title: Fluorescence spectroscopy: an adjunct diagnostic tool to image-guided core needle biopsy of the breast.
abstract: We explored the use of a fiber-optic probe for in vivo fluorescence spectroscopy of breast tissues during percutaneous image-guided breast biopsy. A total of 121 biopsy samples with accompanying histological diagnosis were obtained clinically and investigated in this study. The tissue spectra were analyzed using partial least-squares analysis and represented using a set of principal components (PCs) with dramatically reduced data dimension. For nonmalignant tissue samples, a set of PCs that account for the largest amount of variance in the spectra displayed correlation with the percent tissue composition. For all tissue samples, a set of PCs was identified using a Wilcoxon rank-sum test as showing statistically significant differences between: 1) malignant and fibrous/benign; 2) malignant and adipose; and 3) malignant and nonmalignant breast samples. These PCs were used to distinguish malignant from other nonmalignant tissue types using a binary classification scheme based on both linear and nonlinear support vector machine (SVM) and logistic regression (LR). For the sample set investigated in this study, the SVM classifier provided a cross-validated sensitivity and specificity of up to 81% and 87%, respectively, for discrimination between malignant and fibrous/benign samples, and up to 81% and 81%, respectively, for discriminating between malignant and adipose samples. Classification based on LR was used to generate receiver operator curves with an area under the curve (AUC) of 0.87 for discriminating malignant versus fibrous/benign tissues, and an AUC of 0.84 for discriminating malignant from adipose tissue samples. This study demonstrates the feasibility of performing fluorescence spectroscopy during clinical core needle breast biopsy, and the potential of this technique for identifying breast malignancy in vivo.
authors.affiliation: Department of Electrical and Computer Engineering, University of Wisconsin, Madison, WI 53706, USA. czhu@uwalumni.com
authors.forename: Changfang
authors.initials: C
authors.lastname: Zhu
authors.forename: Elizabeth S
authors.initials: ES
authors.lastname: Burnside
authors.forename: Gale A
authors.initials: GA
authors.lastname: Sisney
authors.forename: Lonie R
authors.initials: LR
authors.lastname: Salkowski
authors.forename: Josephine M
authors.initials: JM
authors.lastname: Harter
authors.forename: Bing
authors.initials: B
authors.lastname: Yu
authors.forename: Nirmala
authors.initials: N
authors.lastname: Ramanujam
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D001185:Artificial Intelligence; D001707:Biopsy, Needle / Q000295:instrumentation / Q000379:methods; D001940:Breast / Q000473:pathology / Q000601:surgery*; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000473:pathology; D004867:Equipment Design; D005260:Female; D005336:Fiber Optic Technology; D006801:Humans; D016018:Least-Squares Analysis; D016015:Logistic Models; D025341:Principal Component Analysis; D012372:ROC Curve; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013050:Spectrometry, Fluorescence / Q000295:instrumentation / Q000379:methods; D018709:Statistics, Nonparametric; D025321:Surgery, Computer-Assisted / Q000295:instrumentation / Q000379:methods