Resection margin assessment in breast lumpectomy specimens using deep learning-based hyperspectral imaging (Conference Presentation)
Lynn-Jade S. Jong, Naomi de Kruif, Freija Geldof, Dinusha Veluponnar, Joyce Sanders, Marie-Jeanne T. F. D. Vrancken Peeters, Frederieke H van Duijnhoven, Henricus J. C. M. Sterenborg, Behdad Dashtbozorg, Theo J. M. Ruers · 2023
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.