ArticleScientific reports2021
Comparison of radiomic feature aggregation methods for patients with multiple tumors.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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30 citing papers in PubMed, 45 citations in OpenAlex.
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- Optimizing anisotropic margins in single-isocenter multiple brain metastases radiosurgery using regressor strategies: A multi-institutional validation study.Journal of applied clinical medical physics · 2025Article
- Emerging Trends in Artificial Intelligence in Neuro-Oncology.Current oncology reports · 2025Review
- Imaging-derived biomarkers fromCancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Radiomics analysis for prediction and classification of submucosal tumors based on gastrointestinal endoscopic ultrasonography.DEN open · 2025Article
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- Evaluating the prognostic value of radiomics and clinical features in metastatic prostate cancer using [Physical and engineering sciences in medicine · 2025Article
- Radiomic-based prediction of lesion-specific systemic treatment response in metastatic disease.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2024Article
- Radiomic-Based Prediction of Lesion-Specific Systemic Treatment Response in Metastatic Disease.medRxiv : the preprint server for health sciences · 2024Article
- Review on radiomic analysis inCancer imaging : the official publication of the International Cancer Imaging Society · 2024Review
- Radiomic-Based Approaches in the Multi-metastatic Setting: A Quantitative Review.medRxiv : the preprint server for health sciences · 2024Article
- The radiomics nomogram predicts the prognosis of pancreatic cancer patients with hepatic metastasis after chemoimmunotherapy.Cancer immunology, immunotherapy : CII · 2024Article
- Classification of multiple primary lung cancer in patients with multifocal lung cancer: assessment of a machine learning approach using multidimensional genomic data.Frontiers in oncology · 2024Article
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- Prostate Region-Wise Imaging Biomarker Profiles for Risk Stratification and Biochemical Recurrence Prediction.Cancers · 2023Article
- A pre-treatment CT-based weighted radiomic approach combined with clinical characteristics to predict durable clinical benefits of immunotherapy in advanced lung cancer.European radiology · 2023Article
- 3D Capsule Networks for Brain Image Segmentation.AJNR. American journal of neuroradiology · 2023Article
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8 authors at 4 institutions in 1 country.
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Abstract
Radiomic feature analysis has been shown to be effective at analyzing diagnostic images to model cancer outcomes. It has not yet been established how to best combine radiomic features in cancer patients with multifocal tumors. As the number of patients with multifocal metastatic cancer continues to rise, there is a need for improving personalized patient-level prognosis to better inform treatment. We compared six mathematical methods of combining radiomic features of 3,596 tumors in 831 patients with multiple brain metastases and evaluated the performance of these aggregation methods using three survival models: a standard Cox proportional hazards model, a Cox proportional hazards model with LASSO regression, and a random survival forest. Across all three survival models, the weighted average of the largest three metastases had the highest concordance index (95% confidence interval) of 0.627 (0.595-0.661) for the Cox proportional hazards model, 0.628 (0.591-0.666) for the Cox proportional hazards model with LASSO regression, and 0.652 (0.565-0.727) for the random survival forest model. This finding was consistent when evaluating patients with different numbers of brain metastases and different tumor volumes. Radiomic features can be effectively combined to estimate patient-level outcomes in patients with multifocal brain metastases. Future studies are needed to confirm that the volume-weighted average of the largest three tumors is an effective method for combining radiomic features across other imaging modalities and tumor types.
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