methods,” Spectrochim. Acta, Part B 139, 63–69 (2018).
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X. Chen, X. Li, S. Yang, X. Yu, and A. Liu, “Discrimination of lymphoma using laser-induced breakdown spectroscopy conducted on whole blood samples,” Biomed. Opt. Express 9(3), 1057–1068 (2018).
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X. Chen, X. Li, X. Yu, D. Chen, and A. Liu, “Diagnosis of human malignancies using laser-induced breakdown spectroscopy in combination with chemometric methods,” Spectrochim. Acta, Part B 139, 63–69 (2018).
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R. Gaudiuso, E. Ewusiannan, N. Melikechi, X. Sun, B. Liu, L. F. Campesato, and T. Merghoub, “Using LIBS to diagnose melanoma in biomedical fluids deposited on solid substrates: Limits of direct spectral analysis and capability of machine learning,” Spectrochim. Acta, Part B 146, 106–114 (2018).
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Lu, Y.

Y. Ma, Z. Hu, Y. Tang, S. Ma, and Y. Lu, “Laser opto-ultrasonic dual detection for simultaneous compositional, structural, and stress analyses for wire + arc additive manufacturing,” Addit. Manuf. 31, 100956 (2020).
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Lu, Y. F.

Ma, S.

Y. Ma, Z. Hu, Y. Tang, S. Ma, and Y. Lu, “Laser opto-ultrasonic dual detection for simultaneous compositional, structural, and stress analyses for wire + arc additive manufacturing,” Addit. Manuf. 31, 100956 (2020).
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Ma, S. X.

Ma, Y.

Y. Ma, Z. Hu, Y. Tang, S. Ma, and Y. Lu, “Laser opto-ultrasonic dual detection for simultaneous compositional, structural, and stress analyses for wire + arc additive manufacturing,” Addit. Manuf. 31, 100956 (2020).
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R. Gaudiuso, E. Ewusiannan, N. Melikechi, X. Sun, B. Liu, L. F. Campesato, and T. Merghoub, “Using LIBS to diagnose melanoma in biomedical fluids deposited on solid substrates: Limits of direct spectral analysis and capability of machine learning,” Spectrochim. Acta, Part B 146, 106–114 (2018).
[Crossref]

C. Tameze, R. Vincelette, N. Melikechi, V. Zeljkovic, and E. Izquierdo, “Empirical Analysis of Libs Images for Ovarian Cancer Detection,” in workshop on image analysis for multimedia interactive services76 (2007).

Merghoub, T.

R. Gaudiuso, E. Ewusiannan, N. Melikechi, X. Sun, B. Liu, L. F. Campesato, and T. Merghoub, “Using LIBS to diagnose melanoma in biomedical fluids deposited on solid substrates: Limits of direct spectral analysis and capability of machine learning,” Spectrochim. Acta, Part B 146, 106–114 (2018).
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R. L. Siegel, K. D. Miller, and A. Jemal, “Cancer statistics, 2015,” Ca-Cancer J. Clin. 65(1), 5–29 (2015).
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B. Busser, S. Moncayo, J. L. Coll, L. Sancey, and V. Motto-Ros, “Elemental imaging using laser-induced breakdown spectroscopy: A new and promising approach for biological and medical applications,” Coord. Chem. Rev. 358, 70–79 (2018).
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Motto-Ros, V.

B. Busser, S. Moncayo, J. L. Coll, L. Sancey, and V. Motto-Ros, “Elemental imaging using laser-induced breakdown spectroscopy: A new and promising approach for biological and medical applications,” Coord. Chem. Rev. 358, 70–79 (2018).
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R. Nagulapalli, K. Hayatleh, S. Barker, S. Raparthy, and F. J. Lidgey, “A CMOS blood cancer detection sensor based on frequency deviation detection,” Analog. Integr. Circ. Sig. Process. 92(3), 437–442 (2017).
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Ning, Z.

