Diffuse reflectance spectroscopy for colorectal cancer surgical guidance: towards real-time tissue characterization and new biomarkers.
Marcelo Saito NogueiraSiddra MaryamMichael AmissahShane KilleenMicheal O'RiordainStefan Andersson-EngelsPublished in: The Analyst (2023)
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 μm source-detector distance (SDD; probe 1) and 2500 μm SDD (probe 2) to measure tissue layers ∼0.5-1 mm and ∼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.
Keyphrases
- papillary thyroid
- machine learning
- poor prognosis
- squamous cell
- living cells
- end stage renal disease
- high resolution
- minimally invasive
- quantum dots
- deep learning
- chronic kidney disease
- long non coding rna
- gene expression
- single molecule
- childhood cancer
- laparoscopic surgery
- newly diagnosed
- risk factors
- squamous cell carcinoma
- low grade
- risk assessment
- ejection fraction
- young adults
- peritoneal dialysis
- magnetic resonance
- coronary artery disease
- photodynamic therapy
- atrial fibrillation
- fluorescence imaging
- liquid chromatography
- contrast enhanced