⚡ Quick Summary
This study evaluated the performance of various ensemble learning models for predicting tumor deposits in rectal cancer using MR imaging. The voting-ensemble learning model (VELM) emerged as the most effective, achieving an AUC of 0.875 and an accuracy of 0.800, highlighting its potential for enhancing preoperative clinical decision-making.
🔍 Key Details
- 📊 Dataset: 199 rectal cancer patients
- 🧩 Features used: Radiomic features from T2-weighted and apparent diffusion coefficient images
- ⚙️ Technologies: Bagging (Random Forest), Boosting (XGBoost, AdaBoost, LightGBM, CatBoost), and Voting Ensemble Learning
- 🏆 Performance: VELM: AUC 0.875, Accuracy 0.800
🔑 Key Takeaways
- 📊 Ensemble learning effectively reduces the risk of model overfitting.
- 💡 VELM outperformed other models in predicting tumor deposits.
- 👩🔬 Advanced statistical methods were used for feature selection.
- 🏆 Calibration plots confirmed VELM’s stability.
- 🤖 t-SNE visualization illustrated clear clustering of radiomic features.
- 🏥 Decision curve analysis validated VELM’s superior net benefit across clinical thresholds.
- 🌍 Study published in Scientific Reports, 2025.
- 🆔 PMID: 39924571.
📚 Background
Rectal cancer remains a significant health challenge, with accurate preoperative predictions being crucial for effective treatment planning. Traditional methods often fall short in providing reliable predictions, leading to the exploration of advanced techniques such as ensemble learning. This approach combines multiple models to enhance predictive performance and mitigate overfitting, making it a promising avenue for improving clinical outcomes.
🗒️ Study
The study involved a comprehensive analysis of 199 rectal cancer patients, focusing on the extraction of radiomic features from MR imaging. Various ensemble learning models were applied, including bagging, boosting, and voting methods, with optimization through grid search and tenfold cross-validation. The aim was to identify the most effective model for predicting tumor deposits preoperatively.
📈 Results
The voting-ensemble learning model (VELM) demonstrated superior performance, achieving an AUC of 0.875 and an accuracy of 0.800 in the testing cohort. The stability of VELM was confirmed through calibration plots, while t-SNE visualization provided insights into the clustering of radiomic features, indicating the model’s robustness and reliability.
🌍 Impact and Implications
The findings from this study have significant implications for the field of oncology. By leveraging ensemble learning models, clinicians can enhance their preoperative decision-making processes, leading to improved patient outcomes in rectal cancer treatment. The potential for integrating such advanced predictive tools into clinical practice could revolutionize how tumor deposits are assessed and managed.
🔮 Conclusion
This research highlights the remarkable potential of ensemble learning in the realm of rectal cancer prediction. The success of the voting-ensemble learning model underscores the importance of adopting innovative technologies in clinical settings. As we move forward, further exploration and validation of these models could pave the way for more accurate and reliable preoperative assessments in oncology.
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Comparative performance of multiple ensemble learning models for preoperative prediction of tumor deposits in rectal cancer based on MR imaging.
Abstract
Ensemble learning can effectively mitigate the risk of model overfitting during training. This study aims to evaluate the performance of ensemble learning models in predicting tumor deposits in rectal cancer (RC) and identify the optimal model for preoperative clinical decision-making. A total of 199 RC patients were analyzed, with radiomic features extracted from T2-weighted and apparent diffusion coefficient images and selected through advanced statistical methods. After that, the bagging-ensemble learning model (random forest), boosting-ensemble learning model (XGBoost, AdaBoost, LightGBM, and CatBoost), and voting-ensemble learning model (integrating 5 classifiers) were applied and optimized using grid search with tenfold cross-validation. The area under the receiver operator characteristic curve, calibration curve, t-distributed stochastic neighbor embedding (t-SNE), and decision curve analysis were adopted to evaluate the performance of each model. The voting-ensemble learning model (VELM) performs best in the testing cohort, with an AUC of 0.875 and an accuracy of 0.800. Notably, Calibration plots confirmed VELM’s stability and t-SNE visualization illustrated clear clustering of radiomic features. Decision curve analysis further validated the VELM’s superior net benefit across a range of clinical thresholds, underscoring its potential as a reliable tool for clinical decision-making in RC.
Author: [‘Wang J’, ‘Hu F’, ‘Li J’, ‘Lv W’, ‘Liu Z’, ‘Wang L’]
Journal: Sci Rep
Citation: Wang J, et al. Comparative performance of multiple ensemble learning models for preoperative prediction of tumor deposits in rectal cancer based on MR imaging. Comparative performance of multiple ensemble learning models for preoperative prediction of tumor deposits in rectal cancer based on MR imaging. 2025; 15:4848. doi: 10.1038/s41598-025-89482-3