Label-free histological identification of intraductal carcinoma of the prostate using texture analysis-based multimodal stimulated Raman scattering microscopy

Intraductal carcinoma of the prostate (IDC-P) is a very aggressive histopathological subtype of prostate cancer (PCa) that is strongly associated with poor clinical outcomes but for which no accurate biomarkers exist. Here, we demonstrate a novel application of texture analysis-based machine learning alongside multimodal nonlinear optical imaging using second-harmonic generation (SHG) and stimulated Raman scattering (SRS) at 1450 cm^-1 and 1668 cm^-1 Raman shifts to distinguish IDC-P from regular PCa and benign prostate. Images from each tissue type were analyzed to extract the first-order statistics and texture-based second-order statistics derived from the gray-level co-occurrence matrix of the images. A machine learning model was constructed using support vector machine (SVM) to classify the prostate tissue based on these statistics. Our results demonstrate that SVM models trained on either SHG or SRS images accurately classify IDC-P as well as high-grade PCa, low-grade PCa, and benign tissue with a mean classification accuracy exceeding 89%. Moreover, a mean classification accuracy of 98% was achieved using an SVM model trained on combined SHG and SRS images. Our study demonstrates that multimodal nonlinear optical imaging using SHG and SRS can be combined with texture analysis-based SVM classification to provide pathologists with a reliable biomarker of IDC-P.