Toward complete oral cavity cancer resection using a handheld diffuse reflectance spectroscopy probe

Susan G. Brouwer de Koning (Corresponding author), Elisabeth J.M. Baltussen, M. Baris Karakullukcu, Behdad Dashtbozorg, Laura A. Smit, Richard Dirven, Benno H.W. Hendriks, Henricus J.C.M. Sterenborg, Theo J.M. Ruers

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This ex-vivo study evaluates the feasibility of diffuse reflectance spectroscopy (DRS) for discriminating tumor from healthy tissue, with the aim to develop a technology that can assess resection margins for the presence of tumor cells during oral cavity cancer surgery. Diffuse reflectance spectra were acquired on fresh surgical specimens from 28 patients with oral cavity squamous cell carcinoma. The spectra (400 to 1600 nm) were detected after illuminating tissue with a source fiber at 0.3-, 0.7-, 1.0-, and 2.0-mm distances from a detection fiber, obtaining spectral information from different sampling depths. The spectra were correlated with histopathology. A total of 76 spectra were obtained from tumor tissue and 110 spectra from healthy muscle tissue. The first- A nd second-order derivatives of the spectra were calculated and a classification algorithm was developed using fivefold cross validation with a linear support vector machine. The best results were obtained by the reflectance measured with a 1-mm source-detector distance (sensitivity, specificity, and accuracy are 89%, 82%, and 86%, respectively). DRS can accurately discriminate tumor from healthy tissue in an ex-vivo setting using a 1-mm source-detector distance. Accurate validation methods are warranted for larger sampling depths to allow for guidance during oral cavity cancer excision.

Original languageEnglish
Article number121611
Number of pages8
JournalJournal of Biomedical Optics
Issue number12
Publication statusPublished - 1 Dec 2018


  • Diffuse Reflectance Spectroscopy
  • Linear support vector machine
  • Machine Learning
  • Oral Cavity Cancer
  • Resection Margin Assessment
  • Tissue Recognition
  • Carcinoma, Squamous Cell/diagnostic imaging
  • Reproducibility of Results
  • Prognosis
  • Mouth Neoplasms/diagnostic imaging
  • Humans
  • Linear Models
  • Support Vector Machine
  • Netherlands
  • Algorithms
  • Image Processing, Computer-Assisted
  • Spectroscopy, Near-Infrared
  • Sensitivity and Specificity
  • Spectrophotometry
  • Surgical Oncology/methods


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