Identification of colorectal malignancies enabled by phasor-based autofluorescence lifetime macroimaging and ensemble learning.

Significance: Colorectal cancer (CRC) remains one of the most frequent cancers and a leading contributor to cancer-associated mortality globally. CRCs are often diagnosed at an advanced stage, which leads to high mortality and morbidity. This outcome is exacerbated by high rates of recurrence and postoperative complications that contribute substantially to poor prognosis. Advancements in endoscopic assessment have improved CRC prevention, early detection, and surveillance over the years. Yet, CRC remains one of the most significant health challenges of the 21st century. Label-free optical spectroscopy methods have long been explored as potential partners to endoscopy, not only to enhance diagnostic accuracy but also to confer predictive capabilities to endoscopic evaluations. Aim: We investigated the potential of time-resolved autofluorescence measurements excited at 375 and 445 nm to correctly classify benign and malignant tissues in CRC surgical specimens from 117 patients. Approach: Multiparametric autofluorescence lifetime data were collected in two distinct datasets, which were used for training ( Results: Using 5-fold cross-validation, we achieved Conclusions: Although preliminary, our findings underscore the potential impact of AI-assisted autofluorescence lifetime measurements in advancing CRC prevention, early detection, and surveillance efforts.