Toward the use of diffuse reflection spectroscopy for intra-operative tissue discrimination during sarcoma surgery

Freija Geldof*, Yvonne M. Schrage, Winan J. van Houdt, 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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Significance: Accurately 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. Aim: We evaluate the performance of diffuse reflectance spectroscopy (DRS) to distinguish tumor tissue from healthy tissue in STSs. Approach: DRS 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. Results: Tumor 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. Conclusions: Automatic tissue discrimination using DRS enables real-time intraoperative guidance, contributing to more accurate STS resections.

Original languageEnglish
Article number027001
JournalJournal of biomedical optics
Issue number2
Publication statusPublished - 15 Feb 2024


  • UT-Gold-D
  • machine learning
  • margin assessment
  • sarcoma surgery
  • tissue classification
  • diffuse reflectance spectroscopy


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