Predicting Tumor Dynamics Post-Staged GKRS: Machine Learning Models in Brain Metastases Prognosis.

Autor: Trofin AM; University of Medicine and Pharmacy 'Grigore T. Popa' Iași, 700115 Iasi, Romania., Buzea CG; Clinical Emergency Hospital 'Prof. Dr. Nicolae Oblu' Iași, 700309 Iasi, Romania.; National Institute of Research and Development for Technical Physics, IFT Iași, 700050 Iasi, Romania., Buga R; University of Medicine and Pharmacy 'Grigore T. Popa' Iași, 700115 Iasi, Romania.; Clinical Emergency Hospital 'Prof. Dr. Nicolae Oblu' Iași, 700309 Iasi, Romania., Agop M; Physics Department, Technical University 'Gheorghe Asachi' Iasi, 700050 Iasi, Romania., Ochiuz L; University of Medicine and Pharmacy 'Grigore T. Popa' Iași, 700115 Iasi, Romania., Iancu DT; University of Medicine and Pharmacy 'Grigore T. Popa' Iași, 700115 Iasi, Romania.; Regional Institute of Oncology, 700483 Iasi, Romania., Eva L; Clinical Emergency Hospital 'Prof. Dr. Nicolae Oblu' Iași, 700309 Iasi, Romania.; University Apollonia, 700511 Iasi, Romania.
Jazyk: angličtina
Zdroj: Diagnostics (Basel, Switzerland) [Diagnostics (Basel)] 2024 Jun 15; Vol. 14 (12). Date of Electronic Publication: 2024 Jun 15.
DOI: 10.3390/diagnostics14121268
Abstrakt: This study assesses the predictive performance of six machine learning models and a 1D Convolutional Neural Network (CNN) 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. The CNN model showcased notable performance with an accuracy of 98% and an AUC of 0.97, effectively complementing the broader model analysis. 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. Our research explores the effects of staged Gamma Knife radiosurgery (GKRS) on larger tumors, revealing no significant outcome differences across protocols. It uniquely considers the impact of beam-on time and fraction intervals on treatment efficacy. However, the investigation is limited by a small patient cohort and data from a single institution, suggesting the need for future multicenter research.
Databáze: MEDLINE
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