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

Publication ,  Journal Article
Mueller, JL; Fu, HL; Mito, JK; Whitley, MJ; Chitalia, R; Erkanli, A; Dodd, L; Cardona, DM; Geradts, J; Willett, RM; Kirsch, DG; Ramanujam, N
Published in: Int J Cancer
November 15, 2015

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.

Duke Scholars

Published In

Int J Cancer

DOI

EISSN

1097-0215

Publication Date

November 15, 2015

Volume

137

Issue

10

Start / End Page

2403 / 2412

Location

United States

Related Subject Headings

  • Sensitivity and Specificity
  • Sarcoma
  • Prospective Studies
  • Oncology & Carcinogenesis
  • Neoplasm, Residual
  • Mice
  • Intraoperative Care
  • Image Processing, Computer-Assisted
  • Humans
  • Diagnostic Imaging
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Mueller, J. L., Fu, H. L., Mito, J. K., Whitley, M. J., Chitalia, R., Erkanli, A., … Ramanujam, N. (2015). A quantitative microscopic approach to predict local recurrence based on in vivo intraoperative imaging of sarcoma tumor margins. Int J Cancer, 137(10), 2403–2412. https://doi.org/10.1002/ijc.29611
Mueller, Jenna L., Henry L. Fu, Jeffrey K. Mito, Melodi J. Whitley, Rhea Chitalia, Alaattin Erkanli, Leslie Dodd, et al. “A quantitative microscopic approach to predict local recurrence based on in vivo intraoperative imaging of sarcoma tumor margins.Int J Cancer 137, no. 10 (November 15, 2015): 2403–12. https://doi.org/10.1002/ijc.29611.
Mueller JL, Fu HL, Mito JK, Whitley MJ, Chitalia R, Erkanli A, et al. A quantitative microscopic approach to predict local recurrence based on in vivo intraoperative imaging of sarcoma tumor margins. Int J Cancer. 2015 Nov 15;137(10):2403–12.
Mueller, Jenna L., et al. “A quantitative microscopic approach to predict local recurrence based on in vivo intraoperative imaging of sarcoma tumor margins.Int J Cancer, vol. 137, no. 10, Nov. 2015, pp. 2403–12. Pubmed, doi:10.1002/ijc.29611.
Mueller JL, Fu HL, Mito JK, Whitley MJ, Chitalia R, Erkanli A, Dodd L, Cardona DM, Geradts J, Willett RM, Kirsch DG, Ramanujam N. A quantitative microscopic approach to predict local recurrence based on in vivo intraoperative imaging of sarcoma tumor margins. Int J Cancer. 2015 Nov 15;137(10):2403–2412.
Journal cover image

Published In

Int J Cancer

DOI

EISSN

1097-0215

Publication Date

November 15, 2015

Volume

137

Issue

10

Start / End Page

2403 / 2412

Location

United States

Related Subject Headings

  • Sensitivity and Specificity
  • Sarcoma
  • Prospective Studies
  • Oncology & Carcinogenesis
  • Neoplasm, Residual
  • Mice
  • Intraoperative Care
  • Image Processing, Computer-Assisted
  • Humans
  • Diagnostic Imaging