M. U. Farid, S. Jeong, D. H. Seo, C. Lau, R. Ahmed, N. K. Gali, Z. Ning, and A. K. An, “Mechanistic insight on the in vitro toxicity of graphene oxide against biofilm forming bacteria using laser-induced breakdown spectroscopy,” Nanoscale 10(9), 4475–4487 (2018).
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H. Balzer, S. Hölters, V. Sturm, and R. Noll, “Systematic line selection for online coating thickness measurements of galvanised sheet steel using LIBS,” Anal. Bioanal. Chem. 385(2), 234–239 (2006).
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D. N. Friedman, E. Lis, C. A. Sklar, K. C. Oeffinger, and I. J. Dunkel, “Whole-body magnetic resonance imaging (WB-MRI) as surveillance for subsequent malignancies in survivors of hereditary retinoblastoma: A pilot study,” Pediatr. Blood Cancer 61(8), 1440–1444 (2014).
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S. Hideshima, R. Sato, S. Inoue, S. Kuroiwa, and T. Osaka, “Detection of tumor marker in blood serum using antibody-modified field effect transistor with optimized BSA blocking,” Sens. Actuators, B 161(1), 146–150 (2012).
[Crossref]

Palchaudhuri, S.

S. J. Rehse, N. Jeyasingham, J. Diedrich, and S. Palchaudhuri, “A membrane basis for bacterial identification and discrimination using laser-induced breakdown spectroscopy,” J. Appl. Phys. 105(10), 102034 (2009).
[Crossref]

Pang, L.

C. Li, S. Zhang, H. Zhang, L. Pang, K. K. Lam, C. Hui, and S. Zhang, “Using the K-Nearest Neighbor Algorithm for the Classification of Lymph Node Metastasis in Gastric Cancer,” Computational and Mathematical Methods in Medicine 2012, 876545 (2012).
[Crossref]

Peller, P. J.

R. Ferraro, A. Agarwal, E. L. Martin-Macintosh, P. J. Peller, and R. M. Subramaniam, “MR Imaging and PET/CT in Diagnosis and Management of Multiple Myeloma,” Radiographics 35(2), 438–454 (2015).
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Raparthy, S.

R. Nagulapalli, K. Hayatleh, S. Barker, S. Raparthy, and F. J. Lidgey, “A CMOS blood cancer detection sensor based on frequency deviation detection,” Analog. Integr. Circ. Sig. Process. 92(3), 437–442 (2017).
[Crossref]

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S. J. Rehse, N. Jeyasingham, J. Diedrich, and S. Palchaudhuri, “A membrane basis for bacterial identification and discrimination using laser-induced breakdown spectroscopy,” J. Appl. Phys. 105(10), 102034 (2009).
[Crossref]

Rui, Yuan

Yuan Rui, Tang Yun, Zhu Zhihao, Hao Zhongqi, Jiaming, and Li, “Accuracy improvement of quantitative analysis for major elements in laser-induced breakdown spectroscopy using single-sample calibration,” Anal. Chim. Acta 1064, 11–16 (2019).
[Crossref]

Sancey, L.

B. Busser, S. Moncayo, J. L. Coll, L. Sancey, and V. Motto-Ros, “Elemental imaging using laser-induced breakdown spectroscopy: A new and promising approach for biological and medical applications,” Coord. Chem. Rev. 358, 70–79 (2018).
[Crossref]

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A. Sharma, G. Sumana, S. Sapra, and B. D. Malhotra, “Quantum Dots Self Assembly Based Interface for Blood Cancer Detection,” Langmuir 29(27), 8753–8762 (2013).
[Crossref]

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M. Saritha, B. B. Prakash, K. Sukesh, and B. Shrinivas, “Detection of blood cancer in microscopic images of human blood samples: A review,” in 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), 596–600 (2016).

Sartori, S.

M. D. Truesdale, P. J. Cheetham, A. T. Turk, S. Sartori, and K. K. Badani, “Gleason score concordance on biopsy-confirmed prostate cancer: Is pathological re-evaluation necessary prior to radical prostatectomy?” BJU Int. 107(5), 749–754 (2011).
[Crossref]

Sato, R.

S. Hideshima, R. Sato, S. Inoue, S. Kuroiwa, and T. Osaka, “Detection of tumor marker in blood serum using antibody-modified field effect transistor with optimized BSA blocking,” Sens. Actuators, B 161(1), 146–150 (2012).
[Crossref]

Seo, D. H.

