Automated Analysis of Scattering-based Light Sheet Microscopy Images of Anal Squamous Intraepithelial Lesions

Abstract

Anal cancer presents diagnostic challenges, particularly in identifying high-grade squamous intraepithelial lesions (HSIL), with its increasing incidence and mortality rates. Current diagnosis methods, including cytology, biopsy, and high resolution anoscopy (HRA), provide important diagnostic information. However, cytology is often limited by suboptimal sensitivity and specificity, while high resolution anoscopy-guided biopsy is limited by its long processing times due to unnecessary biopsies and staining requirements. Scattering-based light sheet microscopy (sLSM) can offer an alternative approach by utilizing intrinsic tissue scattering properties to visualize morphologic features without the need for additional labeling or staining.In this study, we developed and evaluated an automated algorithm for analyzing 187 sLSM images obtained from 80 anal biopsies. The method employed a row-by-row binarization technique for nuclear segmentation, achieving high precision (0.97) and recall (0.91). Seven nuclear features, including nuclear intensity, intensity slope as a function of depth, nuclear-to-nuclear distance, nuclear-to-cytoplasm ratio, cell density, nuclear area, and proportion of pixels corresponding to nuclei were extracted and statistically analyzed. Among the seven features, six showed statistically significant differences between HSIL and non-HSIL (non-dysplastic or low-grade squamous intraepithelial lesion, LSIL). A linear support vector machine (SVM) was trained and tested using five-fold cross validation on these features. The classifier achieved a sensitivity of 90%, specificity of 70%, and area under the curve (AUC) of 0.89 for per-image diagnosis, and sensitivity of 90%, specificity of 80%, and area under the curve (AUC) of 0.93 for per-biopsy diagnosis.