Colorectal cancer surgical guidance by using diffuse reflectance spectroscopy

Colorectal cancer (CRC) is the third most common and second most deadly type of cancer worldwide. Late-stage detection of CRC occurs in most cases and leads to a large mortality due to poor prognosis. Mortality and poor prognosis are partially caused by cancer recurrence and postoperative complications, which should be improved for increased patient survival. Therefore, patient survival could be increased by using accurate surgical guidance tools based on diffuse reflectance spectroscopy (DRS). DRS enables real-time tissue identification for potential cancer margin delineation through determination of the circumferential resection margin (CRM), while also supporting non-invasive and label-free approaches for laparoscopic surgery to avoid short-term complications of open surgery as suitable. In this study, we have estimated the scattering properties and chromophore concentrations based on 2949 DRS measurements of freshly excised ex vivo specimens of 47 patients, and used this estimation to classify colorectal wall (CW), fat and tumor tissues. DRS measurements were performed with fiber-optic probes of 630-µm source detector distance (SDD; probe 1) and 2500-µm SDD (probe 2) to measure tissue layers from ~0.5-1mm and from ~0.5-2 mm deep, respectively. By using the 5-fold cross-validation of machine learning models generated with the classification and regression tree (CART) algorithm, we achieved 95.9 ± 0.7% sensitivity, 98.9 ± 0.3% specificity, 90.2 ± 0.4% accuracy, and 95.5 ± 0.3% AUC for probe 1. Similarly, we achieved 96.9 ± 0.8 % sensitivity, 98.9 ± 0.2 % specificity, 94.0 ± 0.4 % accuracy, and 96.7 ± 0.4 % AUC for probe 2. To the best of our knowledge, this is the first study to evaluate the tissue chromophore concentrations and scattering properties in both superficial and deeper tissue layers based on tissue surface measurements for application in CRC detection during open surgery, laparoscopic surgery and/or robotic surgery. Aiming for the same application, this study is also the first study to use Monte Carlo look-up table methods to extract such values based on the ultraviolet, visible and near-infrared wavelength ranges (350-1920 nm).