M. U. Farid, S. Jeong, D. H. Seo, C. Lau, R. Ahmed, N. K. Gali, Z. Ning, and A. K. An, “Mechanistic insight on the in vitro toxicity of graphene oxide against biofilm forming bacteria using laser-induced breakdown spectroscopy,” Nanoscale 10(9), 4475–4487 (2018).
[Crossref]

Shappell, S. B.

D. J. Grossklaus, C. S. Coffey, S. B. Shappell, G. S. Jack, and M. S. Cookson, “Percent Of Cancer in the Biopsy Set Predicts Pathological Findings After Prostatectomy,” J. Urol. 167(5), 2032–2036 (2002).
[Crossref]

Sharma, A.

A. Sharma, G. Sumana, S. Sapra, and B. D. Malhotra, “Quantum Dots Self Assembly Based Interface for Blood Cancer Detection,” Langmuir 29(27), 8753–8762 (2013).
[Crossref]

Shi, J.

X. Zou, H. Zhao, J. Shi, S. Wang, X. Zhai, and X. Hu, “Texture analysis and grade discriminant of sausages based on ultrasound imaging,” Transactions of the Chinese Society of Agricultural Engineering 33, 284–290 (2017).
[Crossref]

Shrinivas, B.

M. Saritha, B. B. Prakash, K. Sukesh, and B. Shrinivas, “Detection of blood cancer in microscopic images of human blood samples: A review,” in 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), 596–600 (2016).

Siegel, R. L.

R. L. Siegel, K. D. Miller, and A. Jemal, “Cancer statistics, 2015,” Ca-Cancer J. Clin. 65(1), 5–29 (2015).
[Crossref]

Sklar, C. A.

D. N. Friedman, E. Lis, C. A. Sklar, K. C. Oeffinger, and I. J. Dunkel, “Whole-body magnetic resonance imaging (WB-MRI) as surveillance for subsequent malignancies in survivors of hereditary retinoblastoma: A pilot study,” Pediatr. Blood Cancer 61(8), 1440–1444 (2014).
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M. Skurichina and R. P. W. Duin, “Bagging, Boosting and the Random Subspace Method for Linear Classifiers,” Pattern Analysis & Applications 5(2), 121–135 (2002).
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H. Balzer, S. Hölters, V. Sturm, and R. Noll, “Systematic line selection for online coating thickness measurements of galvanised sheet steel using LIBS,” Anal. Bioanal. Chem. 385(2), 234–239 (2006).
[Crossref]

Subramaniam, R. M.

R. Ferraro, A. Agarwal, E. L. Martin-Macintosh, P. J. Peller, and R. M. Subramaniam, “MR Imaging and PET/CT in Diagnosis and Management of Multiple Myeloma,” Radiographics 35(2), 438–454 (2015).
[Crossref]

Sukesh, K.

M. Saritha, B. B. Prakash, K. Sukesh, and B. Shrinivas, “Detection of blood cancer in microscopic images of human blood samples: A review,” in 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), 596–600 (2016).

Sumana, G.

A. Sharma, G. Sumana, S. Sapra, and B. D. Malhotra, “Quantum Dots Self Assembly Based Interface for Blood Cancer Detection,” Langmuir 29(27), 8753–8762 (2013).
[Crossref]

Sun, X.

R. Gaudiuso, E. Ewusiannan, N. Melikechi, X. Sun, B. Liu, L. F. Campesato, and T. Merghoub, “Using LIBS to diagnose melanoma in biomedical fluids deposited on solid substrates: Limits of direct spectral analysis and capability of machine learning,” Spectrochim. Acta, Part B 146, 106–114 (2018).
[Crossref]

Tameze, C.

C. Tameze, R. Vincelette, N. Melikechi, V. Zeljkovic, and E. Izquierdo, “Empirical Analysis of Libs Images for Ovarian Cancer Detection,” in workshop on image analysis for multimedia interactive services76 (2007).

Tang, S. S.

Tang, Y.

