Tissue Classification of Breast Cancer by Hyperspectral Unmixing

Lynn Jade S. Jong*, Anouk L. Post, Dinusha Veluponnar, Freija Geldof, Henricus J.C.M. Sterenborg, Theo J.M. Ruers, Behdad Dashtbozorg

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)
43 Downloads (Pure)

Abstract

(1) Background: Assessing the resection margins during breast-conserving surgery is an important clinical need to minimize the risk of recurrent breast cancer. However, currently there is no technique that can provide real-time feedback to aid surgeons in the margin assessment. Hyperspectral imaging has the potential to overcome this problem. To classify resection margins with this technique, a tissue discrimination model should be developed, which requires a dataset with accurate ground-truth labels. However, establishing such a dataset for resection specimens is difficult. (2) Methods: In this study, we therefore propose a novel approach based on hyperspectral unmixing to determine which pixels within hyperspectral images should be assigned to the ground-truth labels from histopathology. Subsequently, we use this hyperspectral-unmixing-based approach to develop a tissue discrimination model on the presence of tumor tissue within the resection margins of ex vivo breast lumpectomy specimens. (3) Results: In total, 372 measured locations were included on the lumpectomy resection surface of 189 patients. We achieved a sensitivity of 0.94, specificity of 0.85, accuracy of 0.87, Matthew’s correlation coefficient of 0.71, and area under the curve of 0.92. (4) Conclusion: Using this hyperspectral-unmixing-based approach, we demonstrated that the measured locations with hyperspectral imaging on the resection surface of lumpectomy specimens could be classified with excellent performance.

Original languageEnglish
Article number2679
JournalCancers
Volume15
Issue number10
DOIs
Publication statusPublished - May 2023

Keywords

  • breast tissue
  • breast-conserving surgery
  • hyperspectral imaging
  • hyperspectral unmixing
  • resection margin assessment
  • tissue classification

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