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 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.

Insights into Biochemical Sources and Diffuse Reflectance Spectral Features for Colorectal Cancer Detection and Localization.

Colorectal cancer (CRC) is the third most common and second most deadly type of cancer worldwide. Early detection not only reduces mortality but also improves patient prognosis by allowing the use of minimally invasive techniques to remove cancer while avoiding major surgery. Expanding the use of microsurgical techniques requires accurate diagnosis and delineation of the tumor margins in order to allow complete excision of cancer. We have used diffuse reflectance spectroscopy (DRS) to identify the main optical CRC biomarkers and to optimize parameters for the integration of such technologies into medical devices. A total number of 2889 diffuse reflectance spectra were collected in ex vivo specimens from 47 patients. Short source-detector distance (SDD) and long-SDD fiber-optic probes were employed to measure tissue layers from 0.5 to 1 mm and from 0.5 to 1.9 mm deep, respectively. The most important biomolecules contributing to differentiating DRS between tissue types were oxy- and deoxy-hemoglobin (Hb and HbO2), followed by water and lipid. Accurate tissue classification and potential DRS device miniaturization using Hb, HbO2, lipid and water data were achieved particularly well within the wavelength ranges 350-590 nm and 600-1230 nm for the short-SDD probe, and 380-400 nm, 420-610 nm, and 650-950 nm for the long-SDD probe.

Evaluation of wavelength ranges and tissue depth probed by diffuse reflectance spectroscopy for colorectal cancer detection.

Colorectal cancer (CRC) is the third most common type of cancer worldwide and the second most deadly. Recent research efforts have focused on developing non-invasive techniques for CRC detection. In this study, we evaluated the diagnostic capabilities of diffuse reflectance spectroscopy (DRS) for CRC detection by building 6 classification models based on support vector machines (SVMs). Our dataset consists of 2889 diffuse reflectance spectra collected from freshly excised ex vivo tissues of 47 patients over wavelengths ranging from 350 and 1919 nm with source-detector distances of 630-µm and 2500-µm to probe different depths. Quadratic SVMs were used and performance was evaluated using twofold cross-validation on 10 iterations of randomized training and test sets. We achieved (93.5 ± 2.4)% sensitivity, (94.0 ± 1.7)% specificity AUC by probing the superficial colorectal tissue and (96.1 ± 1.8)% sensitivity, (95.7 ± 0.6)% specificity AUC by sampling deeper tissue layers. To the best of our knowledge, this is the first DRS study to investigate the potential of probing deeper tissue layers using larger SDD probes for CRC detection in the luminal wall. The data analysis showed that using a broader spectrum and longer near-infrared wavelengths can improve the diagnostic accuracy of CRC as well as probing deeper tissue layers.

Near Infrared Spectroscopic Mapping of Functional Properties of Equine Articular Cartilage.

Mechanical properties of articular cartilage are vital for normal joint function, which can be severely compromised by injuries. Quantitative characterization of cartilage injuries, and evaluation of cartilage stiffness and thickness by means of conventional arthroscopy is poorly reproducible or impossible. In this study, we demonstrate the potential of near infrared (NIR) spectroscopy for predicting and mapping the functional properties of equine articular cartilage at and around lesion sites. Lesion and non-lesion areas of interests (AI, N = 44) of equine joints (N = 5) were divided into grids and NIR spectra were acquired from all grid points (N = 869). Partial least squares (PLS) regression was used to investigate the correlation between the absorbance spectra and thickness, equilibrium modulus, dynamic modulus, and instantaneous modulus at the grid points of 41 AIs. Subsequently, the developed PLS models were validated with spectral data from the grid points of 3 independent AIs. Significant correlations were obtained between spectral data and cartilage thickness (R 2 = 70.3%, p < 0.0001), equilibrium modulus (R 2 = 67.8%, p < 0.0001), dynamic modulus (R 2 = 68.9%, p < 0.0001) and instantaneous modulus (R 2 = 41.8%, p < 0.0001). Relatively low errors were observed in the predicted thickness (5.9%) and instantaneous modulus (9.0%) maps. Thus, if well implemented, NIR spectroscopy could enable arthroscopic evaluation and mapping of cartilage functional properties at and around lesion sites.