PhD Defense: Paulo Sampaio Explores Mueller Matrix Polarimetry for Pancreatic Cancer Characterization

In this thesis, Paulo addressed the limitations of intraoperative frozen section (FS) analysis used in surgical tumor removal of pancreatic ductal adenocarcinoma (PDAC) by exploring multispectral Mueller matrix polarimetry (MMP) combined with machine learning as a fast, operator-agnostic, and robust alternative for pancreatic tissue characterization.

PDAC is one of the deadliest cancers, often remaining asymptomatic until advanced stages, thus requiring complex surgery for complete tumor removal. FS analysis, although widely used for intraoperative margin assessment, is a lengthy process (up to 40 minutes), relies heavily on operator expertise, and artifacts—created through the freezing process of the tissue—frequently hinder clear interpretation, making it a difficult, expensive and inefficient process.

Paulo used MMP paired with machine learning on fresh pancreatic samples, showing its ability to, in under one minute, (1) perform binary cancer vs. non-cancer classification comparable to reported FS performance, and (2) generate pixel-wise segmentation maps highlighting cancerous regions. To enable this work, he also documented the development of a custom, easy to operate device for semi-autonomous polarimetric data acquisition.

The achieved results demonstrate MMP’s potential for rapid, wide-field, and label-free ex vivo diagnosis, suggesting its suitability for future in vivo applications where non-destructive spatially resolved tissue assessment is required.

Congratulations to Dr. Paulo Sampaio on this incredible achievement and laying a foundation for a new class of time-efficient and user-friendly intraoperative diagnostic tools!

Publications:

Sampaio, Paulo; Scandella, Davide; Patty, Lucas; Márquez-Neila, Pablo; DiFazio, Heather; Wartenberg, Martin; Storni, Federico; Demory, Brice-Olivier; Candinas, Daniel; Perren, Aurel; Sznitman, Raphael (2025). Mueller matrix polarimetry for fresh pancreatic tissue segmentation. Applications of Machine Learning 2025:1360614. SPIE 10.1117/12.3064291

Sampaio, Paulo; Scandella, Davide; Patty, Lucas; DiFazio, Heather; Márquez-Neila, Pablo; Centeno Ramos, Irene; Wartenberg, Martin; Storni, Federico; Demory, Brice-Olivier; Candinas, Daniel; Perren, Aurel; Sznitman, Raphael (2025). Fast and user-friendly multi-spectra Mueller matrix polarimeter for fresh tissue biopsy imaging. Current Directions in Biomedical Engineering 11(1), pp. 170-173. De Gruyter 10.1515/cdbme-2025-0144

Sampaio, Paulo; Lopez-Antuña, Maria; Storni, Federico; Wicht, Jonatan; Sökeland, Greta; Wartenberg, Martin; Márquez-Neila, Pablo; Candinas, Daniel; Demory, Brice-Olivier; Perren, Aurel; Sznitman, Raphael (2023). Müller matrix polarimetry for pancreatic tissue characterization. Scientific Reports 13:16417. Nature Publishing Group 10.1038/s41598-023-43195-7