Layer thickness prediction and tissue classification in two-layered tissue structures using diffuse reflectance spectroscopy

During oncological surgery, it can be challenging to identify the tumor and establish adequate resection margins. This study proposes a new two-layer approach in which diffuse reflectance spectroscopy (DRS) is used to predict the top layer thickness and classify the layers in two-layered phantom and animal tissue. Using wavelet-based and peak-based DRS spectral features, the proposed method could predict the top layer thickness with an accuracy of up to 0.35 mm. In addition, the tissue types of the first and second layers were classified with an accuracy of 0.95 and 0.99. Distinguishing multiple tissue layers during spectral analyses results in a better understanding of more complex tissue structures encountered in surgical practice.Medical Instruments & Bio-Inspired Technolog

Combining diffuse reflectance spectroscopy and ultrasound imaging for resection margin assessment during colorectal cancer surgery

Establishing adequate resection margins during colorectal cancer surgery is challenging. Currently, in up to 30% of the cases the tumor is not completely removed, which emphasizes the lack of a real-time tissue discrimination tool that can assess resection margins up to multiple millimeters in depth. Therefore, we propose to combine spectral data from diffuse reflectance spectroscopy (DRS) with spatial information from ultrasound (US) imaging to evaluate multi-layered tissue structures. First, measurements with animal tissue were performed to evaluate the feasibility of the concept. The phantoms consisted of muscle and fat layers, with a varying top layer thickness of 0-10 mm. DRS spectra of 250 locations were obtained and corresponding US images were acquired. DRS features were extracted using the wavelet transform. US features were extracted based on the graph theory and first-order gradient. Using a regression analysis and combined DRS and US features, the top layer thickness was estimated with an error of up to 0.48 mm. The tissue types of the first and second layers were classified with accuracies of 0.95 and 0.99 respectively, using a support vector machine model. Medical Instruments & Bio-Inspired Technolog

Toward complete oral cavity cancer resection using a handheld diffuse reflectance spectroscopy probe

This ex-vivo study evaluates the feasibility of diffuse reflectance spectroscopy (DRS) for discriminating tumor from healthy tissue, with the aim to develop a technology that can assess resection margins for the presence of tumor cells during oral cavity cancer surgery. Diffuse reflectance spectra were acquired on fresh surgical specimens from 28 patients with oral cavity squamous cell carcinoma. The spectra (400 to 1600 nm) were detected after illuminating tissue with a source fiber at 0.3-, 0.7-, 1.0-, and 2.0-mm distances from a detection fiber, obtaining spectral information from different sampling depths. The spectra were correlated with histopathology. A total of 76 spectra were obtained from tumor tissue and 110 spectra from healthy muscle tissue. The first- and second-order derivatives of the spectra were calculated and a classification algorithm was developed using fivefold cross validation with a linear support vector machine. The best results were obtained by the reflectance measured with a 1-mm source-detector distance (sensitivity, specificity, and accuracy are 89%, 82%, and 86%, respectively). DRS can accurately discriminate tumor from healthy tissue in an ex-vivo setting using a 1-mm source-detector distance. Accurate validation methods are warranted for larger sampling depths to allow for guidance during oral cavity cancer excision.Medical Instruments & Bio-Inspired Technolog