Version 1
: Received: 5 May 2024 / Approved: 6 May 2024 / Online: 6 May 2024 (10:47:26 CEST)
How to cite:
Trofin, A. M.; Buzea, C. G.; Buga, R.; Agop, M.; Ochiuz, L.; Iancu, D. T.; Eva, L. Predicting Tumor Dynamics Post - staged GKRS: Machine Learning Models in Brain Metastases Prognosis. Preprints2024, 2024050293. https://doi.org/10.20944/preprints202405.0293.v1
Trofin, A. M.; Buzea, C. G.; Buga, R.; Agop, M.; Ochiuz, L.; Iancu, D. T.; Eva, L. Predicting Tumor Dynamics Post - staged GKRS: Machine Learning Models in Brain Metastases Prognosis. Preprints 2024, 2024050293. https://doi.org/10.20944/preprints202405.0293.v1
Trofin, A. M.; Buzea, C. G.; Buga, R.; Agop, M.; Ochiuz, L.; Iancu, D. T.; Eva, L. Predicting Tumor Dynamics Post - staged GKRS: Machine Learning Models in Brain Metastases Prognosis. Preprints2024, 2024050293. https://doi.org/10.20944/preprints202405.0293.v1
APA Style
Trofin, A. M., Buzea, C. G., Buga, R., Agop, M., Ochiuz, L., Iancu, D. T., & Eva, L. (2024). Predicting Tumor Dynamics Post - staged GKRS: Machine Learning Models in Brain Metastases Prognosis. Preprints. https://doi.org/10.20944/preprints202405.0293.v1
Chicago/Turabian Style
Trofin, A. M., Dragoș Teodor Iancu and Lucian Eva. 2024 "Predicting Tumor Dynamics Post - staged GKRS: Machine Learning Models in Brain Metastases Prognosis" Preprints. https://doi.org/10.20944/preprints202405.0293.v1
Abstract
This study assesses the predictive performance of six machine learning models in forecasting tumor dynamics within three months following Gamma Knife Radiosurgery (GKRS) in 77 brain metastasis (BM) patients. The analysis meticulously evaluates each model before and after hyperparameter tuning, utilizing accuracy, AUC and other metrics derived from confusion matrices. Initial findings highlighted that XGBoost significantly outperformed other models with an accuracy of 0.95 and an AUC of 0.95 before tuning. Post-tuning, the Support Vector Machine (SVM) demonstrated the most substantial improvement, achieving an accuracy of 0.98 and an AUC of 0.98. Conversely, XGBoost showed a decline in performance after tuning, indicating potential overfitting. The study also explores feature importance across models, noting that features like “control at one year”, “age of the patient”, and “beam on time for volume V1 treated”, were consistently influential across various models, albeit their impacts were interpreted differently depending on the model’s underlying mechanics. This comprehensive evaluation not only underscores the importance of model selection and hyperparameter tuning but also highlights the practical implications in medical diagnostic scenarios, where the accuracy of positive predictions can be crucial.
Medicine and Pharmacology, Neuroscience and Neurology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
