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.
SignificanceAccurately distinguishing tumor tissue from normal tissue is crucial to achieve complete resections during soft tissue sarcoma (STS) surgery while preserving critical structures. Incomplete tumor resections are associated with an increased risk of local recurrence and worse patient prognosis.AimWe evaluate the performance of diffuse reflectance spectroscopy (DRS) to distinguish tumor tissue from healthy tissue in STSs.ApproachDRS spectra were acquired from different tissue types on multiple locations in 20 freshly excised sarcoma specimens. A k-nearest neighbors classification model was trained to predict the tissue types of the measured locations, using binary and multiclass approaches.ResultsTumor tissue could be distinguished from healthy tissue with a classification accuracy of 0.90, sensitivity of 0.88, and specificity of 0.93 when well-differentiated liposarcomas were included. Excluding this subtype, the classification performance increased to an accuracy of 0.93, sensitivity of 0.94, and specificity of 0.93. The developed model showed a consistent performance over different histological subtypes and tumor locations.ConclusionsAutomatic tissue discrimination using DRS enables real-time intra-operative guidance, contributing to more accurate STS resections.
Optical technologies are widely used for tissue sensing purposes, however maneuvering conventional probe designs with flat-tipped fibers in narrow spaces can be challenging, such as in pelvic colorectal cancer surgery. In this study, a compact side-firing fiber probe was developed for tissue discrimination during colorectal cancer surgery using diffuse reflectance spectroscopy. The light behavior was compared to flat-tipped fibers using both Monte Carlo simulations and the tissue classification performance was examined using freshly excised colorectal cancer specimens. Using the developed probe and classification algorithm, we achieved an accuracy of 0.92 for the discrimination of colorectal tumor tissue from healthy tissue.
Achieving adequate resection margins during breast-conserving surgery is crucial for minimizing the risk of tumor recurrence in patients with breast cancer but remains challenging due to the lack of intraoperative feedback. Here, we evaluated the use of hyperspectral imaging to discriminate healthy tissue from tumor tissue in lumpectomy specimens of 121 patients. A dataset on tissue slices was used to develop and evaluate three convolutional neural networks. Subsequently, these networks were fine-tuned with lumpectomy data to predict the tissue percentages on the lumpectomy resection surface. We achieved a MCC of 0.92 on the tissue slices and an RMSE of 9% on the lumpectomy resection surface.
Diffuse reflectance spectroscopy (DRS) has already been successfully used for tissue discrimination during colorectal cancer surgery. In clinical practice, however, tissue often consists of several layers. Therefore, a novel multi-output convolutional neural network (CNN) was designed to classify multiple layers of colorectal cancer tissue simultaneously. DRS data was acquired with an array of six fibers with different fiber distances to sample at multiple depths. After training a 2D CNN with the DRS data as input, the first, second, and third tissue layers could be classified with mean accuracies of 0.90, 0.71, and 0.62, respectively.
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.
Hyperspectral imaging has emerged as a promising diagnostic technique in the medical field. However, reflection from a sample often consists of a combination of surface reflection (also known as glare) and volume reflection. In this study, we propose a method to separate these two by illuminating the samples from three different angles and using a least squares optimization. This widely applicable method showed an adequate distinction between surface and volume reflectance in optical phantoms as well as in breast tissue samples.
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