Background: Intraoperative identification of cancerous tissue is fundamental during oncological surgical or endoscopic procedures. This relies on visual assessment supported by histopathological evaluation, implying a longer operative time. Hyperspectral imaging (HSI), a contrast-free and contactless imaging technology, provides spatially resolved spectroscopic analysis, with the potential to differentiate tissue at a cellular level. However, HSI produces “big data”, which is impossible to directly interpret by clinicians. We hypothesize that advanced machine learning algorithms (convolutional neural networks–CNNs) can accurately detect colorectal cancer in HSI data. Methods: In 34 patients undergoing colorectal resections for cancer, immediately after extraction, the specimen was opened, the tumor-bearing section was exposed and imaged using HSI. Cancer and normal mucosa were categorized from histopathology. A state-of-the-art CNN was developed to automatically detect regions of colorectal cancer in a hyperspectral image. Accuracy was validated with three levels of cross-validation (twofold, fivefold, and 15-fold). Results: 32 patients had colorectal adenocarcinomas confirmed by histopathology (9 left, 11 right, 4 transverse colon, and 9 rectum). 6 patients had a local initial stage (T1-2) and 26 had a local advanced stage (T3-4). The cancer detection performance of the CNN using 15-fold cross-validation showed high sensitivity and specificity (87% and 90%, respectively) and a ROC-AUC score of 0.95 (considered outstanding). In the T1-2 group, the sensitivity and specificity were 89% and 90%, respectively, and in the T3-4 group, the sensitivity and specificity were 81% and 93%, respectively. Conclusions: Automatic colorectal cancer detection on fresh specimens using HSI, using a properly trained CNN is feasible and accurate, even with small datasets, regardless of the local tumor extension. In the near future, this approach may become a useful intraoperative tool during oncological endoscopic and surgical procedures, and may result in precise and non-destructive optical biopsies to support objective and consistent tumor-free resection margins.
