4.7 Article

A quantitative microscopic approach to predict local recurrence based on in vivo intraoperative imaging of sarcoma tumor margins

Journal

INTERNATIONAL JOURNAL OF CANCER
Volume 137, Issue 10, Pages 2403-2412

Publisher

WILEY
DOI: 10.1002/ijc.29611

Keywords

optical fluorescence imaging; intraoperative imaging; soft tissue sarcoma; image analysis; logistic models

Categories

Funding

  1. Department of Defense [W81XWH-09-1-0410]
  2. NIH [1R01EB01157]
  3. Direct For Computer & Info Scie & Enginr
  4. Division of Computing and Communication Foundations [1418976] Funding Source: National Science Foundation

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The goal of resection of soft tissue sarcomas located in the extremity is to preserve limb function while completely excising the tumor with a margin of normal tissue. With surgery alone, one-third of patients with soft tissue sarcoma of the extremity will have local recurrence due to microscopic residual disease in the tumor bed. Currently, a limited number of intraoperative pathology-based techniques are used to assess margin status; however, few have been widely adopted due to sampling error and time constraints. To aid in intraoperative diagnosis, we developed a quantitative optical microscopy toolbox, which includes acriflavine staining, fluorescence microscopy, and analytic techniques called sparse component analysis and circle transform to yield quantitative diagnosis of tumor margins. A series of variables were quantified from images of resected primary sarcomas and used to optimize a multivariate model. The sensitivity and specificity for differentiating positive from negative ex vivo resected tumor margins was 82 and 75%. The utility of this approach was tested by imaging the in vivo tumor cavities from 34 mice after resection of a sarcoma with local recurrence as a bench mark. When applied prospectively to images from the tumor cavity, the sensitivity and specificity for differentiating local recurrence was 78 and 82%. For comparison, if pathology was used to predict local recurrence in this data set, it would achieve a sensitivity of 29% and a specificity of 71%. These results indicate a robust approach for detecting microscopic residual disease, which is an effective predictor of local recurrence.

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