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A quantitative microscopic approach to predict local recurrence based on in vivo intraoperative imaging of sarcoma tumor margins
Author(s) -
Mueller Jenna L.,
Fu Henry L.,
Mito Jeffrey K.,
Whitley Melodi J.,
Chitalia Rhea,
Erkanli Alaattin,
Dodd Leslie,
Cardona Diana M.,
Geradts Joseph,
Willett Rebecca M.,
Kirsch David G.,
Ramanujam Nimmi
Publication year - 2015
Publication title -
international journal of cancer
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.475
H-Index - 234
eISSN - 1097-0215
pISSN - 0020-7136
DOI - 10.1002/ijc.29611
Subject(s) - sarcoma , medicine , surgical margin , soft tissue sarcoma , in vivo , soft tissue , ex vivo , radiology , pathology , sampling (signal processing) , resection , surgery , biology , computer science , microbiology and biotechnology , filter (signal processing) , computer vision
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.