Objective To evaluate the security and clinical value of the combination of three-dimensional computed tomography-bronchography and angiography (3D-CTBA) and indocyanine green (ICG) staining in video-assisted thoracic surgery (VATS) segmentectomy. Methods The clinical data of 125 patients who received VATS segmentectomy from January 2020 to January 2021 in our hospital were retrospectively analyzed. There were 40 (32.0%) males and 85 (68.0%) females with an average age of 54.8±11.1 years. Results The procedure was almost identical to the preoperative simulation. All intersegment planes were displayed successfully by ICG reverse staining method. There was no allergic patient. A total of 130 pathological specimens were obtained from the 125 patients. The mean operation time was 126.8±41.9 min, the time of first appearance of fluorescence was 22.7±4.9 s, the mean mark time was 65.6±20.3 s, the median blood loss was 20.0 (10.0-400.0) mL, the postoperative hospital stay was 5.6 (4.0-28.0) d, and the postoperative retention of chest tube time was 3.2 (2.0-25.0) d. Pathological results showed that microinvasive adenocarcinoma was the most common type (38.5%, 50/130), followed by invasive adenocarcinoma (36.9%, 48/130); there were 3 metastatic tumors (3/130, 2.3%).Conclusion The combination of 3D-CTBA and ICG reverse staining is proved to be a safe, necessary and feasible method. It solves the difficult work encountered in the procedure of segmentectomy, and it is worth popularizing and applying in clinic.
Objective To evaluate the predictive performance of the geometric characteristics, centerline (CL) of pulmonary nodules for prognosis in patients with surgically treatment in the National Lung Screening Trial (NLST). MethodsCT images of 178 patients who underwent surgical treatment and were diagnosed with non-small cell lung cancer (NSCLC) in the low-dose CT (LDCT) cohort from the NLST image database were selected, including 99 males and 79 females, with a median age of 64 (59, 68) years. CT images were processed using commercial software Mimics 21.0 to record the volume, surface area, CL and the area perpendicular to the centerline of pulmonary nodules. Receiver operating characteristic (ROC) curve was used to compare the predictive performance of LD, AD and CL on prognosis. Univariate Cox regression was used to explore the influencing factors for postoperative disease-free survival (DFS) and overall survival (OS), and meaningful independent variables were included in the multivariate Cox regression to construct the prediction model. ResultsThe area under the curve (AUC) of CL for postoperative recurrence and death were 0.650 and 0.719, better than LD (0.596, 0.623) and AD (0.600, 0.631). Multivariate Cox proportional risk regression analysis showed that pulmonary nodule volume (P=0.010), the maximum area perpendicular to the centerline (MApc) (P=0.028) and lymph node metastasis (P<0.001) were independent risk factors for DFS. Meanwhile, age (P=0.010), CL (P=0.043), lymph node metastasis (P<0.001), MApc (P=0.022) and the average area perpendicular to the centerline (AApc) (P=0.016) were independently associated with OS. ConclusionFor the postoperative outcomes of NSCLC patients in the LDCT cohort of the NLST, the CL of the pulmonary nodule prediction performance for prognosis is superior to the LD and AD, CL can effectively predict the risk stratification and prognosis of lung cancer, and spheroid tumors have a better prognosis.
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.