ObjectiveTo explore the application value of machine learning models in predicting postoperative survival of patients with thoracic squamous esophageal cancer. MethodsThe clinical data of 369 patients with thoracic esophageal squamous carcinoma who underwent radical esophageal cancer surgery at the Department of Thoracic Surgery of Northern Jiangsu People's Hospital from January 2014 to September 2015 were retrospectively analyzed. There were 279 (75.6%) males and 90 (24.4%) females aged 41-78 years. The patients were randomly divided into a training set (259 patients) and a test set (110 patients) with a ratio of 7 : 3. Variable screening was performed by selecting the best subset of features. Six machine learning models were constructed on this basis and validated in an independent test set. The performance of the models' predictions was evaluated by area under the curve (AUC), accuracy and logarithmic loss, and the fit of the models was reflected by calibration curves. The best model was selected as the final model. Risk stratification was performed using X-tile, and survival analysis was performed using the Kaplan-Meier method with log-rank test. ResultsThe 5-year postoperative survival rate of the patients was 67.5%. All clinicopathological characteristics of patients between the two groups in the training and test sets were not statistically different (P>0.05). A total of seven variables, including hypertension, history of smoking, history of alcohol consumption, degree of tissue differentiation, pN stage, vascular invasion and nerve invasion, were included for modelling. The AUC values for each model in the independent test set were: decision tree (AUC=0.796), support vector machine (AUC=0.829), random forest (AUC=0.831), logistic regression (AUC=0.838), gradient boosting machine (AUC=0.846), and XGBoost (AUC=0.853). The XGBoost model was finally selected as the best model, and risk stratification was performed on the training and test sets. Patients in the training and test sets were divided into a low risk group, an intermediate risk group and a high risk group, respectively. In both data sets, the differences in surgical prognosis among three groups were statistically significant (P<0.001). ConclusionMachine learning models have high value in predicting postoperative prognosis of thoracic squamous esophageal cancer. The XGBoost model outperforms common machine learning methods in predicting 5-year survival of patients with thoracic squamous esophageal cancer, and it has high utility and reliability.
ObjectiveTo explore the role of preoperative evaluation indicators for decision-making on treatment modalities in papillary thyroid microcarcinoma (PTMC) with intermediate- and high-risk. MethodThe recent pertinent literatures on studies of risk factors influencing PTMC were collected and reviewed. ResultsThe surgical treatment was advocated for the PTMC with intermediate- and high-risk. However, the intraoperative surgical resection range and the postoperative prognosis of patients were debated. The malignancy of cell puncture pathology was a key factor in determining the surgical protocol. The patients with less than 45 years old at surgery, male, higher body mass index, higher serum thyrotropin level, and multifocal and isthmic tumors, and nodule internal hypoecho, calcification, unclear boundary, and irregular morphology by ultrasound, as well as mutations in BRAFV600E and telomerase reverse transcriptase gene were the risk factors for preoperative evaluation of PTMC with intermediate- and high-risk. ConclusionsAccording to a comprehensive understanding of preoperative risk factors for PTMC with intermediate- and high-risk, it is convenient to conduct an accurate preoperative evaluation and fully grasp the patients’ conditions. Clinicians should formulate individualized surgical treatment plans for patients based on preoperative assessment and their own clinical experiences.