ArticleAdvances in radiation oncology
MRI Radiomic Features Are Independently Associated With Overall Survival in Soft Tissue Sarcoma.
Article in Advances in radiation oncology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it.
- MRI Volumetrics and Image Texture Analysis in Assessing Systemic Treatment Response in Extra-Abdominal Desmoid Fibromatosis.Radiology. Imaging cancer · 2021Pooled it
- MRI-Based radiomic signature for predicting pathologic treatment response to neoadjuvant chemoradiotherapy and radioimmunotherapy in soft tissue sarcoma.Skeletal radiology · 2026Article
- MRI-Based Delta Necrosis as a Prognostic Marker Following Neoadjuvant Chemotherapy in Soft Tissue Sarcoma.Cancers · 2026Article
- Deep Subspace Reconstruction-Driven Adaptive Canonical Correlation Analysis Model for Identifying Imaging Genetics Associations in Sarcoma.Applied biochemistry and biotechnology · 2025Article
- Integrating Radiogenomics and Machine Learning in Musculoskeletal Oncology Care.Diagnostics (Basel, Switzerland) · 2025Review
- Heterogeneity in response to neoadjuvant radiotherapy between soft tissue sarcoma histotypes: associations between radiology and pathology findings.European radiology · 2025Article
- What Is the Prevalence and Fate of Myxoid Soft-Tissue Tumors With an Indeterminate Diagnosis Prior to Resection?Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews · 2025Article
- Intratumoral habitat and peritumor radiomics for progression risk stratification of patients with soft tissue sarcoma: a multicenter study.Frontiers in oncology · 2025Article
- Prediction of Bone Marrow Metastases Using Computed Tomography (CT) Radiomics in Patients with Gastric Cancer: Uncovering Invisible Metastases.Diagnostics (Basel, Switzerland) · 2024Article
- Article
- Multi-institutional validation of a radiomics signature for identification of postoperative progression of soft tissue sarcoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- The impact of radiomics in the management of soft tissue sarcoma.Discover oncology · 2024Review
- The Molecular Twin artificial-intelligence platform integrates multi-omic data to predict outcomes for pancreatic adenocarcinoma patients.Nature cancer · 2024Article
- Repeatability, reproducibility, and the effects of radiotherapy on radiomic features of lowfield MR-LINAC images of the prostate.Frontiers in oncology · 2024Article
- Artificial Intelligence and Precision Medicine: A New Frontier for the Treatment of Brain Tumors.Life (Basel, Switzerland) · 2022Review
- Radiomics of Musculoskeletal Sarcomas: A Narrative Review.Journal of imaging · 2022Review
- Prognosis prediction of extremity and trunk wall soft-tissue sarcomas treated with surgical resection with radiomic analysis based on random survival forest.Updates in surgery · 2022Article
- PET/MR fusion texture analysis for the clinical outcome prediction in soft-tissue sarcoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2022Article
- Deep Learning Radiomics Nomogram to Predict Lung Metastasis in Soft-Tissue Sarcoma: A Multi-Center Study.Frontiers in oncology · 2022Article
- Virtual Biopsy in Soft Tissue Sarcoma. How Close Are We?Frontiers in oncology · 2022Review
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeSoft tissue sarcomas (STS) represent a heterogeneous group of diseases, and selection of individualized treatments remains a challenge. The goal of this study was to determine whether radiomic features extracted from magnetic resonance (MR) images are independently associated with overall survival (OS) in STS. METHODS AND MATERIALS: This study analyzed 2 independent cohorts of adult patients with stage II-III STS treated at center 1 (N = 165) and center 2 (N = 61). Thirty radiomic features were extracted from pretreatment T1-weighted contrast-enhanced MR images. Prognostic models for OS were derived on the center 1 cohort and validated on the center 2 cohort. Clinical-only (C), radiomics-only (R), and clinical and radiomics (C+R) penalized Cox models were constructed. Model performance was assessed using Harrell's concordance index.
resultsIn the R model, tumor volume (hazard ratio [HR], 1.5) and 4 texture features (HR, 1.1-1.5) were selected. In the C+R model, both age (HR, 1.4) and grade (HR, 1.7) were selected along with 5 radiomic features. The adjusted c-indices of the 3 models ranged from 0.68 (C) to 0.74 (C+R) in the derivation cohort and 0.68 (R) to 0.78 (C+R) in the validation cohort. The radiomic features were independently associated with OS in the validation cohort after accounting for age and grade (HR, 2.4;
conclusionsThis study found that radiomic features extracted from MR images are independently associated with OS when accounting for age and tumor grade. The overall predictive performance of 3-year OS using a model based on clinical and radiomic features was replicated in an independent cohort. Optimal models using clinical and radiomic features could improve personalized selection of therapy in patients with STS.
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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.