Abstract
Preoperative prediction of soft tissue sarcoma (STS) grade is important for treatment decisions. Therefore, formulation an STS grade model is strongly needed. To develop and test an magnetic resonance imaging (MRI)-based radiomics nomogram for predicting the grade of STS (low-grade vs. high grade). 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). Unenhanced T1-weighted (T1WI) and fat-suppressed T2-weighted images (FS-T2WI) were acquired at 1.5T and 3.0T. 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. 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. The proposed noninvasive MRI-based radiomics models showed good performance in differentiating low-grade from high-grade STSs. 3 TECHNICAL EFFICACY STAGE: 2.
Highlights
Preoperative prediction of the soft tissue sarcomas (STSs) grade is important for treatment decisions
The remaining factors showed no significant difference between the two groups in either the training set or external validation set
The radiomics nomogram, which combined the RS-Combined model with clinical factors, successfully distinguished high-grade from low-grade STS with the highest performance and exhibited good calibration in both sets, indicating the incremental value of the nomogram and showing that it can be a promising tool for clinical strategy adjustment
Summary
Preoperative prediction of the soft tissue sarcomas (STSs) grade is important for treatment decisions. Soft tissue sarcomas (STSs) are a heterogeneous group of malignant mesenchymal neoplasms [1]. They account for 1% of all tumors and have a high mortality rate of about 50% [2]. According to the American Cancer Society, the incidence of STS was approximately 8790 cases each year in the United States in 2017 [2]. This is comparable with the annual occurrence of esophageal cancer (17,500 cases) and cervical cancer (12,000 cases) [3]; the rarity of STS may be overestimated. Identification of prognostic biomarkers is needed to stratify high-risk patients and improve the prognosis
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