A radiograph-based deep learning model improves radiologists' performance for classification of histological types of primary bone tumors: A multicenter study.

PURPOSE: To develop a deep learning (DL) model for classifying histological types of primary bone tumors (PBTs) using radiographs and evaluate its clinical utility in assisting radiologists. METHODS: This retrospective study included 878 patients with pathologically confirmed PBTs from two centers (638, 77, 80, and 83 for the training, validation, internal test, and external test sets, respectively). We classified PBTs into five categories by histological types: chondrogenic tumors, osteogenic tumors, osteoclastic giant cell-rich tumors, other mesenchymal tumors of bone, or other histological types of PBTs. A DL model combining radiographs and clinical features based on the EfficientNet-B3 was developed for five-category classification. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to evaluate model performance. The clinical utility of the model was evaluated in an observer study with four radiologists. RESULTS: The combined model achieved a macro average AUC of 0.904/0.873, with an accuracy of 67.5 %/68.7 %, a macro average sensitivity of 66.9 %/57.2 %, and a macro average specificity of 92.1 %/91.6 % on the internal/external test set, respectively. Model-assisted analysis improved accuracy, interpretation time, and confidence for junior (50.6 % vs. 72.3 %, 53.07[s] vs. 18.55[s] and 3.10 vs. 3.73 on a 5-point Likert scale [P < 0.05 for each], respectively) and senior radiologists (68.7 % vs. 75.3 %, 32.50[s] vs. 21.42[s] and 4.19 vs. 4.37 [P < 0.05 for each], respectively). CONCLUSION: The combined DL model effectively classified histological types of PBTs and assisted radiologists in achieving better classification results than their independent visual assessment.

Construction and validation of risk prediction model for gastrointestinal bleeding in patients after coronary artery bypass grafting.

This study aimed to develop a risk prediction model for gastrointestinal bleeding in patients after coronary artery bypass grafting (CABG) and assessed its accuracy. A retrospective analysis was conducted on 232 patients who underwent CABG under general anesthesia in our hospital between January 2022 and December 2022. The patients were divided into gastrointestinal bleeding (GIB) group (n = 52) and group without gastrointestinal bleeding (non-GIB) (n = 180). The independent risk factors for gastrointestinal bleeding in post-CABG patients were analyzed using χ2 test, t test and logistic multivariate regression analysis. A prediction model was established based on the identified risk factors. To verify the accuracy of the prediction model, a verification group of 161 patients who met the criteria was selected between January to June 2023, and the Bootstrap method was used for internal validation. The discrimination of the prediction model was evaluated using the area under the curve (AUC), where a higher AUC indicates a stronger discrimination effect of the model. The study developed a risk prediction model for gastrointestinal bleeding after CABG surgery. The model identified four independent risk factors: duration of stay in the intensive care unit (ICU) (OR 0.761), cardiopulmonary bypass time (OR 1.019), prolonged aortic occlusion time (OR 0.981) and re-operation for bleeding (OR 0.180). Based on these factors, an individualized risk prediction model was constructed. The C-index values of the modeling group and the verification group were 0.805 [95% CI (0.7303-0.8793)] and 0.785 [95% CI (0.6932-0.8766)], respectively, which indicated a good accuracy and discrimination of this model. The calibration and standard curves showed similar results, which further supported the accuracy of the risk prediction model. In conclusion, ICU time, cardiopulmonary bypass time, aortic occlusion time and re-operation for bleeding are identified as independent risk factors for gastrointestinal bleeding in patients after CABG. The risk prediction model developed in this study demonstrates strong predictive performance and provides valuable insights for clinical medical professionals in evaluating gastrointestinal complications in CABG patients.