Background: To develop a multi-modality MRI-based radiomics nomogram for predicting the lung metastasis (LM) in soft-tissue sarcoma (STS).
Methods: We enrolled 122 patients who clinicopathologically confirmed STS from our hospital to form a primary cohort. Thirty-two patients from another hospital were included as an external validation cohort. All patients underwent pretreatment T1-weighted contrast-enhanced (T1-CE) MRI scans. Radiomics features were calculated and selected from the T1-CE MRI sequence, and used to build the radiomics signature. Clinical factors were evaluated by the logistic regression. Multivariable logistic regression analysis was applied to construct a radiomics nomogram incorporating the radiomics signature with the important clinical factor. Receiver operating characteristic (ROC), calibration and decision curve analysis (DCA) curves were plotted to assess the radiomics methods.
Results: A total of 5 features were finally identified highly related to the LM status to develop the radiomics signature. A clinical-radiomics nomogram integrating the radiomics signature and margin achieved the best prediction performance in the training (AUCs, nomogram vs. radiomics signature vs. margin, 0.918 vs. 0.864 vs. 0.609), internal validation (AUCs, nomogram vs. radiomics signature vs. margin, 0.864 vs. 0.841 vs. 0.666) and external validation (AUCs, nomogram vs. radiomics signature vs. margin, 0.843 vs. 0.800 vs. 0.643) cohort. DCA indicated potential usefulness of the nomogram.
Conclusions: This study evaluated predictive values of T1-CE MRI for the prediction of lung metastasis in STSs, and proposed a nomogram model to potentially facilitate the preoperative individualized treatment decision making for STSs.