Discriminating healthy from tumor tissue in breast lumpectomy specimens using deep learning-based hyperspectral imaging

Lynn Jade S. Jong, Naomi de Kruif, Freija Geldof, Dinusha Veluponnar, Joyce Sanders, Marie Jeanne T.F.D. Vrancken Peeters, Frederieke V.A.N. Duijnhoven, Henricus J.C.M. Sterenborg, Behdad Dashtbozorg*, Theo J.M. Ruers

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

Achieving an adequate resection margin during breast-conserving surgery 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. We first used a dataset obtained on tissue slices to develop and evaluate three convolutional neural networks. Second, we fine-tuned the networks with lumpectomy data to predict the tissue percentages of the lumpectomy resection surface. A MCC of 0.92 was achieved on the tissue slices and an RMSE of 9% on the lumpectomy resection surface. This shows the potential of hyperspectral imaging to classify the resection margins of lumpectomy specimens.

Original languageEnglish
Pages (from-to)2581-2604
Number of pages24
JournalBiomedical optics express
Volume13
Issue number5
DOIs
Publication statusPublished - 1 May 2022

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