Y. Ma, Z. Hu, Y. Tang, S. Ma, and Y. Lu, “Laser opto-ultrasonic dual detection for simultaneous compositional, structural, and stress analyses for wire + arc additive manufacturing,” Addit. Manuf. 31, 100956 (2020).
[Crossref]

Tortschanoff, A.

A. Tortschanoff, M. Baumgart, and G. Kroupa, “Compact DPSS-laser source for LIBS analysis of steel,” in Society of Photo-optical Instrumentation Engineers (2017).

Truesdale, M. D.

M. D. Truesdale, P. J. Cheetham, A. T. Turk, S. Sartori, and K. K. Badani, “Gleason score concordance on biopsy-confirmed prostate cancer: Is pathological re-evaluation necessary prior to radical prostatectomy?” BJU Int. 107(5), 749–754 (2011).
[Crossref]

Tucker, J. M.

M. D. Dyar, J. M. Tucker, S. Humphries, S. M. Clegg, R. C. Wiens, and M. D. Lane, “Strategies for Mars remote Laser-Induced Breakdown Spectroscopy analysis of sulfur in geological samples,” Spectrochim. Acta, Part B 66(1), 39–56 (2011).
[Crossref]

Turk, A. T.

M. D. Truesdale, P. J. Cheetham, A. T. Turk, S. Sartori, and K. K. Badani, “Gleason score concordance on biopsy-confirmed prostate cancer: Is pathological re-evaluation necessary prior to radical prostatectomy?” BJU Int. 107(5), 749–754 (2011).
[Crossref]

Vincelette, R.

C. Tameze, R. Vincelette, N. Melikechi, V. Zeljkovic, and E. Izquierdo, “Empirical Analysis of Libs Images for Ovarian Cancer Detection,” in workshop on image analysis for multimedia interactive services76 (2007).

Wang, S.

X. Zou, H. Zhao, J. Shi, S. Wang, X. Zhai, and X. Hu, “Texture analysis and grade discriminant of sausages based on ultrasound imaging,” Transactions of the Chinese Society of Agricultural Engineering 33, 284–290 (2017).
[Crossref]

Wiens, R. C.

M. D. Dyar, J. M. Tucker, S. Humphries, S. M. Clegg, R. C. Wiens, and M. D. Lane, “Strategies for Mars remote Laser-Induced Breakdown Spectroscopy analysis of sulfur in geological samples,” Spectrochim. Acta, Part B 66(1), 39–56 (2011).
[Crossref]

Yang, S.

Yu, X.

X. Chen, X. Li, S. Yang, X. Yu, and A. Liu, “Discrimination of lymphoma using laser-induced breakdown spectroscopy conducted on whole blood samples,” Biomed. Opt. Express 9(3), 1057–1068 (2018).
[Crossref]

X. Chen, X. Li, X. Yu, D. Chen, and A. Liu, “Diagnosis of human malignancies using laser-induced breakdown spectroscopy in combination with chemometric methods,” Spectrochim. Acta, Part B 139, 63–69 (2018).
[Crossref]

Yun, Tang

Yuan Rui, Tang Yun, Zhu Zhihao, Hao Zhongqi, Jiaming, and Li, “Accuracy improvement of quantitative analysis for major elements in laser-induced breakdown spectroscopy using single-sample calibration,” Anal. Chim. Acta 1064, 11–16 (2019).
[Crossref]

Zeljkovic, V.

C. Tameze, R. Vincelette, N. Melikechi, V. Zeljkovic, and E. Izquierdo, “Empirical Analysis of Libs Images for Ovarian Cancer Detection,” in workshop on image analysis for multimedia interactive services76 (2007).

Zeng, X. Y.

Zhai, X.

X. Zou, H. Zhao, J. Shi, S. Wang, X. Zhai, and X. Hu, “Texture analysis and grade discriminant of sausages based on ultrasound imaging,” Transactions of the Chinese Society of Agricultural Engineering 33, 284–290 (2017).
[Crossref]

Zhang, H.

C. Li, S. Zhang, H. Zhang, L. Pang, K. K. Lam, C. Hui, and S. Zhang, “Using the K-Nearest Neighbor Algorithm for the Classification of Lymph Node Metastasis in Gastric Cancer,” Computational and Mathematical Methods in Medicine 2012, 876545 (2012).
[Crossref]

Zhang, S.

