Radiation oncology (London, England) · 2025
Machine learning-based integration of dosiomics and pre-radiotherapy multimodal MRI radiomics for survival stratification in patients with glioblastoma multiforme.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Mahmoudi Atefeh, Sadeghi Arash Zare, Iraji Hamed, Barahman Maedeh, Saadatmand Pegah, Yazdani Elmira, Mahdavi Seied Rabi
Аннотация
BACKGROUND: Tumor heterogeneity is a significant factor contributing to the marked differences in survival rates among glioblastoma multiforme (GBM) patients, who face a poor prognosis. To improve personalized treatment, it is essential to identify specific tumor characteristics that capture this variability and aid in predicting survival. This study aimed to evaluate the utility of dosiomics and radiomics in predicting overall survival (OS). The central hypothesis was that integrating dosiomics and radiomics could improve survival outcome predictions.
METHODS: A total of 74 GBM patients from The Cancer Imaging Archive were retrospectively included. Dosiomic features from the gross tumor volume (GTV) of planned dose distributions, along with radiomic features from the contrast-enhanced tumor (CET) and edema/non-contrast-enhanced tumor (ED/nCET) subregions across various pre-radiation MRI modalities, were extracted and optimized using L1-based feature selection. Logistic Regression (LR) models were built utilizing different feature configurations to assess the discriminative power of dosiomic and radiomic features, considering the impact of heterogeneous subregions. Model performance was assessed through stratified 10-fold cross-validation (CV).
RESULTS: The dosiomic model exhibited a mean area under the receiver operating characteristic (ROC) curve (AUC) of 0.80 ± 0.12. The subregion-based models demonstrated mean AUC values of 0.90 ± 0.09 for the CET subregion and 0.76 ± 0.10 for the ED/nCET subregion, indicating that the CET subregion significantly outperformed the ED/nCET subregion (p-value < 0.05). The mean AUC values for modality-based models were as follows: 0.86 ± 0.12 for T1-weighted contrast-enhanced (T1CE), 0.84 ± 0.18 for T1-weighted (T1), 0.85 ± 0.14 for T2-weighted (T2), and 0.76 ± 0.21 for fluid-attenuated inversion recovery (FLAIR) sequences. There was no significant difference in discrimination power among the four modalities (p-value > 0.05). The combined dosiomic and CET model improved performance to 0.96 ± 0.07 (p < 0.05).
CONCLUSIONS: Dosiomic and pre-radiotherapy MRI-derived radiomic features are capable of stratifying GBM patients into two long-term and short-term groups. Notably, the integration of dosiomics and radiomics significantly enhances survival prediction in GBM patients.
METHODS: A total of 74 GBM patients from The Cancer Imaging Archive were retrospectively included. Dosiomic features from the gross tumor volume (GTV) of planned dose distributions, along with radiomic features from the contrast-enhanced tumor (CET) and edema/non-contrast-enhanced tumor (ED/nCET) subregions across various pre-radiation MRI modalities, were extracted and optimized using L1-based feature selection. Logistic Regression (LR) models were built utilizing different feature configurations to assess the discriminative power of dosiomic and radiomic features, considering the impact of heterogeneous subregions. Model performance was assessed through stratified 10-fold cross-validation (CV).
RESULTS: The dosiomic model exhibited a mean area under the receiver operating characteristic (ROC) curve (AUC) of 0.80 ± 0.12. The subregion-based models demonstrated mean AUC values of 0.90 ± 0.09 for the CET subregion and 0.76 ± 0.10 for the ED/nCET subregion, indicating that the CET subregion significantly outperformed the ED/nCET subregion (p-value < 0.05). The mean AUC values for modality-based models were as follows: 0.86 ± 0.12 for T1-weighted contrast-enhanced (T1CE), 0.84 ± 0.18 for T1-weighted (T1), 0.85 ± 0.14 for T2-weighted (T2), and 0.76 ± 0.21 for fluid-attenuated inversion recovery (FLAIR) sequences. There was no significant difference in discrimination power among the four modalities (p-value > 0.05). The combined dosiomic and CET model improved performance to 0.96 ± 0.07 (p < 0.05).
CONCLUSIONS: Dosiomic and pre-radiotherapy MRI-derived radiomic features are capable of stratifying GBM patients into two long-term and short-term groups. Notably, the integration of dosiomics and radiomics significantly enhances survival prediction in GBM patients.