ArticleAdvances in radiation oncology2024
Pretreatment Spatially Aware Magnetic Resonance Imaging Radiomics Can Predict Distant Brain Metastases (DBMs) After Stereotactic Radiosurgery/Radiation Therapy (SRS/SRT).
Article in Advances in radiation oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 4 citations in OpenAlex.
- Knowledge attitudes and practices regarding MRI safety among healthcare providers and patients/family members in China.Scientific reports · 2026Article
- Spatially aware radiomics integrating anatomical knowledge to improve lymph node malignancy prediction in head and neck cancer.Journal of applied clinical medical physics · 2026Article
- Stereotactic radiosurgery for patients with brain metastases: current principles, expanding indications and opportunities for multidisciplinary care.Nature reviews. Clinical oncology · 2025Review
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Stereotactic radiosurgery/radiation therapy (SRS/SRT) increasingly has been used to treat brain metastases. However, the development of distant brain metastases (DBMs) in the untreated brain remains a serious complication. We sought to develop a spatially aware radiomic signature to model the time-to-DBM development in a cohort of patients leveraging pretreatment magnetic resonance imaging (MRI) and radiation therapy treatment planning data including radiation dose distribution maps. Methods and Materials: We retrospectively analyzed a cohort of 105 patients with brain metastases treated by SRS/SRT with pretreatment multiparametric MRI (T1, T1 postcontrast, T2, fluid-attenuated inversion recovery). Three-dimensional radiomic features were extracted from each MRI sequence within 5 isodose regions of interest (ROIs) identified via radiation dose distribution maps and gross target volume (GTV) contours. Clinical features including patient performance status, number of lesions treated, tumor volume, and tumor stage were collected to serve as a baseline for comparison. Cox proportional hazards (CPH) modeling and Kaplan-Meier analysis were used to model time-to-DBM development. Results: CPH models trained using radiomic features achieved a mean concordance index (c-index) of 0.63 (standard deviation [SD], 0.08) compared with a c-index of 0.49 (SD, 0.09) for CPH models trained using clinical factors. A CPH model trained using both radiomic and clinical features achieved a c-index of 0.69 (SD, 0.08). The identified radiomic signature was able to stratify patients into distinct risk groups with statistically significant differences ( Conclusions: Our results suggest that radiomic features extracted from pretreatment MRI and multiple isodose ROIs can model time-to-DBM development in patients receiving SRS/SRT for brain metastases, outperforming clinical feature baselines. Notably, we believe we are the first to leverage SRS/SRT dose maps for ROI identification and subsequent radiomic analysis of peritumoral and untargeted brain regions using multiparametric MRI. We observed that the peritumoral environment may be implicated in DBM development for SRS/SRT-treated brain metastases. Our preliminary results might enable the identification of patients with predisposition to DBM development and prompt subsequent changes in disease management.
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