ArticleNeuro-oncology advances
Multiparametric radiomic tissue signature and machine learning for distinguishing radiation necrosis from tumor progression after stereotactic radiosurgery.
Article in Neuro-oncology advances. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 20 citations in OpenAlex.
- A Radiogenomic Deep Ensemble Learning Model for Identifying Radionecrosis Following Brain Metastases (BM) Stereotactic Radiosurgery in Patients With Non-small Cell Lung Cancer BM.Advances in radiation oncology · 2025Article
- Article
- Artificial intelligence in the task of segmentation and classification of brain metastases images: current challenges and future opportunities.Frontiers in neurology · 2025Review
- Outcomes of Management of Progressive Radiosurgery-Treated Brain Metastasis With Resection Followed by Pathology-Informed Management: A Retrospective Study.Neurosurgery practice · 2024Article
- Brain Tumor Recurrence vs. Radiation Necrosis Classification and Patient Survivability Prediction.IEEE journal of biomedical and health informatics · 2024Article
- Leveraging radiomics and machine learning to differentiate radiation necrosis from recurrence in patients with brain metastases.Journal of neuro-oncology · 2024Article
- Prediction of treatment response after stereotactic radiosurgery of brain metastasis using deep learning and radiomics on longitudinal MRI data.Scientific reports · 2024Article
- Computational Modeling and AI in Radiation Neuro-Oncology and Radiosurgery.Advances in experimental medicine and biology · 2024Review
- Article
- Article
- Recognition and Management of the Long-term Effects of Cranial Radiation.Current treatment options in oncology · 2023Review
- Assessment of brain cancer atlas maps with multimodal imaging features.Journal of translational medicine · 2023Review
- Review
- Application of artificial intelligence to stereotactic radiosurgery for intracranial lesions: detection, segmentation, and outcome prediction.Journal of neuro-oncology · 2023Review
- Towards updated understanding of brain metastasis.American journal of cancer research · 2022Review
- Radiomics as an emerging tool in the management of brain metastases.Neuro-oncology advancesReview
Corrections and comments
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Authors and funding
10 authors at 5 institutions in 1 country.
Funding
Abstract
backgroundStereotactic radiosurgery (SRS) may cause radiation necrosis (RN) that is difficult to distinguish from tumor progression (TP) by conventional MRI. We hypothesize that MRI-based multiparametric radiomics (mpRad) and machine learning (ML) can differentiate TP from RN in a multi-institutional cohort.
methodsPatients with growing brain metastases after SRS at 2 institutions underwent surgery, and RN or TP were confirmed by histopathology. A radiomic tissue signature (RTS) was selected from mpRad, as well as single T
resultsOne hundred and thirty-five discrete lesions (37 RN, 98 TP) from 109 patients were included. Radiographic diagnoses by an experienced neuroradiologist were concordant with histopathology in 67% of cases (sensitivity 69%, specificity 59% for TP). Radiomic analysis indicated institutional origin as a significant confounding factor for diagnosis. A random forest model incorporating 1 mpRad, 4 T
conclusionsMRI-based mpRad and ML can distinguish TP from RN with high specificity, which may facilitate the triage of patients with growing brain metastases after SRS for repeat radiation versus surgical intervention.
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