Rapid, label-free cancer detection in fresh pancreatic tissue using deep learning and multispectral Mueller matrix polarimetry.

Abstract

Frozen section (FS) tissue assessment is essential for guiding intraoperative surgical decision-making in oncology, particularly in procedures such as pancreatic ductal adenocarcinoma (PDAC) resections, where margin status critically impacts patient survival. The current gold standard, (FS), while widely used, suffers from notable limitations, including tissue artifacts, dependence on specialized expertise, and slow turnaround times, resulting in sampling errors and false negatives. To address these challenges, we present a novel approach for automatic cancer identification in fresh tissue biopsies using mul tispectral Mueller Matrix (MM) polarimetry. Our custom-built multispectral MM polarimeter captures polarization-resolved imaging across multiple wavelengths, enabling pixel-level analysis of tissue microstructure without staining or histology sectioning. Our approach thus allows for assessments in quasi-real time. From these, we propose a deep learning model that uses MM data collected from PDAC patients to distinguish cancerous from non-cancerous biopsies to assess samples automatically. Experimental results demonstrate classification performance comparable to RFS assessments performance found in clinical routine, with enhanced diagnostic speed. We show that our approach is consistent and coherent against pixel-wise annotations from histology slides. This study highlights the potential of MM polarimetry combined with machine learning as a viable, label-free alternative for real-time intraoperative cancer detection.