Background Preoperative prediction of soft tissue sarcoma (STS) grade is important for treatment decisions. Therefore, formulation an STS grade model is strongly needed. Purpose To develop and test an magnetic resonance imaging (MRI)‐based radiomics nomogram for predicting the grade of STS (low‐grade vs. high grade). Study Type Retrospective Population One hundred and eighty patients with STS confirmed by pathologic results at two independent institutions were enrolled (training set, N = 109; external validation set, N = 71). Field Strength/Sequence Unenhanced T1‐weighted (T1WI) and fat‐suppressed T2‐weighted images (FS‐T2WI) were acquired at 1.5 T and 3.0 T. Assessment Clinical‐MRI characteristics included age, gender, tumor‐node‐metastasis (TNM) stage, American Joint Committee on Cancer (AJCC) stage, progression‐free survival (PFS), and MRI morphological features (ie, margin). Radiomics feature extraction were performed on T1WI and FS‐T2WI images by minimum redundancy maximum relevance (MRMR) method and least absolute shrinkage and selection operator (LASSO) algorithm. The selected features constructed three radiomics signatures models (RS‐T1, RS‐FST2, and RS‐Combined). Univariate and multivariate logistic regression analysis were applied for screening significant risk factors. Radiomics nomogram was constructed by incorporating the radiomics signature and risk factors. Statistical Tests Clinical‐MRI characteristics were performed by a univariate analysis. Model performances (discrimination, calibration, and clinical usefulness) were validated in the external validation set. The RS‐T1 model, RS‐FST2 model, and RS‐Combined model had an area under curves (AUCs) of 0.645, 0.641, and 0.829, respectively, in the external validation set. The radiomics nomogram, incorporating significant risk factors and the RS‐Combined model had AUCs of 0.916 (95%CI, 0.866–0.966, training set) and 0.879 (95%CI, 0.791–0.967, external validation set), and demonstrated good calibration and good clinical utility. Data Conclusion The proposed noninvasive MRI‐based radiomics models showed good performance in differentiating low‐grade from high‐grade STSs. Level of Evidence 3 Technical Efficacy Stage 2
Background: Preoperative prediction of the soft tissue sarcomas (STSs) grade is important for treatment decisions. To preoperatively distinguish low-grade (grades I and II) and high-grade (grade III) STSs, we developed and validated the performance of a magnetic resonance imaging (MRI)-based radiomics nomogram.Methods: Patients with an STS based on the French Federation of Cancer Centers Sarcoma Group grading system at two independent institutions were enrolled (training set, n = 109; external validation set, n = 71). The minimum redundancy maximum relevance method and least absolute shrinkage and selection operator logistic regression were used to process feature selection and radiomics signature development. Three radiomics signature models were constructed based on T1-weighted imaging (RS-T1 model) and fat-suppressed T2-weighted imaging sequences (RS-FST2 model) and their combination (RS-Combined model). Model performance (discrimination capability, calibration curve, and clinical usefulness) was evaluated in the external validation set. Results: The RS-T1 model, RS-FST2 model, and RS-Combined model achieved predictive abilities with area under the receiver operating characteristic curves (AUCs) of 0.645, 0.641, and 0.829, respectively, in the external validation set. The nomogram, incorporating significant clinical factors and the RS-Combined model, showed extremely high predictive ability in the training set and external validation set with AUCs of 0.916 (95% confidence interval, 0.866–0.966) and 0.879 (0.791–0.967), respectively. The nomogram achieved significant patient stratification.Conclusions: The proposed noninvasive MRI-based radiomics nomogram shows superior predictive performance in differentiating low-grade from high-grade STS.
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