Prediction of treatment response and metastatic disease in soft tissue sarcoma

Hamidreza Farhidzadeh; Mu Zhou; Dmitry B. Goldgof; Lawrence O. Hall; Meera. Raghavan; Robert A. Gatenby

doi:10.1117/12.2043792

20 March 2014 Prediction of treatment response and metastatic disease in soft tissue sarcoma

Hamidreza Farhidzadeh, Mu Zhou, Dmitry B. Goldgof, Lawrence O. Hall, Meera. Raghavan, Robert A. Gatenby

Proceedings Volume 9035, Medical Imaging 2014: Computer-Aided Diagnosis; 903518 (2014) https://doi.org/10.1117/12.2043792
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

Soft tissue sarcomas (STS) are a heterogenous group of malignant tumors comprised of more than 50 histologic subtypes. Based on spatial variations of the tumor, predictions of the development of necrosis in response to therapy as well as eventual progression to metastatic disease are made. Optimization of treatment, as well as management of therapy-related side effects, may be improved using progression information earlier in the course of therapy. Multimodality pre- and post-gadolinium enhanced magnetic resonance images (MRI) were taken before and after treatment for 30 patients. Regional variations in the tumor bed were measured quantitatively. The voxel values from the tumor region were used as features and a fuzzy clustering algorithm was used to segment the tumor into three spatial regions. The regions were given labels of high, intermediate and low based on the average signal intensity of pixels from the post-contrast T1 modality. These spatially distinct regions were viewed as essential meta-features to predict the response of the tumor to therapy based on necrosis (dead tissue in tumor bed) and metastatic disease (spread of tumor to sites other than primary). The best feature was the difference in the number of pixels in the highest intensity regions of tumors before and after treatment. This enabled prediction of patients with metastatic disease and lack of positive treatment response (i.e. less necrosis). The best accuracy, 73.33%, was achieved by a Support Vector Machine in a leave-one-out cross validation on 30 cases predicting necrosis < 90% post treatment and metastasis.

Citation Download Citation

Hamidreza Farhidzadeh, Mu Zhou, Dmitry B. Goldgof, Lawrence O. Hall, Meera. Raghavan, and Robert A. Gatenby "Prediction of treatment response and metastatic disease in soft tissue sarcoma", Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 903518 (20 March 2014); https://doi.org/10.1117/12.2043792

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available