Application of Raman Spectroscopy to Identify Microcalcifications and Underlying Breast Lesions at Stereotactic Core Needle Biopsy

被引:78
作者
Barman, Ishan [1 ]
Dingari, Narahara Chari [1 ]
Saha, Anushree [2 ,3 ]
McGee, Sasha [2 ,3 ]
Galindo, Luis H. [1 ]
Liu, Wendy [2 ,3 ,4 ,5 ]
Plecha, Donna [2 ,3 ,4 ,5 ]
Klein, Nina [2 ,3 ,4 ,5 ]
Dasari, Ramachandra Rao [1 ]
Fitzmaurice, Maryann [2 ,3 ]
机构
[1] MIT, GR Harrison Spect Lab, Cambridge, MA 02139 USA
[2] Case Western Reserve Univ, Dept Pathol, Cleveland, OH 44106 USA
[3] Case Western Reserve Univ, Dept Radiol, Cleveland, OH 44106 USA
[4] Univ Hosp Case Med Ctr, Dept Pathol, Cleveland, OH USA
[5] Univ Hosp Case Med Ctr, Dept Radiol, Cleveland, OH USA
关键词
SUPPORT VECTOR MACHINES; CANCER DIAGNOSIS; BENIGN; TISSUE; CLASSIFICATION; CALCIFICATIONS; MODEL;
D O I
10.1158/0008-5472.CAN-12-2313
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Microcalcifications are a feature of diagnostic significance on a mammogram and a target for stereotactic breast needle biopsy. Here, we report development of a Raman spectroscopy technique to simultaneously identify microcalcification status and diagnose the underlying breast lesion, in real-time, during stereotactic core needle biopsy procedures. Raman spectra were obtained ex vivo from 146 tissue sites from fresh stereotactic breast needle biopsy tissue cores from 33 patients, including 50 normal tissue sites, 77 lesions with microcalcifications, and 19 lesions without microcalcifications, using a compact clinical system. The Raman spectra were modeled on the basis of the breast tissue components, and a support vector machine framework was used to develop a singlestep diagnostic algorithm to distinguish normal tissue, fibrocystic change (FCC), fibroadenoma, and breast cancer, in the absence and presence of microcalcifications. This algorithm was subjected to leave-one-site-out cross-validation, yielding a positive predictive value, negative predictive value, sensitivity, and specificity of 100%, 95.6%, 62.5%, and 100% for diagnosis of breast cancer (with or without microcalcifications) and an overall accuracy of 82.2% for classification into specific categories of normal tissue, FCC, fibroadenoma, or breast cancer (with and without microcalcifications). Notably, the majority of breast cancers diagnosed are ductal carcinoma in situ (DCIS), the most common lesion associated with microcalcifications, which could not be diagnosed using previous Raman algorithm(s). Our study shows the potential of Raman spectroscopy to concomitantly detect microcalcifications and diagnose associated lesions, including DCIS, and thus provide real-time feedback to radiologists during such biopsy procedures, reducing nondiagnostic and false-negative biopsies. (C) 2013 AACR.
引用
收藏
页码:3206 / 3215
页数:10
相关论文
共 37 条
  • [1] Raman 'optical biopsy' of human breast cancer
    Abramczyk, Halina
    Brozek-Pluska, Beata
    Surmacki, Jakub
    Jablonska-Gajewicz, Joanna
    Kordek, Radzislaw
    [J]. PROGRESS IN BIOPHYSICS & MOLECULAR BIOLOGY, 2012, 108 (1-2) : 74 - 81
  • [2] [Anonymous], 2012, Breast cancer facts figures 2011-2012
  • [3] Depth profiling of calcifications in breast tissue using picosecond Kerr-gated Raman spectroscopy
    Baker, Rebecca
    Matousek, Pavel
    Ronayne, Kate Louise
    Parker, Anthony William
    Rogers, Keith
    Stone, Nicholas
    [J]. ANALYST, 2007, 132 (01) : 48 - 53
  • [4] Rapid and accurate determination of tissue optical properties using least-squares support vector machines
    Barman, Ishan
    Dingari, Narahara Chari
    Rajaram, Narasimhan
    Tunnell, James W.
    Dasari, Ramachandra R.
    Feld, Michael S.
    [J]. BIOMEDICAL OPTICS EXPRESS, 2011, 2 (03): : 592 - 599
  • [5] Segmentation and numerical analysis of microcalcifications on mammograms using mathematical morphology
    Betal, D
    Roberts, N
    Whitehouse, GH
    [J]. BRITISH JOURNAL OF RADIOLOGY, 1997, 70 (837) : 903 - 917
  • [6] Biochemical analysis of human breast tissues using Fourier-transform Raman spectroscopy
    Bitar, Renata Andrade
    Martinho, Herculano da Silva
    Tierra-Criollo, Carlos Julio
    Zambelli Ramalho, Leandra Naira
    Netto, Mario Mourao
    Martin, Airton Abrahao
    [J]. JOURNAL OF BIOMEDICAL OPTICS, 2006, 11 (05)
  • [7] Support Vector Machines for classification and regression
    Brereton, Richard G.
    Lloyd, Gavin R.
    [J]. ANALYST, 2010, 135 (02) : 230 - 267
  • [8] Discrimination of normal, benign, and malignant breast tissues by Raman spectroscopy
    Chowdary, M. V. P.
    Kumar, K. Kalyan
    Kurien, Jacob
    Mathew, Stanley
    Krishna, C. Murali
    [J]. BIOPOLYMERS, 2006, 83 (05) : 556 - 569
  • [9] Biochemical Correlation of Raman Spectra of Normal, Benign and Malignant Breast Tissues: A Spectral Deconvolution Study
    Chowdary, M. V. P.
    Kumar, K. Kalyan
    Mathew, Stanley
    Rao, Lakshmi
    Krishna, C. Murali
    Kurien, Jacob
    [J]. BIOPOLYMERS, 2009, 91 (07) : 539 - 546
  • [10] Demsar J, 2004, LECT NOTES ARTIF INT, V3202, P537