Comparison of CT and MRI images for the prediction of soft-tissue sarcoma grading and lung metastasis via a convolutional neural networks model

Abstract

To realise the automated prediction of soft-tissue sarcoma (STS) grading and lung metastasis based on computed tomography (CT), T1-weighted (T1W) magnetic resonance imaging (MRI), and fat-suppressed T2-weighted MRI (FST2W) via the convolutional neural networks (CNN) model. MRI and CT images of 51 patients diagnosed with STS were analysed retrospectively. The patients could be divided into three groups based on disease grading: high-grade group (n=28), intermediate-grade group (n=15), low-grade group (n=8). Among these patients, 32 had lung metastasis, while the remaining 19 had no lung metastasis. The data were divided into the training, validation, and testing groups according to the ratio of 5:2:3. The receiver operating characteristic (ROC) curves and accuracy values were acquired using the testing dataset to evaluate the performance of the CNN model. For STS grading, the accuracy of the T1W, FST2W, CT, and the fusion of T1W and FST2W testing data were 0.86, 0.89, 0.86, and 0.85, respectively. In addition, Area Under Curve (AUC) were 0.96, 0.97, 0.97, and 0.94 respectively. For the prediction of lung metastasis, the accuracy of the T1W, FST2W, CT, and the fusion of T1W and FST2W test data were 0.92, 0.93, 0.88, and 0.91, respectively. The corresponding AUC values were 0.97, 0.96, 0.95, and 0.95, respectively. FST2W MRI performed best for predicting STS grading and lung metastasis. MRI and CT images combined with the CNN model can be useful for making predictions regarding STS grading and lung metastasis, thus providing help for patient diagnosis and treatment.