C. Li, S. Zhang, H. Zhang, L. Pang, K. K. Lam, C. Hui, and S. Zhang, “Using the K-Nearest Neighbor Algorithm for the Classification of Lymph Node Metastasis in Gastric Cancer,” Computational and Mathematical Methods in Medicine 2012, 876545 (2012).
[Crossref]

C. Li, S. Zhang, H. Zhang, L. Pang, K. K. Lam, C. Hui, and S. Zhang, “Using the K-Nearest Neighbor Algorithm for the Classification of Lymph Node Metastasis in Gastric Cancer,” Computational and Mathematical Methods in Medicine 2012, 876545 (2012).
[Crossref]

Zhao, H.

X. Zou, H. Zhao, J. Shi, S. Wang, X. Zhai, and X. Hu, “Texture analysis and grade discriminant of sausages based on ultrasound imaging,” Transactions of the Chinese Society of Agricultural Engineering 33, 284–290 (2017).
[Crossref]

Zhihao, Zhu

Yuan Rui, Tang Yun, Zhu Zhihao, Hao Zhongqi, Jiaming, and Li, “Accuracy improvement of quantitative analysis for major elements in laser-induced breakdown spectroscopy using single-sample calibration,” Anal. Chim. Acta 1064, 11–16 (2019).
[Crossref]

Zhongqi, Hao

Yuan Rui, Tang Yun, Zhu Zhihao, Hao Zhongqi, Jiaming, and Li, “Accuracy improvement of quantitative analysis for major elements in laser-induced breakdown spectroscopy using single-sample calibration,” Anal. Chim. Acta 1064, 11–16 (2019).
[Crossref]

Zou, X.

X. Zou, H. Zhao, J. Shi, S. Wang, X. Zhai, and X. Hu, “Texture analysis and grade discriminant of sausages based on ultrasound imaging,” Transactions of the Chinese Society of Agricultural Engineering 33, 284–290 (2017).
[Crossref]

Addit. Manuf. (1)

Y. Ma, Z. Hu, Y. Tang, S. Ma, and Y. Lu, “Laser opto-ultrasonic dual detection for simultaneous compositional, structural, and stress analyses for wire + arc additive manufacturing,” Addit. Manuf. 31, 100956 (2020).
[Crossref]

Anal. Bioanal. Chem. (1)

H. Balzer, S. Hölters, V. Sturm, and R. Noll, “Systematic line selection for online coating thickness measurements of galvanised sheet steel using LIBS,” Anal. Bioanal. Chem. 385(2), 234–239 (2006).
[Crossref]

Anal. Chim. Acta (1)

Yuan Rui, Tang Yun, Zhu Zhihao, Hao Zhongqi, Jiaming, and Li, “Accuracy improvement of quantitative analysis for major elements in laser-induced breakdown spectroscopy using single-sample calibration,” Anal. Chim. Acta 1064, 11–16 (2019).
[Crossref]

Analog. Integr. Circ. Sig. Process. (1)

R. Nagulapalli, K. Hayatleh, S. Barker, S. Raparthy, and F. J. Lidgey, “A CMOS blood cancer detection sensor based on frequency deviation detection,” Analog. Integr. Circ. Sig. Process. 92(3), 437–442 (2017).
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Biomed. Opt. Express (2)

BJU Int. (1)

M. D. Truesdale, P. J. Cheetham, A. T. Turk, S. Sartori, and K. K. Badani, “Gleason score concordance on biopsy-confirmed prostate cancer: Is pathological re-evaluation necessary prior to radical prostatectomy?” BJU Int. 107(5), 749–754 (2011).
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Ca-Cancer J. Clin. (2)

N. Houssami and D. F. Hayes, “Review of Preoperative Magnetic Resonance Imaging (MRI) in Breast Cancer: Should MRI Be Performed on All Women with Newly Diagnosed, Early Stage Breast Cancer?” Ca-Cancer J. Clin. 59(5), 290–302 (2009).
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R. L. Siegel, K. D. Miller, and A. Jemal, “Cancer statistics, 2015,” Ca-Cancer J. Clin. 65(1), 5–29 (2015).
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Cancer Biol. Ther. (1)

