ArticleEuropean radiology2018
Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study.
Article in European radiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers, 4 of them syntheses that pooled it.
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Who cites it
73 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Imaging of cancer of unknown primary: a systematic literature review of the past, present, and future.The British journal of radiology · 2025Pooled it
- Brain metastases from breast cancer using magnetic resonance imaging: A systematic review.Journal of medical radiation sciences · 2024Pooled it
- Novel cancer therapies for advanced cutaneous melanoma: The added value of radiomics in the decision making process-A systematic review.Cancer medicine · 2020Pooled it
- Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics.European journal of nuclear medicine and molecular imaging · 2019Pooled it
- MRI Based Radiomics as an Imaging Biomarker for Locally Advanced Carcinoma Rectum: Predicting Tumor Response Following Neoadjuvant Chemoradiotherapy as an Organ Preservation Strategy.Cancer reports (Hoboken, N.J.) · 2026Article
- Modality-level attribution and redundancy-aware radiomics for MRI-based differentiation of melanoma and NSCLC brain metastases.Frontiers in oncology · 2026Article
- A large scale multi-dataset investigation of brain metastases distribution based on primary cancer type.BMC cancer · 2025Article
- Diagnostic, Therapeutic, and Prognostic Applications of Artificial Intelligence (AI) in the Clinical Management of Brain Metastases (BMs).Brain sciences · 2025Review
- Using Machine Learning on MRI Radiomics to Diagnose Parotid Tumours Before Comparing Performance with Radiologists: A Pilot Study.Journal of imaging informatics in medicine · 2025Article
- Multiparametric-MRI habitat radiomics analysis for discriminating pathological types of brain metastases.Frontiers in oncology · 2025Article
- Artificial intelligence in the task of segmentation and classification of brain metastases images: current challenges and future opportunities.Frontiers in neurology · 2025Review
- Topological embedding and directional feature importance in ensemble classifiers for multi-class classification.Computational and structural biotechnology journal · 2024Article
- A predictive model based on radiomics, clinical features, and pathologic indicators for disease-free survival after liver transplantation for hepatocellular carcinoma: a 7-year retrospective study.Journal of gastrointestinal oncology · 2024Article
- Radiomics-based lymph nodes prognostic models from three MRI regions in nasopharyngeal carcinoma.Heliyon · 2024Article
- Article
- Advances in Neuro-Oncological Imaging: An Update on Diagnostic Approach to Brain Tumors.Cancers · 2024Review
- Magnetic resonance-based imaging biopsy with signatures including topological Betti number features for prediction of primary brain metastatic sites.Physical and engineering sciences in medicine · 2023Article
- Development and Validation of a Radiomics Nomogram for Liver Metastases Originating from Gastric and Colorectal Cancer.Diagnostics (Basel, Switzerland) · 2023Article
- Computed tomography-based radiomics machine learning classifiers to differentiate type I and type II epithelial ovarian cancers.European radiology · 2023Article
- Novel radiomic features versus deep learning: differentiating brain metastases from pathological lung cancer types in small datasets.The British journal of radiology · 2023Article
13 more citing papers are in PubMed but not listed here.
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5 authors.
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Abstract
objectiveTo examine the capability of MRI texture analysis to differentiate the primary site of origin of brain metastases following a radiomics approach.
methodsSixty-seven untreated brain metastases (BM) were found in 3D T1-weighted MRI of 38 patients with cancer: 27 from lung cancer, 23 from melanoma and 17 from breast cancer. These lesions were segmented in 2D and 3D to compare the discriminative power of 2D and 3D texture features. The images were quantized using different number of gray-levels to test the influence of quantization. Forty-three rotation-invariant texture features were examined. Feature selection and random forest classification were implemented within a nested cross-validation structure. Classification was evaluated with the area under receiver operating characteristic curve (AUC) considering two strategies: multiclass and one-versus-one.
resultsIn the multiclass approach, 3D texture features were more discriminative than 2D features. The best results were achieved for images quantized with 32 gray-levels (AUC = 0.873 ± 0.064) using the top four features provided by the feature selection method based on the p-value. In the one-versus-one approach, high accuracy was obtained when differentiating lung cancer BM from breast cancer BM (four features, AUC = 0.963 ± 0.054) and melanoma BM (eight features, AUC = 0.936 ± 0.070) using the optimal dataset (3D features, 32 gray-levels). Classification of breast cancer and melanoma BM was unsatisfactory (AUC = 0.607 ± 0.180).
conclusionVolumetric MRI texture features can be useful to differentiate brain metastases from different primary cancers after quantizing the images with the proper number of gray-levels. KEY POINTS: • Texture analysis is a promising source of biomarkers for classifying brain neoplasms. • MRI texture features of brain metastases could help identifying the primary cancer. • Volumetric texture features are more discriminative than traditional 2D texture features.
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