Development and comparative assessment of Raman spectroscopic classification algorithms for lesion discrimination in stereotactic breast biopsies with microcalcifications

Microcalcifications are an early mammographic sign of breast cancer and a target for stereotactic breast needle biopsy. Here, we develop and compare different approaches for developing Raman classification algorithms to diagnose invasive and in situ breast cancer, fibrocystic change and fibroadenoma that can be associated with microcalcifications. In this study, Raman spectra were acquired from tissue cores obtained from fresh breast biopsies and analyzed using a constituent‐based breast model. Diagnostic algorithms based on the breast model fit coefficients were devised using logistic regression, C4.5 decision tree classification, k‐nearest neighbor (k ‐NN) and support vector machine (SVM) analysis, and subjected to leave‐one‐out cross validation. The best performing algorithm was based on SVM analysis (with radial basis function), which yielded a positive predictive value of 100% and negative predictive value of 96% for cancer diagnosis. Importantly, these results demonstrate that Raman spectroscopy provides adequate diagnostic information for lesion discrimination even in the presence of microcalcifications, which to the best of our knowledge has not been previously reported. (© 2013 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

[1]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[2]  R. Dasari,et al.  Raman spectroscopy-based sensitive and specific detection of glycated hemoglobin. , 2012, Analytical chemistry.

[3]  Ishan Barman,et al.  Incorporation of support vector machines in the LIBS toolbox for sensitive and robust classification amidst unexpected sample and system variability. , 2012, Analytical chemistry.

[4]  W. Marsden.  I and J , 2012 .

[5]  Zoya I. Volynskaya,et al.  Raman spectroscopy: a real-time tool for identifying microcalcifications during stereotactic breast core needle biopsies , 2011, Biomedical optics express.

[6]  Michael S Feld,et al.  Wavelength selection-based nonlinear calibration for transcutaneous blood glucose sensing using Raman spectroscopy. , 2011, Journal of biomedical optics.

[7]  R. Dasari,et al.  Investigation of the specificity of Raman spectroscopy in non-invasive blood glucose measurements , 2011, Analytical and bioanalytical chemistry.

[8]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[9]  Michael S Feld,et al.  Development of robust calibration models using support vector machines for spectroscopic monitoring of blood glucose. , 2010, Analytical chemistry.

[10]  P. Matousek,et al.  Towards a safe non-invasive method for evaluating the carbonate substitution levels of hydroxyapatite (HAP) in micro-calcifications found in breast tissue. , 2010, The Analyst.

[11]  N. Shepherd,et al.  New relationships between breast microcalcifications and cancer , 2010, British Journal of Cancer.

[12]  Z. Volynskaya.  Multimodal spectroscopy : real-time diagnosis of breast cancer during core needle biopsy , 2010 .

[13]  Zoya I. Volynskaya,et al.  Diagnosing breast cancer using Raman spectroscopy: prospective analysis. , 2009, Journal of biomedical optics.

[14]  Michael S Feld,et al.  Turbidity-corrected Raman spectroscopy for blood analyte detection. , 2009, Analytical chemistry.

[15]  Anita Mahadevan-Jansen,et al.  Spatially offset Raman spectroscopy of layered soft tissues. , 2009, Optics letters.

[16]  A. Rim,et al.  Trends in breast cancer screening and diagnosis. , 2008, Cleveland Clinic journal of medicine.

[17]  Wei Zheng,et al.  Classification of colonic tissues using near-infrared Raman spectroscopy and support vector machines. , 2008, International journal of oncology.

[18]  R. Fassihi,et al.  A novel approach in the assessment of polymeric film formation and film adhesion on different pharmaceutical solid substrates , 2004, AAPS PharmSciTech.

[19]  Pavel Matousek,et al.  Prospects for the diagnosis of breast cancer by noninvasive probing of calcifications using transmission Raman spectroscopy. , 2007, Journal of biomedical optics.

[20]  R. J. Jackman,et al.  Breast microcalcifications: retrieval failure at prone stereotactic core and vacuum breast biopsy--frequency, causes, and outcome. , 2006, Radiology.

[21]  Zoya I. Volynskaya,et al.  In vivo margin assessment during partial mastectomy breast surgery using raman spectroscopy. , 2006, Cancer research.

[22]  Yi Pan,et al.  Rule generation for protein secondary structure prediction with support vector machines and decision tree , 2006, IEEE Transactions on NanoBioscience.

[23]  R. Dasari,et al.  Diagnosing breast cancer by using Raman spectroscopy. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[24]  Ivan Bratko,et al.  Microarray data mining with visual programming , 2005, Bioinform..

[25]  Anthony Widjaja,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.

[26]  Giles M. Foody,et al.  A relative evaluation of multiclass image classification by support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[27]  L. Buydens,et al.  Multivariate calibration with least-squares support vector machines. , 2004, Analytical chemistry.

[28]  Michael S Feld,et al.  Optical fiber probe for biomedical Raman spectroscopy. , 2004, Applied optics.

[29]  Richard Baumgartner,et al.  Class prediction and discovery using gene microarray and proteomics mass spectroscopy data: curses, caveats, cautions , 2003, Bioinform..

[30]  G. Mercier,et al.  Support vector machines for hyperspectral image classification with spectral-based kernels , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).

[31]  A. Belousov,et al.  A flexible classification approach with optimal generalisation performance: support vector machines , 2002 .

[32]  R. Dasari,et al.  Identifying microcalcifications in benign and malignant breast lesions by probing differences in their chemical composition using Raman spectroscopy. , 2002, Cancer research.

[33]  R. Dasari,et al.  Raman microspectroscopic model of human breast tissue: implications for breast cancer diagnosis in vivo , 2002 .

[34]  L. S. Davis,et al.  An assessment of support vector machines for land cover classi(cid:142) cation , 2002 .

[35]  Alexander J. Smola,et al.  Learning with Kernels: support vector machines, regularization, optimization, and beyond , 2001, Adaptive computation and machine learning series.

[36]  M. Fitzmaurice,et al.  Principles and pitfalls of diagnostic test development: implications for spectroscopic tissue diagnosis. , 2000, Journal of biomedical optics.

[37]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[38]  J. Landercasper,et al.  Histological correlation of microcalcifications in breast biopsy specimens. , 1999, Archives of surgery.

[39]  Massimiliano Pontil,et al.  Properties of Support Vector Machines , 1998, Neural Computation.

[40]  A K Smilde,et al.  Influence of temperature on vibrational spectra and consequences for the predictive ability of multivariate models. , 1998, Analytical chemistry.

[41]  M. Feld,et al.  Raman Spectroscopy and Fluorescence Photon Migration for Breast Cancer Diagnosis and Imaging , 1998, Photochemistry and photobiology.

[42]  Georges G. Grinstein,et al.  DNA visual and analytic data mining , 1997, Proceedings. Visualization '97 (Cat. No. 97CB36155).

[43]  Hans-Peter Kriegel,et al.  'Circle Segments': A Technique for Visually Exploring Large Multidimensional Data Sets , 1996 .

[44]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[45]  Belur V. Dasarathy,et al.  Nearest neighbor (NN) norms: NN pattern classification techniques , 1991 .

[46]  Radi Mj,et al.  Calcium oxalate crystals in breast biopsies. An overlooked form of microcalcification associated with benign breast disease. , 1989 .