ObjectiveTo explore the changes of bone and risk of micro-fracture in femoral head after removal of cannulated screws following femoral neck fracture healing under the impact force of daily stress.MethodsA total of 42 specimens of normal hip joint were collected from 21 adult fresh cadaveric pelvic specimens. Wiberg central-edge (CE) angle, bone mineral density, diameter of femoral head, neck-shaft angle, and anteversion angle of femoral neck were measured. Then, the 3 cannulated screws were implanted according to the AO recommended method and removed to simulate the complete anatomical union of femoral neck fracture. The morphology of screw canal in the femoral head was observed by CT. Finally, the specimens were immobilized vertically within the impact device in an upside-down manner, and the femoral heads were impacted vertically. Every specimen was impacted at 200, 600, and 1 980 N for 20 times with the impacting device. After impact, every specimen was scanned by CT to observe the morphology changes of screw canal in the femoral head. Micro-fractures in the femoral head could be confirmed when there was change in the morphology of screw canal, and statistical software was used to analyze the risk factors associated with micro-fractures.ResultsAfter impact at 200 and 600 N, CT showed that the morphology of screw canal of all specimens did not change significantly compared with the original. After impact at 1 980 N, there were protrusion and narrowing in the screw canal of the 22 femoral head specimens (11 pelvic specimens), showing obvious changes compared with the original screw canal, indicating that there were micro-fractures in the femoral head. The incidence of micro-fracture was 52.38% (11/21). logistic regression results showed that there was correlation between micro-fracture and bone mineral density (P= 0.039), but no correlation was found with CE angle, diameter of femoral head, neck-shaft angle, and anteversion angle (P>0.05).ConclusionThe micro-fractures in the femoral head may occur when the femoral head is impacted by daily activities after removal of cannulated screws for femoral neck fractures, and such micro-fractures are associated with decreased bone density of the femoral head.
ObjectiveTo develop and validate a machine learning model based on preoperative clinical characteristics, laboratory indices, and radiological features for the non-invasive prediction of spread through air spaces (STAS) in patients with early-stage lung adenocarcinoma. Methods Preoperative data from patients with early-stage lung adenocarcinoma who underwent surgical resection at Northern Jiangsu People's Hospital between January 2020 and August 2025 were retrospectively collected. The data included clinical characteristics, laboratory indices, and radiological features. Patients were divided into a STAS-positive and a STAS-negative group based on postoperative pathological findings. The dataset was randomly split into a training set and a testing set at a 7 : 3 ratio. Feature variables were selected using the maximum relevance and minimum redundancy (mRMR) algorithm and the least absolute shrinkage and selection operator (LASSO) regression. Five machine learning models were constructed: logistic regression (LR), random forest (RF), support vector machine (SVM), light gradient boosting machine (LightGBM), and extreme gradient boosting (XGBoost). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA). The shapley additive explanations (SHAP) method was employed to interpret the optimal prediction model. Results A total of 377 patients were included, comprising 177 (46.9%) males and 200 females (53.1%), with a mean age of (63.31±9.73) years. There were 261 patients in the training set and 116 patients in the testing set. In the training set, statistically significant differences were observed between the STAS-positive group (n=130) and STAS-negative group (n=131) across multiple features, including age, sex, neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), clinical T stage, and maximum solid component diameter (P<0.05). A final set of 10 feature variables was selected by combining mRMR and LASSO regression, and five machine learning models (LR, RF, SVM, LightGBM, XGBoost) were developed. The XGBoost model demonstrated superior predictive performance in both the training and testing sets, achieving AUCs of 0.947 [95%CI (0.920, 0.975)] and 0.943 [95%CI (0.894, 0.993)], respectively, and achieved the optimal level in the testing set. DCA indicated that the XGBoost model provided a high net clinical benefit across a wide range of threshold probabilities. SHAP analysis revealed that the vessel convergence sign, clinical T stage, age, consolidation-to-tumor ratio (CTR), and MLR were the features with the highest contributions to STAS prediction. Conclusion The XGBoost model effectively predicts preoperative STAS status in early-stage lung adenocarcinoma, exhibiting excellent discriminative performance and good clinical interpretability. Key predictors such as the vessel convergence sign, clinical T stage, age and CTR provide a crucial reference for preoperative risk assessment and the individualized selection of surgical strategies, ultimately benefiting patients.