Cholangiocarcinoma is a highly malignant tumor. It is not sensitive to radiotherapy and chemotherapy and has a poor prognosis. At present, there is no effective treatment. As a new method for treating cancer, magnetic fluid hyperthermia has been clinically applied to a variety of cancers in recent years. This article introduces it to the cholangiocarcinoma model and systematically studies the effect of magnetic fluid hyperthermia on cholangiocarcinoma. Starting from the theory of magnetic fluid heating, the electromagnetic and heat transfer models were constructed in the finite element simulation software COMSOL using the Pennes biological heat transfer equation. The Helmholtz coil was used as an alternating magnetic field generating device. The relationship between the magnetic fluid-related properties and the heating power was analyzed according to Rosensweig’s theory. After the multiphysics coupling simulation was performed, the electromagnetic field and thermal field distribution in the hyperthermia region were obtained. The results showed that the magnetic field distribution in the treatment area was uniform, and the thermal field distribution met the requirements of hyperthermia. After the magnetic fluid injection, the cholangiocarcinoma tissue warmed up rapidly, and the temperature of tumor tissues could reach above 42 °C, but the surrounding healthy tissues did not heat up significantly. At the same time, it was verified that the large blood vessels around the bile duct, the overflow of the magnetic fluid, and the eddy current heat had little effect on thermotherapy. The results of this article can provide a reference for the clinical application of magnetic fluid hyperthermia for cholangiocarcinoma.
ObjectiveTo develop and validate a prediction model for postoperative hepatic dysfunction after Stanford type A aortic dissection (TAAD), providing a reference for early identification and intervention. MethodsWe retrospectively enrolled the patients with TAAD who underwent surgical treatment at Renmin Hospital of Wuhan University from August 2022 to August 2025 and at Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology from December 2022 to June 2025. The dataset was randomly divided into a training set and a validation set at a ratio of 7.5 : 2.5. Independent predictors were identified using univariate analysis and multivariate logistic regression. Predictive models were developed using logistic regression (LR), random forest (RF), and extreme gradient boosting (XGBoost). Model performance was assessed using receiver operating characteristic (ROC) curves, DeLong tests, calibration curves, and decision curve analysis (DCA). A nomogram was constructed based on the optimal model. ResultsA total of 482 patients were included, comprising 368 males and 114 females, with a median age of 54 (45, 62) years. Among them, 214 (44.4%) patients developed postoperative hepatic dysfunction. Multivariable analysis identified five independent predictors: preoperative serum creatinine level, pericardial effusion, postoperative mechanical ventilation duration, procalcitonin, and total bilirubin (all P<0.05). In the validation cohort, the area under the curve (AUC) values of the LR, RF, and XGBoost models were 0.741, 0.725, and 0.712, respectively, with the LR model demonstrating the best overall performance. The DeLong test showed no significant difference in AUC among the three models (P>0.05). Calibration curves demonstrated better agreement for the LR model. The DCA indicated that the LR model provided greater net benefit across a wider range of threshold probabilities. ConclusionMachine learning models do not outperform traditional LR in predicting postoperative hepatic dysfunction after TAAD. The LR model demonstrats more stable predictive performance and greater clinical utility. The nomogram developed based on this model may provide a reference for individualized clinical risk assessment.