C. Dong, M. Ji, and C. Ji, “Micro-RNAs and their potential target genes in leukemia pathogenesis,” Cancer Biol. Ther. 8(3), 200–205 (2009).
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Cell Journal (1)

R. Mansurabadi, S. Abroun, A. Hajifathali, A. Asri, A. Atashi, and M. Haghighi, “Expression ofhsa-MIR-204, RUNX2, PPARγ,andBCL2in Bone Marrow Derived Mesenchymal Stem Cells from Multiple Myeloma Patients and Normal Individuals,” Cell Journal 19, 27–36 (2017).
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Clin. Cancer Res. (1)

I. Cheung and N. K. V. Cheung, “Molecular detection of GAGE expression in peripheral blood and bone marrow: Utility as a tumor marker for neuroblastoma,” Clin. Cancer Res. 3, 821–826 (1997).

Computational and Mathematical Methods in Medicine (1)

C. Li, S. Zhang, H. Zhang, L. Pang, K. K. Lam, C. Hui, and S. Zhang, “Using the K-Nearest Neighbor Algorithm for the Classification of Lymph Node Metastasis in Gastric Cancer,” Computational and Mathematical Methods in Medicine 2012, 876545 (2012).
[Crossref]

Coord. Chem. Rev. (1)

B. Busser, S. Moncayo, J. L. Coll, L. Sancey, and V. Motto-Ros, “Elemental imaging using laser-induced breakdown spectroscopy: A new and promising approach for biological and medical applications,” Coord. Chem. Rev. 358, 70–79 (2018).
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J. Appl. Phys. (1)

S. J. Rehse, N. Jeyasingham, J. Diedrich, and S. Palchaudhuri, “A membrane basis for bacterial identification and discrimination using laser-induced breakdown spectroscopy,” J. Appl. Phys. 105(10), 102034 (2009).
[Crossref]

J. Urol. (1)

D. J. Grossklaus, C. S. Coffey, S. B. Shappell, G. S. Jack, and M. S. Cookson, “Percent Of Cancer in the Biopsy Set Predicts Pathological Findings After Prostatectomy,” J. Urol. 167(5), 2032–2036 (2002).
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Langmuir (1)

A. Sharma, G. Sumana, S. Sapra, and B. D. Malhotra, “Quantum Dots Self Assembly Based Interface for Blood Cancer Detection,” Langmuir 29(27), 8753–8762 (2013).
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Nanoscale (1)

M. U. Farid, S. Jeong, D. H. Seo, C. Lau, R. Ahmed, N. K. Gali, Z. Ning, and A. K. An, “Mechanistic insight on the in vitro toxicity of graphene oxide against biofilm forming bacteria using laser-induced breakdown spectroscopy,” Nanoscale 10(9), 4475–4487 (2018).
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Nihon Ketsueki Gakkai Zasshi (1)

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Opt. Express (1)

Pattern Analysis & Applications (1)

M. Skurichina and R. P. W. Duin, “Bagging, Boosting and the Random Subspace Method for Linear Classifiers,” Pattern Analysis & Applications 5(2), 121–135 (2002).
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Pediatr. Blood Cancer (1)

D. N. Friedman, E. Lis, C. A. Sklar, K. C. Oeffinger, and I. J. Dunkel, “Whole-body magnetic resonance imaging (WB-MRI) as surveillance for subsequent malignancies in survivors of hereditary retinoblastoma: A pilot study,” Pediatr. Blood Cancer 61(8), 1440–1444 (2014).
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Radiographics (1)

R. Ferraro, A. Agarwal, E. L. Martin-Macintosh, P. J. Peller, and R. M. Subramaniam, “MR Imaging and PET/CT in Diagnosis and Management of Multiple Myeloma,” Radiographics 35(2), 438–454 (2015).
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Sens. Actuators, B (1)

S. Hideshima, R. Sato, S. Inoue, S. Kuroiwa, and T. Osaka, “Detection of tumor marker in blood serum using antibody-modified field effect transistor with optimized BSA blocking,” Sens. Actuators, B 161(1), 146–150 (2012).
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Spectrochim. Acta, Part B (3)

