Objective. To determine if osteosarcoma (OS) and Ewing sarcoma (EWS) of the pelvis based on MRI can be differentiated using radiomic analysis. Materials and Methods. In this study, 3.0 T magnetic resonance (MR) data of 66 patients (40 males and 26 females, mean age 27.6 ± 13.9 years) with pathologically confirmed OS or EWS of the pelvis (35 with OS and 31 with EWS) taken from April 2013 to December 2017 were retrospectively reviewed. T2‐weighted fat‐saturated (T2‐FS) and contrast‐enhanced T1‐weighted (CET1) images were manually segmented, and imaging features were extracted. Independent‐sample t‐test, Spearman’s test, and the least absolute shrinkage and selection operator (LASSO) method were used to select the most useful features from the original data set. The performance of radiomic analysis was investigated by the area under the receiver operating characteristic (ROC) curve (AUC) analysis. Results. 385 initial features were extracted from T2‐FS and CET1 MR data. Nine features from T2‐FS and 7 features from CET1 were selected by using the LASSO method. The radiomic analysis to differentiate OS and EWS of the pelvis based on T2‐FS and CET1 images using the aforementioned selected features achieved AUC values of 0.881 (95% confidence interval (CI): 0.799–0.963) and 0.765 (95% CI: 0.652–0.878), respectively. Conclusion. Radiomic analysis showed potential in differentiating OS from EWS of the pelvis, in which T2‐FS demonstrated better diagnostic value. To differentiate OS from EWS of the pelvis using our multiparametric MRI‐based radiomic analysis could preoperatively improve diagnostic accuracy and greatly contribute to therapy planning.

Objective: To develop and validate clinical-radiomics nomograms based on three-dimensional CT and multiparametric MRI (mpMRI) for pre-operative differentiation of sacral chordoma (SC) and sacral giant cell tumor (SGCT). Methods: A total of 83 SC and 54 SGCT patients diagnosed through surgical pathology were retrospectively analyzed. We built six models based on CT, CT enhancement (CTE), T 1 weighted, T 2 weighted, diffusion-weighted imaging (DWI), and contrast-enhanced T 1 weighted features, two radiomics nomograms and two clinical-radiomics nomograms combined radiomics mixed features with clinical data. The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) analysis were used to assess the performance of the models. Results: SC and SGCT presented significant differences in terms of age, sex, and tumor location (tage = 9.00, χ2 sex = 10.86, χ2 location = 26.20; p < 0.01). For individual scan, the radiomics model based on diffusion-weighted imaging features yielded the highest AUC of 0.889 and ACC of 0.885, followed by CT (AUC = 0.857; ACC = 0.846) and CT enhancement (AUC = 0.833; ACC = 0.769). For the combined features, the radiomics model based on mixed CT features exhibited a better AUC of 0.942 and ACC of 0.880, whereas mixed MRI features achieved a lower performance than the individual scan. The clinical-radiomics nomogram based on combined CT features achieved the highest AUC of 0.948 and ACC of 0.920. Conclusions: The radiomics model based on CT and multiparametricMRI present a certain predictive value in distinguishing SC and SGCT, which can be used for auxiliary diagnosis before operation. The clinical-radiomics nomograms performed better than radiomics nomograms. Advances in knowledge: Clinical-radiomics nomograms based on CT and mpMRI features can be used for preoperative differentiation of SC and SGCT.

Objectives: This study aims to assess the performance of radiomics approaches based on 3D computed tomography (CT), clinical and semantic features in predicting the pathological classification of thymic epithelial tumors (TETs).Methods: A total of 190 patients who underwent surgical resection and had pathologically confirmed TETs were enrolled in this retrospective study. All patients underwent non-contrast-enhanced CT (NECT) scans and contrast-enhanced CT (CECT) scans before treatment. A total of 396 hand-crafted radiomics features of each patient were extracted from the volume of interest in NECT and CECT images. We compared three clinical features and six semantic features (observed radiological traits) between patients with TETs. Two triple-classification radiomics models (RMs), two corresponding clinical RMs, and two corresponding clinical-semantic RMs were built to identify the types of the TETs. The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) were useful to evaluate the different models.Results: Of the 190 patients, 83 had low-risk thymoma, 58 had high-risk thymoma, and 49 had thymic carcinoma. Clinical features (Age) and semantic features (mediastinal fat infiltration, mediastinal lymph node enlargement, and pleural effusion) were significantly different among the groups(P < 0.001). In the validation set, the NECT-based clinical RM (AUC = 0.770 for low-risk thymoma, 0.689 for high-risk thymoma, and 0.783 for thymic carcinoma; ACC = 0.569) performed better than the CECT-based clinical-semantic RM (AUC = 0.785 for low-risk thymoma, 0.576 for high-risk thymoma, and 0.774 for thymic carcinoma; ACC = 0.483).Conclusions: NECT-based and CECT-based RMs may provide a non-invasive method to distinguish low-risk thymoma, high-risk thymoma, and thymic carcinoma, and NECT-based RMs performed better.Advances in Knowledge: Radiomics models may be used for the preoperative prediction of the pathological classification of TETs.