Share Email Print
cover

Proceedings Paper

Prediction of treatment outcome in soft tissue sarcoma based on radiologically defined habitats
Author(s): Hamidreza Farhidzadeh; Baishali Chaudhury; Mu Zhou; Dmitry B. Goldgof; Lawrence O. Hall; Robert A. Gatenby; Robert J. Gillies; Meera Raghavan
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Soft tissue sarcomas are malignant tumors which develop from tissues like fat, muscle, nerves, fibrous tissue or blood vessels. They are challenging to physicians because of their relative infrequency and diverse outcomes, which have hindered development of new therapeutic agents. Additionally, assessing imaging response of these tumors to therapy is also difficult because of their heterogeneous appearance on magnetic resonance imaging (MRI). In this paper, we assessed standard of care MRI sequences performed before and after treatment using 36 patients with soft tissue sarcoma. Tumor tissue was identified by manually drawing a mask on contrast enhanced images. The Otsu segmentation method was applied to segment tumor tissue into low and high signal intensity regions on both T1 post-contrast and T2 without contrast images. This resulted in four distinctive subregions or “habitats.” The features used to predict metastatic tumors and necrosis included the ratio of habitat size to whole tumor size and components of 2D intensity histograms. Individual cases were correctly classified as metastatic or non-metastatic disease with 80.55% accuracy and for necrosis ≥ 90 or necrosis <90 with 75.75% accuracy by using meta-classifiers which contained feature selectors and classifiers.

Paper Details

Date Published: 20 March 2015
PDF: 5 pages
Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94141U (20 March 2015); doi: 10.1117/12.2082324
Show Author Affiliations
Hamidreza Farhidzadeh, Univ. of South Florida (United States)
Baishali Chaudhury, Univ. of South Florida (United States)
Mu Zhou, Univ. of South Florida (United States)
Dmitry B. Goldgof, Univ. of South Florida (United States)
Lawrence O. Hall, Univ. of South Florida (United States)
Robert A. Gatenby, H. Lee Moffitt Cancer Ctr. & Research Institute (United States)
Robert J. Gillies, H. Lee Moffitt Cancer Ctr. & Research Institute (United States)
Meera Raghavan, H. Lee Moffitt Cancer Ctr. & Research Institute (United States)


Published in SPIE Proceedings Vol. 9414:
Medical Imaging 2015: Computer-Aided Diagnosis
Lubomir M. Hadjiiski; Georgia D. Tourassi, Editor(s)

© SPIE. Terms of Use
Back to Top