Infrared Imaging Combined with Machine Learning for Detection of the (Pre)Invasive Pancreatic Neoplasia

With the challenge of limited early stage detection and a resulting five-year survival rate of only 13%, pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal cancers. Replacing the high-cost and time-consuming grading of pancreatic samples by pathologists with automated diagnostic approaches can revolutionize PDAC detection and thus accelerate patient admission into the clinical setting for treatment. To address this unmet diagnostic need and facilitate the shift of tissue screening toward automated systems, we combined stain-free histology-specifically, Fourier-transform infrared (FT-IR) imaging-with machine learning. The obtained stain-free model was trained to distinguish between normal, benign, and malignant areas in analyzed specimens using hematoxylin and eosin stained pancreatic tissues isolated from KC (Kras^G12D/+; Pdx1-Cre) or KPC mice (Kras^G12D/+; Trp53^R172H/+; Pdx1-Cre). Due to the pancreas-specific mosaic expression of the mutant Kras and Trp53 genes, changes in pancreatic tissues of this mouse model of PDAC closely mirror the gradual transformation of normal pancreatic epithelia into (pre)malignant structures. Thus, this mouse model provides a reliable representation of human disease progression, which we tracked in our study with a Random Forest classifier to achieve accurate detection at the cellular level. This approach yielded a comprehensive model that distinguishes normal pancreatic tissues from pathological features such as pancreatic intraepithelial neoplasia (PanIN), cancerous regions, hemorrhages, and collagen fibers, as well as a streamlined model designed to rapidly identify normal tissues versus pathologically altered regions, including PanINs. These models offer highly accurate diagnostic tools for the early detection of pancreatic malignancies, thus significantly improving the chance for timely therapeutic intervention against PDAC.