Diffuse reflectance spectroscopy for colorectal cancer surgical guidance: towards real-time tissue characterization and new biomarkers

Colorectal cancer (CRC) is the third most common and second most deadly type of cancer worldwide, representing 11.3% of the diagnosed cancer cases and resulting in 10.2% (0.88 million) of the cancer related deaths in 2020. CRCs are typically detected at the late stage, which leads to high mortality and morbidity. Mortality and poor prognosis are partially caused by cancer recurrence and postoperative complications. Patient survival could be increased by improving precision in surgical resection 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 normal colorectal wall (CW), fat and tumor tissues. DRS measurements were performed with fiber-optic probes of 630 mu m source-detector distance (SDD; probe 1) and 2500 mu m SDD (probe 2) to measure tissue layers similar to 0.5-1 mm and similar to 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. Our study assessed the most important light-based markers for surgical and laparoscopic delineation of colorectal cancer margins. Real-time and accurate tissue identification can improve surgical outcomes and obviate the need for multiple biopsies.