Favorable tumor response (pathologic necrosis [PN] >90-95%) has been associated with improved outcome in soft tissue sarcoma (STS). STS is a diverse disease with variable response to radiation (RT), therefore tools such as radiomics may improve our prediction of tumor radiosensitivity. Radiomic habitats are analyses of overlaid images, providing insight on the underlying innate biology. We hypothesize that utilizing pretreatment MRI habitat analysis can predict for favorable tumor response in STS. We identified a total of 432 non-metastatic sarcoma patients that were treated with neoadjuvant radiation (50Gy/25 fractions) followed by surgery, from 2001 to 2019. Patients with a pre-RT MRI were included, gross volume was manually segmented on T1-post contrast and T2 STIR sequences. The sequences were dichotomized by signal intensity and each region designated into 1 of 4 habitats (e.g. T1 high/low, T2 high/low), and 154 habitat features were extracted. The cohort was split 2:1 into training and validation subsets balanced by PN, clinical tumor T (cT) and N stage (cN), receiving induction chemotherapy, and age. Feature reduction and machine learning was utilized to identify habitat features predictive of PN rates >90% or >95% at the time of surgery. Overall, patients eligible for analysis (n = 97) had a median age of 63 and tumor size of 11 cm. They most commonly consisted of undifferentiated pleomorphic sarcoma (40%), extremity (80%), and no prior chemotherapy (87%). An 8-feature model, 4 radiomic and 4 clinical (recurrent disease, age, cT stage, cN stage), predicted for PN>90% (training: 72%, ROC 0.746; validation: 65%, ROC: 0.713). Evaluating patients treated with neoadjuvant RT alone (n = 84), a second model with 5 radiomic and 3 clinical features (prior RT, cT and cN stage), had the highest prediction for PN>95% (training: 78%, ROC 0.721; validation: 72%, ROC 0.600). In both the overall cohort and RT only subset, the diversity in the number of habitat volumes within the tumor predicted for tumor response (OR 7.71 and 3.31). Models incorporating pretreatment MRI-habitats and clinical features can be used to predict radiation response in STS. Radiomic features suggestive of habitat diversity within a tumor appear to be associated with radiation response, and highlight the importance of intratumoral heterogeneity when evaluating radiosensitivity. Prospective studies utilizing this model to predict radiation response may identify patients that could benefit from treatment escalation.
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