Development of a risk prediction model for radiation dermatitis following proton radiotherapy in head and neck cancer using ensemble machine learning.

PURPOSE: This study aims to develop an ensemble machine learning-based (EML-based) risk prediction model for radiation dermatitis (RD) in patients with head and neck cancer undergoing proton radiotherapy, with the goal of achieving superior predictive performance compared to traditional models. MATERIALS AND METHODS: Data from 57 head and neck cancer patients treated with intensity-modulated proton therapy at Kaohsiung Chang Gung Memorial Hospital were analyzed. The study incorporated 11 clinical and 9 dosimetric parameters. Pearson's correlation was used to eliminate highly correlated variables, followed by feature selection via LASSO to focus on potential RD predictors. Model training involved traditional logistic regression (LR) and advanced ensemble methods such as Random Forest and XGBoost, which were optimized through hyperparameter tuning. RESULTS: Feature selection identified six key predictors, including smoking history and specific dosimetric parameters. Ensemble machine learning models, particularly XGBoost, demonstrated superior performance, achieving the highest AUC of 0.890. Feature importance was assessed using SHAP (SHapley Additive exPlanations) values, which underscored the relevance of various clinical and dosimetric factors in predicting RD. CONCLUSION: The study confirms that EML methods, especially XGBoost with its boosting algorithm, provide superior predictive accuracy, enhanced feature selection, and improved data handling compared to traditional LR. While LR offers greater interpretability, the precision and broader applicability of EML make it more suitable for complex medical prediction tasks, such as predicting radiation dermatitis. Given these advantages, EML is highly recommended for further research and application in clinical settings.

Low-Voltage and Low-Power True-Single-Phase 16-Transistor Flip-Flop Design.

A low-voltage and low-power true single-phase flip-flop that minimum the total transistor count by using the pass transistor logic circuit scheme is proposed in this paper. Optimization measures lead to a new flip-flop design with better various performances such as speed, power, energy, and layout area. Based on post-layout simulation results using the TSMC CMOS 180 nm and 90 nm technologies, the proposed design achieves the conventional transmission-gate-based flip-flop design with a 53.6% reduction in power consumption and a 63.2% reduction in energy, with 12.5% input data switching activity. In order to further the performance parameters of the proposed design, a shift-register design has been realized. Experimental measurements at 0.5 V/0.5 MHz show that this proposed design reduces power consumption by 47.3% while achieving a layout area reduction of 30.5% compared to the conventional design.