Clinical-Deep Neural Network and Clinical-Radiomics Nomograms for Predicting the Intraoperative Massive Blood Loss of Pelvic and Sacral Tumors

BACKGROUND: Patients with pelvic and sacral tumors are prone to massive blood loss (MBL) during surgery, which may endanger their lives. PURPOSES: This study aimed to determine the feasibility of using deep neural network (DNN) and radiomics nomogram (RN) based on 3D computed tomography (CT) features and clinical characteristics to predict the intraoperative MBL of pelvic and sacral tumors. MATERIALS AND METHODS: This single-center retrospective analysis included 810 patients with pelvic and sacral tumors. 1316 CT and CT enhanced radiomics features were extracted. RN1 and RN2 were constructed by random grouping and time node grouping, respectively. The DNN models were constructed for comparison with RN. Clinical factors associated with the MBL were also evaluated. The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) were used to evaluate different models. RESULTS: Radscore, tumor type, tumor location, and sex were significant predictors of the MBL of pelvic and sacral tumors (P < 0.05), of which radscore (OR, ranging from 2.109 to 4.706, P < 0.001) was the most important. The clinical-DNN and clinical-RN performed better than DNN and RN. The best-performing clinical-DNN model based on CT features exhibited an AUC of 0.92 and an ACC of 0.97 in the training set, and an AUC of 0.92 and an ACC of 0.75 in the validation set. CONCLUSIONS: The clinical-DNN and clinical-RN had good performance in predicting the MBL of pelvic and sacral tumors, which could be used for clinical decision-making.