ArticleNeuro-oncology2020
Use of radiomics for the prediction of local control of brain metastases after stereotactic radiosurgery.
Article in Neuro-oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers.
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Who cites it
64 citing papers in PubMed.
- Machine learning-based radiomics for histological classification of parotid tumors using morphological MRI: a comparative study.European radiology · 2022Trial
- Radiomics for risk stratification in brain metastases: potential in melanoma brain metastases.Clinical & experimental metastasis · 2026Article
- Radiomics using contrast-enhanced T1-weighted imaging and clinical features for predicting response to EGFR-TKIs in EGFR-mutated non-small cell lung cancer patients with brain metastases.BMC medical imaging · 2026Article
- Multimodal DeepSurv model with SHAP-based interpretation for predicting local failure in patients with brain metastases undergoing hypofractionated stereotactic radiotherapy.Frontiers in neuroscience · 2026Article
- Therapeutic hybrid intelligence with neural and knowledge-based expert reasoning for SRS: an AI model for breast cancer brain metastases.Frontiers in oncology · 2026Article
- Artificial Intelligence in the Diagnosis and Treatment of Brain Gliomas.Biomedicines · 2025Review
- Assessment of outcomes and machine Learning-based models to predict local failure risk following stereotactic radiosurgery for small brain metastases.Journal of neuro-oncology · 2025Article
- Hybrid Deep Learning for Survival Prediction in Brain Metastases Using Multimodal MRI and Clinical Data.Diagnostics (Basel, Switzerland) · 2025Article
- Prediction of Pituitary Adenoma's Volumetric Response to Gamma Knife Radiosurgery Using Machine Learning-Supported MRI Radiomics.Journal of clinical medicine · 2025Article
- Prediction of brain metastasis progression after stereotactic radiosurgery: sensitivity to changing the definition of progression.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Precision radiotherapy with molecular-profiling of CNS tumours.Journal of neuro-oncology · 2025Review
- Early characterization and prediction of glioblastoma and brain metastasis treatment efficacy using medical imaging-based radiomics and artificial intelligence algorithms.Frontiers in oncology · 2025Review
- Research progress in artificial intelligence for brain metastases.Frontiers in medicine · 2025Review
- Predicting local control of brain metastases after stereotactic radiotherapy with clinical, radiomics and deep learning features.Radiation oncology (London, England) · 2024Article
- Dose-Incorporated Deep Ensemble Learning for Improving Brain Metastasis Stereotactic Radiosurgery Outcome Prediction.International journal of radiation oncology, biology, physics · 2024Article
- Radiomics-based prediction of local control in patients with brain metastases following postoperative stereotactic radiotherapy.Neuro-oncology · 2024Article
- Morphological MRI features as prognostic indicators in brain metastases.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Recent Outcomes and Challenges of Artificial Intelligence, Machine Learning, and Deep Learning in Neurosurgery.World neurosurgery: X · 2024Review
- Prediction of treatment response after stereotactic radiosurgery of brain metastasis using deep learning and radiomics on longitudinal MRI data.Scientific reports · 2024Article
- Pretreatment Spatially Aware Magnetic Resonance Imaging Radiomics Can Predict Distant Brain Metastases (DBMs) After Stereotactic Radiosurgery/Radiation Therapy (SRS/SRT).Advances in radiation oncology · 2024Article
4 more citing papers are in PubMed but not listed here.
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9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundLocal response prediction for brain metastases (BM) after stereotactic radiosurgery (SRS) is challenging, particularly for smaller BM, as existing criteria are based solely on unidimensional measurements. This investigation sought to determine whether radiomic features provide additional value to routinely available clinical and dosimetric variables to predict local recurrence following SRS.
methodsAnalyzed were 408 BM in 87 patients treated with SRS. A total of 440 radiomic features were extracted from the tumor core and the peritumoral regions, using the baseline pretreatment volumetric post-contrast T1 (T1c) and volumetric T2 fluid-attenuated inversion recovery (FLAIR) MRI sequences. Local tumor progression was determined based on Response Assessment in Neuro-Oncology‒BM criteria, with a maximum axial diameter growth of >20% on the follow-up T1c indicating local failure. The top radiomic features were determined based on resampled random forest (RF) feature importance. An RF classifier was trained using each set of features and evaluated using the area under the receiver operating characteristic curve (AUC).
resultsThe addition of any one of the top 10 radiomic features to the set of clinical features resulted in a statistically significant (P < 0.001) increase in the AUC. An optimized combination of radiomic and clinical features resulted in a 19% higher resampled AUC (mean = 0.793; 95% CI = 0.792-0.795) than clinical features alone (0.669, 0.668-0.671).
conclusionsThe increase in AUC of the RF classifier, after incorporating radiomic features, suggests that quantitative characterization of tumor appearance on pretreatment T1c and FLAIR adds value to known clinical and dosimetric variables for predicting local failure.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.