ArticleRadiology2025
MRI-based Habitat Analysis for the Prediction of Progression-Free Survival in Primary Spinal Tumors.
Article in Radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Biomaterials for intervertebral disc regeneration: Niche reprogramming, precision therapeutics, and structural reconstruction.Bioactive materials · 2027Review
- Spectral CT-based habitat analysis for predicting pathologic response to neoadjuvant therapy in gastric cancer.European radiology · 2026Article
- ASO Author Reflections: CT-Based Nested Habitats Analysis for Prognostication of Hepatocellular Carcinoma-Capturing Intratumoral Heterogeneity to Predict Early Recurrence.Annals of surgical oncology · 2026Article
- CT-Based Nested Habitats Analysis for Early Recurrence Prediction and Risk Stratification in Hepatocellular Carcinoma: Development and Multicenter Validation Across Four Cohorts.Annals of surgical oncology · 2026Article
- A CT-based multi-scale fusion model with SHAP interpretation for preoperative differentiation between lung adenocarcinoma in situ/minimally invasive adenocarcinoma and invasive adenocarcinoma: a multicenter study.Frontiers in oncology · 2026Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Background Prediction of prognosis in patients with primary spinal tumors is critical for treatment planning but a clinical challenge. Purpose To develop an MRI-based nested habitat radiomics model to predict progression-free survival (PFS) in patients with primary spinal tumors. Materials and Methods This dual-center retrospective study analyzed patients with resected primary spinal tumors. Patients from March 2010 to August 2019 were assigned to a training set; from September 2019 to October 2022, to an internal test set; and from March 2010 to October 2022, to an external test set. Whole-tumor regions of interest were delineated on T1- and T2-weighted MRI scans. Nested habitat radiomic analysis was performed to locate aggressive subregions associated with a poorer prognosis. Initially, a support vector machine model classified tumors based on global features. Then, local radiomics features were extracted to generate probability maps to identify aggressive tumor subregions with use of
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Registered trials
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