Application of Raman spectroscopy to identify microcalcifications and underlying breast lesions at stereotactic core needle biopsy

Abstract

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 single-step 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.

Figure 1

Schematic diagram of sequential two-step optimized Raman algorithm using a logistic regression algorithm for Step 1 and two separate support vector machine classification models (naïve and optimized) for Step 2. Both these algorithms were used in a leave-one-out cross-validation protocol. (^*Normal tissue sites with microcalcifications were not considered for algorithm development as they represent a discordance between radiographic assessment (lesion with microcalcifications) and histopathological evaluation (normal))

Figure 2

Histopathology and Raman spectrum (blue) with model fit (red) and residual (black) for a typical breast lesion (FCC) with type II microcalcifications. The microcalcifications are visible as dark blue concretions (arrow) in the photomicrograph in Figure 2(a) (H&E; 10X). Note the yellow ink on the breast tissue surface at the top in Figure 2(a), marking the site for spectral correlation. The corresponding Raman spectrum in Figure 2(b) shows a prominent band at 960 cm⁻¹ due to CHA (arrow), which is a major constituent of type II microcalcifications.

Figure 3

ROC curve for the single-step SVM Raman decision algorithm for the diagnosis of breast cancer with microcalcifications. The x- and y-axis represent the false positive (FP) rate and the true positive (TP) rate, respectively. The ROC curve of two indistinguishable populations, represented by the dashed line, is included for comparison. The area under the curve (AUC) is 0.92, the AUC for a perfect algorithm is 1.