Quantitative spectral reflectance imaging device for intraoperative breast tumor margin assessment

Annu Int Conf IEEE Eng Med Biol Soc. 2009:2009:6554-6. doi: 10.1109/IEMBS.2009.5334501.

Abstract

Diffuse reflectance spectroscopy of tissue allows quantification of underlying physiological and morphological changes associated with cancer, provided that the absorption and scattering properties of the tissue can be effectively decoupled. A particular application of interest for tissue reflectance spectroscopy in the UV-VIS is intraoperative detection of residual cancer at the margins of excised breast tumors, which could prevent costly and unnecessary repeat surgeries. Our multi-disciplinary group has developed an optical imaging device, which employs a model-based algorithm for quantification of tissue optical properties, and is capable of surveying the entire specimen surface down to a depth of 1-2 mm, all within a short time as required for intraoperative use. In an ongoing IRB-approved study, reflectance spectral images were acquired from 55 margins in 48 patients. Conversion of the spectral images to quantitative tissue parameter maps was facilitated by a fast scalable inverse Monte-Carlo model. Data from margin parameter images were reduced to image-descriptive scalar values and compared to gold-standard margin pathology. Use of a decision-tree based classification algorithm on the two most significant optical parameters resulted in a sensitivity of 79% and specificity of 67% for detection of residual tumor of all pathologic variants, with an 89% sensitivity for ductal carcinoma in situ alone. Preliminary data from this ongoing clinical study suggest that this technology could significantly reduce the number of unnecessary repeat breast conserving surgeries annually.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Breast Neoplasms / pathology*
  • Breast Neoplasms / surgery*
  • Carcinoma, Ductal, Breast / pathology
  • Carcinoma, Ductal, Breast / surgery
  • Diagnostic Imaging / methods*
  • Female
  • Humans
  • Intraoperative Care / methods*
  • Spectrum Analysis / methods*