A Quantitative Imaging-based Biomarker for Assessment of Therapy Response in Soft Tissue Sarcomas by Differential Volume Estimation of Viable and Non-viable Tumor Fractions

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Anand K. Singh, M.D.,  Massachusetts General Hospital
Recipient of the: $25,000 research award

The rapid developments in imaging techniques and action of newer chemotherapeutic drugs have highlighted the limitations of response evaluation criteria in solid tumors (RECIST) for assessing treatment response in soft tissue sarcoma. Some chemotherapeutic agents may induce more tumor necrosis compared to another, causing enlargement of the total tumor size and thus leading to false positive interpretations of disease progression on RECIST of an otherwise stable or regressed disease state. Newer imaging techniques like tumor perfusion and positron emission tomography (PET) need standardization with regard to apparent diffusion coefficient values and tighter control on false positive detections respectively with added disadvantages of extra costs and scanning. Revisions in tumor response assessment criteria’s are therefore gaining importance. In our preliminary study, excellent correlation was observed between the proposed MRI volumetry technique and histopathology for estimation of non-viable tumor fraction in treated and excised tumors. We thus hypothesize that non viable tissue fraction of soft tissue sarcomas can be accurately quantified by performing 3D segmentations on MRI datasets and such bio-estimates will serve as a better predictor of therapy response compared to existing RECIST criteria. We will obtain interval change in viable and non-viable tumor fractions by applying semi-automated 3D segmentation techniques on axial slices of contrast-enhanced pre-treatment and post-treatment T1weighted MRI datasets and compare them with the response obtained by RECIST criteria 1.0. We will also estimate one-dimensional tumor measurements on both MRI time-point datasets and assess therapy response based on RECIST criteria. Finally, we will compare statistical estimates of two methods generated by Kaplan-Meier survival curves for progression-free disease and overall (long-term) survival time. After successful testing of our hypothesis, this cost-effective and feasible innovation may have significant positive impact in clinical decision-making for treatment of sarcomas where similar principles can be applied on wider latitude for other body tumors.