ArticleJournal of cancer research and clinical oncology2024
A subregion-based RadioFusionOmics model discriminates between grade 4 astrocytoma and glioblastoma on multisequence MRI.
Article in Journal of cancer research and clinical oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Predicting early recurrence of craniopharyngioma using multi-omics radiomic modeling: a retrospective cohort study.Neurosurgical review · 2026Article
- Cellularity habitat-based MRI radiomics for non-invasive grading and IDH mutation prediction in adult-type diffuse glioma.BMC medical imaging · 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
- Habitat Radiomics Analysis Based on Non-contrast CT in Differentiation of Parotid Pleomorphic Adenoma and AdenolymphomaCurrent medical imaging · 2026Article
- A novel MRI-based habitat analysis and deep learning for predicting perineural invasion in prostate cancer: a two-center study.BMC cancer · 2025Article
- Predicting the molecular subtypes of 2021 WHO grade 4 glioma by a multiparametric MRI-based machine learning model.BMC cancer · 2025Article
- MRI-based habitat imaging predicts high-risk molecular subtypes and early risk assessment of lower-grade gliomas.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Application of machine learning based on habitat imaging and vision transformer to predict treatment response of locally advanced esophageal squamous cell carcinoma following neoadjuvant chemoimmunotherapy: a multi-center study.Frontiers in immunology · 2025Article
- Habitat Analysis in Tumor Imaging: Advancing Precision Medicine Through Radiomic Subregion Segmentation.Cancer management and research · 2025Review
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Authors and funding
8 authors.
Funding
Abstract
purposeTo explore a subregion-based RadioFusionOmics (RFO) model for discrimination between adult-type grade 4 astrocytoma and glioblastoma according to the 2021 WHO CNS5 classification.
methods329 patients (40 grade 4 astrocytomas and 289 glioblastomas) with histologic diagnosis was retrospectively collected from our local institution and The Cancer Imaging Archive (TCIA). The volumes of interests (VOIs) were obtained from four multiparametric MRI sequences (T
resultsThe two best VOIs were manual VOI
conclusionThe performance of subregions defined by clustering was comparable to that of subregions that were manually defined. Fusion of features from the edematous subregions of multiple MRI sequences by the RFO model resulted in differentiation between grade 4 astrocytoma and glioblastoma.
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