Magnetic Resonance Imaging‐Based Radiomics Nomogram for Prediction of the Histopathological Grade of Soft Tissue Sarcomas: A Two‐Center Study. Issue 6 (18th February 2021)

• Record Type: Journal Article
• Authors: Yan, Ruixin; Hao, Dapeng; Li, Jie; Liu, Jihua; Hou, Feng; Chen, Haisong; Duan, Lisha; Huang, Chencui; Wang, Hexiang; Yu, Tengbo
• Is Part Of: Journal of magnetic resonance imaging. Volume 53:Issue 6(2021)
• Page Start: 1683
• Page End: 1696
• Publication Date: 2021-02-18
• Subjects: magnetic resonance imaging -- radiomics -- soft tissue sarcomas -- tumor grading; Magnetic resonance imaging -- Periodicals; 616
• DOI: 10.1002/jmri.27532
• Languages: English
• ISSNs: 1053-1807

Abstract

Abstract : 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