Develop a radiomics-based machine learning model to predict the stone-free rate post-percutaneous nephrolithotomy.

Radiomics and machine learning have been extensively utilized in the realm of urinary stones, particularly in forecasting stone treatment outcomes. The objective of this study was to integrate clinical variables and radiomic features to develop a machine learning model for predicting the stone-free rate (SFR) following percutaneous nephrolithotomy (PCNL). A total of 212 eligible patients who underwent PCNL surgery at the Second Affiliated Hospital of Nanchang University were included in a retrospective analysis. Preoperative clinical variables and non-contrast-enhanced CT images of all patients were collected, and radiomic features were extracted after delineating the stone ROI. Univariate analysis was conducted to identify clinical variables strongly correlated with the stone-free rate after PCNL, and the least absolute shrinkage and selection operator algorithm (lasso regression) was utilized to screen radiomic features. Four supervised machine learning algorithms, including Logistic Regression, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Gradient Boosting Decision Tree (GBDT), were employed. The clinical variables with strong correlation and screened radiomic features were integrated into the four machine learning algorithms to construct a prediction model, and the receiver operating curve was plotted. The area under the receiver operating curve (AUC), the accuracy rate, the specificity, etc., were used to evaluate the predictive performance of the four models. After analyzing postoperative statistics, the stone-free rate following the procedure was found to be 70.3% (n = 149). Among the various clinical variables examined, factors, such as stone number, stone diameter, stone CT value, stone location, and history of stone surgery, were identified as statistically significant in relation to the stone-free rate after PCNL. A total of 121 radiomic features were extracted, and through lasso regression, 7 features most closely associated with the stone-free rate post-PCNL were identified. The predictive accuracy of different models (Logistic Regression, RF, XGBoost, and GBDT) for determining the stone-free rate after PCNL was evaluated, yielding accuracies of 78.1%, 76.6%, 75.0%, and 73.4%, respectively. The corresponding area under the curve AUC (95%CI) were 0.85 (0.83-0.89), 0.81 (0.76-0.85), 0.82 (0.78-0.85), and 0.77 (0.73-0.81), positioning these models among the top performers in logistic regression prediction. In terms of predictive importance scores, the key factors identified by the logistic regression model were number of stone, zone percentage, stone diameter, and surface area. Similarly, the RF model highlighted number of stone, stone CT value, stone diameter, and surface area as the top predictors. Among the four machine learning models, the logistic regression model demonstrated the highest accuracy and discrimination ability in predicting the stone-free rate following PCNL. In comparison to XGBoost and GBDT, RF also exhibited superior accuracy and a certain level of discrimination ability. However, based on the performance of all four models, logistic regression is more likely to aid in clinical decision-making by assisting clinicians in diagnosing PCNL in patients. This enables us to effectively predict the presence of residual stones post-surgery and ultimately select patients who are suitable candidates for PCNL.

Decreased interoceptive accuracy in children with autism spectrum disorder and with comorbid attention deficit/hyperactivity disorder.

Interoception refers to the awareness of internal physiological state. Several previous studies reported that people with autism spectrum disorders (ASD) and adults with attention-deficit/hyperactivity disorder (ADHD) have diverse patterns of interoception, but the extent of literature is limited and inconsistent. This study aimed to investigate the interoceptive accuracy (IA) in children with ASD, children with comorbid ASD and ADHD, and typically developing (TD) children with high and low levels of autistic traits. We administered the eye-tracking interoceptive accuracy task (EIAT) to 30 children with ASD, 20 children with comorbid ASD and ADHD, and 63 TD controls with high and low levels of autistic traits. Parent-report scales concerning ASD and ADHD symptoms were collected. ASD children with and without comorbid ADHD both exhibited lower IA than TD children. Reduced IA was also found in TD children with high-autistic traits relative to those with low-autistic traits. IA was negatively correlated with autistic and ADHD symptoms. Atypical cardiac interoception could be found in children with ASD. Difficulties in sensing and comprehending internal bodily signals in childhood may be related to both ASD and ADHD symptoms. LAY SUMMARY: The present study examined interoceptive accuracy (IA) in children with autism spectrum disorders (ASD), children with comorbid ASD and attention-deficit/hyperactivity disorder (ADHD), and typically developing (TD) children with high and low levels of autistic traits. ASD children with and without comorbid ADHD both exhibited lower IA than TD children. TD children with high-autistic traits exhibited decreased IA compared to those with low-autistic traits. These results have implications for understanding sensory atypicality found in ASD and ADHD.