Prognostic Assessment in High-Grade Soft-Tissue Sarcoma Patients: A Comparison of Semantic Image Analysis and Radiomics
نویسندگان
چکیده
Background: In patients with soft-tissue sarcomas of the extremities, treatment decision is currently regularly based on tumor grading and size. The imaging-based analysis may pose an alternative way to stratify patients’ risk. this work, we compared value MRI-based radiomics expert-derived semantic imaging features for prediction overall survival (OS). Methods: Fat-saturated T2-weighted sequences (T2FS) contrast-enhanced T1-weighted fat-saturated (T1FSGd) were collected from two independent retrospective cohorts (training: 108 patients; testing: 71 patients). After preprocessing, 105 radiomic extracted. Semantic determined by three radiologists. Three machine learning techniques (elastic net regression (ENR), least absolute shrinkage selection operator, random forest) predict OS. Results: ENR models achieved best predictive performance. Histologies clinical staging differed significantly between both cohorts. prognostic model a performance C-index 0.58 within test set. This was worse system (C-index: 0.61) (C-indices: T1FSGd: 0.64, T2FS: 0.63). Both significant patient stratification. Conclusions: T2FS T1FSGd-based outperformed assessment.
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ژورنال
عنوان ژورنال: Cancers
سال: 2021
ISSN: ['2072-6694']
DOI: https://doi.org/10.3390/cancers13081929