SignificanceDuring breast-conserving surgeries, it is essential to evaluate the resection margins (edges of breast specimen) to determine whether the tumor has been removed completely. In current surgical practice, there are no methods available to aid in accurate real-time margin evaluation.AimIn this study, we investigated the diagnostic accuracy of diffuse reflectance spectroscopy (DRS) combined with tissue classification models in discriminating tumorous tissue from healthy tissue up to 2 mm in depth on the actual resection margin of in vivo breast tissue.ApproachWe collected an extensive dataset of DRS measurements on ex vivo breast tissue and in vivo breast tissue, which we used to develop different classification models for tissue classification. Next, these models were used in vivo to evaluate the performance of DRS for tissue discrimination during breast conserving surgery. We investigated which training strategy yielded optimum results for the classification model with the highest performance.ResultsWe achieved a Matthews correlation coefficient of 0.76, a sensitivity of 96.7% (95% CI 95.6% to 98.2%), a specificity of 90.6% (95% CI 86.3% to 97.9%) and an area under the curve of 0.98 by training the optimum model on a combination of ex vivo and in vivo DRS data.ConclusionsDRS allows real-time margin assessment with a high sensitivity and specificity during breast-conserving surgeries.
Despite improved surgical techniques, the reported rate of positive surgical margins (PSM) after robotic-assisted laparoscopic prostatectomy remains as high as 20-40%. These PSM are associated with disease progression after surgery and should be avoided. Currently, there is no technique available to detect PSM intraoperatively. Diffuse Reflectance Spectroscopy (DRS) is proposed as a technique to detect tumor tissue at the surgical resection margin, intraoperatively. Over 800 measurements were performed on the prostate surface and, to mimic a PSM, on the cleaved prostate surface. A machine learning-based classification model was able to discriminate healthy tissue from tumor tissue with sufficient accuracy.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.