R. Gaudiuso, E. Ewusiannan, N. Melikechi, X. Sun, B. Liu, L. F. Campesato, and T. Merghoub, “Using LIBS to diagnose melanoma in biomedical fluids deposited on solid substrates: Limits of direct spectral analysis and capability of machine learning,” Spectrochim. Acta, Part B 146, 106–114 (2018).
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X. Chen, X. Li, X. Yu, D. Chen, and A. Liu, “Diagnosis of human malignancies using laser-induced breakdown spectroscopy in combination with chemometric methods,” Spectrochim. Acta, Part B 139, 63–69 (2018).
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M. D. Dyar, J. M. Tucker, S. Humphries, S. M. Clegg, R. C. Wiens, and M. D. Lane, “Strategies for Mars remote Laser-Induced Breakdown Spectroscopy analysis of sulfur in geological samples,” Spectrochim. Acta, Part B 66(1), 39–56 (2011).
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Talanta (1)

A. Elhussein, A. K. Kassem, H. Ismail, and M. A. Harith, “Exploiting LIBS as a spectrochemical analytical technique in diagnosis of some types of human malignancies,” Talanta 82(2), 495–501 (2010).
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Transactions of the Chinese Society of Agricultural Engineering (1)

X. Zou, H. Zhao, J. Shi, S. Wang, X. Zhai, and X. Hu, “Texture analysis and grade discriminant of sausages based on ultrasound imaging,” Transactions of the Chinese Society of Agricultural Engineering 33, 284–290 (2017).
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Other (3)

C. Tameze, R. Vincelette, N. Melikechi, V. Zeljkovic, and E. Izquierdo, “Empirical Analysis of Libs Images for Ovarian Cancer Detection,” in workshop on image analysis for multimedia interactive services76 (2007).

A. Tortschanoff, M. Baumgart, and G. Kroupa, “Compact DPSS-laser source for LIBS analysis of steel,” in Society of Photo-optical Instrumentation Engineers (2017).

M. Saritha, B. B. Prakash, K. Sukesh, and B. Shrinivas, “Detection of blood cancer in microscopic images of human blood samples: A review,” in 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), 596–600 (2016).

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Figures (8)

Fig. 1.
Fig. 1. The whole spectra of (a) the substrate, (b) healthy control serum sample (c) AML serum sample, (d) CML serum sample, (e) MM serum samples, and (f) lymphoma serum samples.
Fig. 2.
Fig. 2. The classification results for AML versus (vs) healthy control (HC), CML vs HC, MM vs HC, and Lymphoma vs HC using (a) LDA, (b) KNN.
Fig. 3.
Fig. 3. The classification results for AML vs healthy control (HC), CML vs HC, MM vs HC, and Lymphoma vs HC using (a) RSM-LDA, (b) RSM-KNN
Fig. 4.
Fig. 4. The comparison of four kinds of model using ROC curve for (a) AML vs healthy control (HC), (b) CML vs HC, (c) MM vs HC, and (d) lymphoma vs HC.
Fig. 5.
Fig. 5. The results of the votes using RSM-LDA for a randomly sampling. (a) AML vs healthy control (HC), (b) CML vs HC, (c) MM vs HC, (d) lymphoma vs HC.
Fig. 6.
Fig. 6. The VI of selected lines for (a-c) AML vs healthy control classification, (d-f) CML vs healthy control classification, (g-i) MM vs healthy control classification, and (j-l) lymphoma vs healthy control classification.
Fig. 7.
Fig. 7. (a) The accuracy rates of four kinds of methods. (b) The confusion matrix of RSM-LDA.
Fig. 8.
Fig. 8. The vote of the RSM-LDA model for (a) AML samples, (b) CML samples, (c) MM samples, and (d) lymphoma samples.

Tables (3)

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Table 1. Analytical lines used for discrimination analysis.

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Table 2. The accuracy rate of four classifiers for identification between cancerous sample and healthy control (HC).

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Table 3. The ranking of VI for those four types of classification.

Equations (1)

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D e t e c t a b l e r a t e = i S ( R i N > t h r e s h o l d ) / S

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