ArticleStatistics in medicine2020
Tumor heterogeneity estimation for radiomics in cancer.
Article in Statistics in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed, 38 citations in OpenAlex.
- Radiomic Parameters for the Evaluation of Response to Treatment in Metastatic Colorectal Cancer Patients with Liver Metastasis: Findings from the CAVE-GOIM mCRC Phase 2 Trial.Clinical drug investigation · 2024Trial
- Multi-b-value diffusion-weighted imaging-based habitat imaging for preoperative prediction of histological grade and prognosis in soft tissue sarcoma: focusing on intratumoral heterogeneity.Quantitative imaging in medicine and surgery · 2026Article
- Multiomics Machine Learning to Predict Neoadjuvant Chemotherapy Outcome and Relapse of Breast Cancer.BME frontiers · 2026Article
- Perspective on the integration of radiomics and spatial omics in the analysis of the tumor microenvironment of bladder cancer and prospects for precision diagnosis and treatment.Frontiers in immunology · 2026Review
- Interpretable multimodal MRI radiomics for predicting neoadjuvant chemotherapy response in nasopharyngeal carcinoma.BMC medical imaging · 2025Article
- Pathology-interpretable radiomic model for predicting clinical outcome in patients with osteosarcoma: a retrospective, multicenter study.BMC medicine · 2025Article
- Graph neural network model using radiomics for lung CT image segmentation.Scientific reports · 2025Article
- Non-contrast computed tomography radiomics model to predict benign and malignant thyroid nodules with lobe segmentation: A dual-center study.World journal of radiology · 2025Article
- Spatial-temporal radiogenomics in predicting neoadjuvant chemotherapy efficacy for breast cancer: a comprehensive review.Journal of translational medicine · 2025Review
- Deep learning MRI-based radiomic models for predicting recurrence in locally advanced nasopharyngeal carcinoma after neoadjuvant chemoradiotherapy: a multi-center study.Clinical & experimental metastasis · 2025Article
- Enhancing diagnostic frameworks in pancreatic cancer imaging: A critical appraisal.World journal of radiology · 2025Article
- Radiomics to predict tumor response to combination chemoradiotherapy in squamous cell carcinoma of the anal canal: a preliminary investigation.European radiology experimental · 2025Article
- Deep learning-based intratumoral and peritumoral features for differentiating ocular adnexal lymphoma and idiopathic orbital inflammation.European radiology · 2025Article
- Radiomic Analysis of Magnetic Resonance Imaging for Breast Cancer withDiagnostics (Basel, Switzerland) · 2025Article
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- radioGWAS links radiome to genome to discover driver genes with somatic mutations for heterogeneous tumor image phenotype in pancreatic cancer.Scientific reports · 2024Article
- Quantitative analysis of lung lesions using unenhanced chest computed tomography images.The clinical respiratory journal · 2024Article
- Hemodynamic property incorporated brain tumor segmentation by deep learning and density-based analysis of dynamic susceptibility contrast-enhanced magnetic resonance imaging (MRI).Quantitative imaging in medicine and surgery · 2024Article
- Brain tumour microstructure is associated with post-surgical cognition.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 1 country.
Funding
Abstract
Radiomics is an emerging field of medical image analysis research where quantitative measurements are obtained from radiological images that can be utilized to predict patient outcomes and inform treatment decisions. Cancer patients routinely undergo radiological evaluations when images of various modalities including computed tomography, positron emission tomography, and magnetic resonance images are collected for diagnosis and for evaluation of disease progression. Tumor characteristics, often referred to as measures of tumor heterogeneity, can be computed using these clinical images and used as predictors of disease progression and patient survival. Several approaches for quantifying tumor heterogeneity have been proposed, including intensity histogram-based measures, shape and volume-based features, and texture analysis. Taking into account the topology of the tumors we propose a statistical framework for estimating tumor heterogeneity using clustering based on Markov random field theory. We model the voxel intensities using a Gaussian mixture model using a Gibbs prior to incorporate voxel neighborhood information. We propose a novel approach to choosing the number of mixture components. Subsequently, we show that the proposed procedure outperforms the existing approaches when predicting lung cancer survival.
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Registered trials
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.