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6 March 2023 Resection margin assessment in breast lumpectomy specimens using deep learning-based hyperspectral imaging
Lynn-Jade Jong, Naomi de Kruif, Freija Geldof, Dinusha Veluponnar, Joyce Sanders, Marie-Jeanne Vrancken Peeters, Frederieke van Duijnhoven, Henricus Sterenborg, Behdad Dashtbozorg, Theo Ruers
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Proceedings Volume PC12368, Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XXI; PC123680F (2023) https://doi.org/10.1117/12.2649003
Event: SPIE BiOS, 2023, San Francisco, California, United States
Abstract
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.
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Lynn-Jade Jong, Naomi de Kruif, Freija Geldof, Dinusha Veluponnar, Joyce Sanders, Marie-Jeanne Vrancken Peeters, Frederieke van Duijnhoven, Henricus Sterenborg, Behdad Dashtbozorg, and Theo Ruers "Resection margin assessment in breast lumpectomy specimens using deep learning-based hyperspectral imaging", Proc. SPIE PC12368, Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XXI, PC123680F (6 March 2023); https://doi.org/10.1117/12.2649003
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KEYWORDS
Tissues
Breast
Hyperspectral imaging
Tumors
Breast cancer
Convolutional neural networks
Natural surfaces
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