ArticleRadiology. Artificial intelligence2020
Tumor Habitat-derived Radiomic Features at Pretreatment MRI That Are Prognostic for Progression-free Survival in Glioblastoma Are Associated with Key Morphologic Attributes at Histopathologic Examination: A Feasibility Study.
Article in Radiology. Artificial intelligence, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 1 of them a synthesis that pooled it.
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Who cites it
55 citing papers in PubMed, 1 synthesis or guideline pooled it, 71 citations in OpenAlex.
- Systematic review and epistemic meta-analysis to advance binomial AI-radiomics integration for predicting high-grade glioma progression and enhancing patient management.Scientific reports · 2025Pooled it
- Comparative study of radiomics features between ASL and DSC perfusion-weighted imaging in distinguishing radiation-induced brain injury from glioma recurrence.Neuroradiology · 2026Article
- Precision Risk Stratification in Prostate Cancer: Unveiling MRI-Based Habitat-Driven Radiomics Model for Enhanced Treatment Guidance.Journal of imaging informatics in medicine · 2026Article
- Interpretable habitat radiomics based nomogram for predicting T790M mutation in non-small cell lung cancer with brain metastases.Quantitative imaging in medicine and surgery · 2026Article
- MRI-based perfusion-diffusion habitat analysis for characterizing intratumoral heterogeneity in rectal adenocarcinoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Advancing precision risk stratification in adult diffuse gliomas through DSC-MRI-based habitat analysis.BMC medical imaging · 2026Article
- Development and Validation of an Explainable MRI-Based Habitat Radiomics Model for Predicting p53-Abnormal Endometrial Cancer: A Multicentre Feasibility Study.Journal of imaging informatics in medicine · 2026Article
- Habitat-based CT radiomics profiling spatial-temporal heterogeneity in resectable NSCLC predict pathological response to neoadjuvant chemoimmunotherapy: a multi-center study.NPJ precision oncology · 2026Article
- PNET-PRISM: a multicenter-validated radiomics nomogram for noninvasive grading of pancreatic neuroendocrine tumors.Insights into imaging · 2026Article
- Radiomics in glioblastoma recurrence: advances in prediction, localization, and differentiation from treatment-related effects.Journal of translational medicine · 2026Review
- MRI-based habitat analysis for pathologic response prediction after neoadjuvant chemoradiotherapy in rectal cancer: a multicenter study.European radiology · 2026Article
- Habitat-based MRI radiomics for enhanced parkinson's diagnosis.Scientific reports · 2026Article
- Temporal-spatial evolution of tumor habitat analysis: a bibliometric study on research hotspots and trends in medical imaging (2014-2025).Translational cancer research · 2026Article
- Pre-treatment CEMRI habitat radiomics and deep learning for noninvasive prediction of the VETC pattern in hepatocellular carcinoma: an exploratory radiogenomic analysis.Frontiers in oncology · 2026Article
- Habitat-radiomics combining multichannel 2.5D deep learning for differentiating adrenal adenomas from metastases using automatic segmentation: a multicenter study.Frontiers in endocrinology · 2026Article
- Clinical value of habitat radiomics within the 2-cm edema zone surrounding the postoperative residual cavity in predicting glioma recurrence.Frontiers in oncology · 2026Article
- Risk stratification for metachronous metastasis in colon cancer based on landscape ecological analysis.Scientific reports · 2025Article
- MRI-based Habitat Analysis for the Prediction of Progression-Free Survival in Primary Spinal Tumors.Radiology · 2025Article
- Prediction of therapeutic response to transarterial chemoembolization plus systemic therapy regimen in hepatocellular carcinoma using pretreatment contrast-enhanced MRI based habitat analysis and Crossformer model.Abdominal radiology (New York) · 2025Article
- Habitat imaging radiomics increases the accuracy of a nomogram for predicting Ki-67-positivity in laryngeal squamous cell carcinoma.European journal of radiology open · 2025Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
purposeTo identify radiomic features extracted from the tumor habitat on routine MR images that are prognostic for progression-free survival (PFS) and to assess their morphologic basis with corresponding histopathologic attributes in glioblastoma (GBM). MATERIALS AND
methodsIn this retrospective study, 156 pretreatment GBM MR images (gadolinium-enhanced T1-weighted, T2-weighted, and fluid-attenuated inversion recovery [FLAIR] images) were curated. Of these 156 images, 122 were used for training (90 from The Cancer Imaging Archive and 32 from the Cleveland Clinic, acquired between December 1, 2011, and May 1, 2018) and 34 were used for validation. The validation set was obtained from the Ivy Glioblastoma Atlas Project database, for which the percentage extent of 11 histologic attributes was available on corresponding histopathologic specimens of the resected tumor. Following expert annotations of the tumor habitat (necrotic core, enhancing tumor, and FLAIR-hyperintense subcompartments), 1008 radiomic descriptors (eg, Haralick texture features, Laws energy features, co-occurrence of local anisotropic gradient orientations [CoLIAGe]) were extracted from the three MRI sequences. The top radiomic features were obtained from each subcompartment in the training set on the basis of their ability to risk-stratify patients according to PFS. These features were then concatenated to create a radiomics risk score (RRS). The RRS was independently validated on a holdout set. In addition, correlations (
resultsRRS yielded a concordance index of 0.80 on the validation set and constituted radiomic features, including Laws (capture edges, waves, ripple patterns) and CoLIAGe (capture disease heterogeneity) from enhancing tumor and FLAIR hyperintensity. These radiomic features were correlated with histopathologic attributes associated with disease aggressiveness in GBM, particularly tumor infiltration (
conclusionPreliminary findings demonstrated significant associations of prognostic radiomic features with disease-specific histologic attributes, with implications for risk-stratifying patients with GBM for personalized treatment decisions.
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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.