ArticleCancers2021
Enhancement of Radiosurgical Treatment Outcome Prediction Using MRI Radiomics in Patients with Non-Small Cell Lung Cancer Brain Metastases.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of brain metastases from lung cancer using magnetic resonance neuroimaging: Clinical and technical aspects.Journal of medical radiation sciences · 2024Pooled it
- Trial
- Value of CT Radiomics and Clinical Features in Predicting Bone Metastases in Patients with NSCLC.Contrast media & molecular imaging · 2022Trial
- Predicting early response to ablative radiotherapy in oligometastatic disease: a scoping review of radiomics-based machine learning and deep learning models.European radiology · 2026Article
- Early predicting brain metastases of EGFR positive lung adenocarcinoma patients by CT radiomics.Physical and engineering sciences in medicine · 2026Article
- Prediction of Pituitary Adenoma's Volumetric Response to Gamma Knife Radiosurgery Using Machine Learning-Supported MRI Radiomics.Journal of clinical medicine · 2025Article
- Article
- Prediction of brain metastasis progression after stereotactic radiosurgery: sensitivity to changing the definition of progression.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Early characterization and prediction of glioblastoma and brain metastasis treatment efficacy using medical imaging-based radiomics and artificial intelligence algorithms.Frontiers in oncology · 2025Review
- Prediction of bone metastasis of prostate cancer based on intratumoral and peritumoral radiomics of MRI T2WI combined with ADC images.Frontiers in oncology · 2025Article
- Predicting local control of brain metastases after stereotactic radiotherapy with clinical, radiomics and deep learning features.Radiation oncology (London, England) · 2024Article
- Multiomics-Based Outcome Prediction in Personalized Ultra-Fractionated Stereotactic Adaptive Radiotherapy (PULSAR).Cancers · 2024Article
- Personalized prediction of immunotherapy response in lung cancer patients using advanced radiomics and deep learning.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Enhancing intracranial efficacy prediction of osimertinib in non-small cell lung cancer: a novel approach through brain MRI radiomics.Frontiers in neurology · 2024Article
- Radiomics of metastatic brain tumor as a predictive image biomarker of progression-free survival in patients with non-small-cell lung cancer with brain metastasis receiving tyrosine kinase inhibitors.Translational oncology · 2024Article
- Computational Modeling and AI in Radiation Neuro-Oncology and Radiosurgery.Advances in experimental medicine and biology · 2024Review
- Predicting stereotactic radiosurgery outcomes with multi-observer qualitative appearance labelling versus MRI radiomics.Scientific reports · 2023Article
- Predicting survival after radiosurgery in patients with lung cancer brain metastases using deep learning of radiomics and EGFR status.Physical and engineering sciences in medicine · 2023Article
- Application of artificial intelligence to stereotactic radiosurgery for intracranial lesions: detection, segmentation, and outcome prediction.Journal of neuro-oncology · 2023Review
- A radiomics-based deep learning approach to predict progression free-survival after tyrosine kinase inhibitor therapy in non-small cell lung cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023Article
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Authors and funding
9 authors.
Funding
Abstract
The diagnosis of brain metastasis (BM) is commonly observed in non-small cell lung cancer (NSCLC) with poor outcomes. Accordingly, developing an approach to early predict BM response to Gamma Knife radiosurgery (GKRS) may benefit the patient treatment and monitoring. A total of 237 NSCLC patients with BMs (for survival prediction) and 256 patients with 976 BMs (for prediction of local tumor control) treated with GKRS were retrospectively analyzed. All the survival data were recorded without censoring, and the status of local tumor control was determined by comparing the last MRI follow-up in patients' lives with the pre-GKRS MRI. Overall 1763 radiomic features were extracted from pre-radiosurgical magnetic resonance images. Three prediction models were constructed, using (1) clinical data, (2) radiomic features, and (3) clinical and radiomic features. Support vector machines with a 30% hold-out validation approach were constructed. For treatment outcome predictions, the models derived from both the clinical and radiomics data achieved the best results. For local tumor control, the combined model achieved an area under the curve (AUC) of 0.95, an accuracy of 90%, a sensitivity of 91%, and a specificity of 89%. For patient survival, the combined model achieved an AUC of 0.81, an accuracy of 77%, a sensitivity of 78%, and a specificity of 80%. The pre-radiosurgical radiomics data enhanced the performance of local tumor control and survival prediction models in NSCLC patients with BMs treated with GRKS. An outcome prediction model based on radiomics combined with clinical features may guide therapy in these patients.
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