ArticleCancers2021
Prognostic Assessment in High-Grade Soft-Tissue Sarcoma Patients: A Comparison of Semantic Image Analysis and Radiomics.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Integrating radiomics, artificial intelligence, and molecular signatures in bone and soft tissue tumors: advances in diagnosis and prognostication.Frontiers in oncology · 2025Pooled it
- Radiologic response assessment in patients with desmoid-type fibromatosis treated with percutaneous cryoablation.European radiology experimental · 2026Article
- MRI features outperform histologic grade for outcome prediction in translocation-driven enriched soft-tissue sarcoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Radiomics of soft tissue sarcoma metastases to assess prognostic factors related to intrapatient intertumor heterogeneity.Translational cancer research · 2026Article
- Radiomics-based diagnosis of patellar chondromalacia using sagittal T2-weighted images.Radiologie (Heidelberg, Germany) · 2025Article
- Radiomics-based prediction of local control in patients with brain metastases following postoperative stereotactic radiotherapy.Neuro-oncology · 2024Article
- CT-based radiomics for predicting breast cancer radiotherapy side effects.Scientific reports · 2024Article
- Integration of pre-treatment computational radiomics, deep radiomics, and transcriptomics enhances soft-tissue sarcoma patient prognosis.NPJ precision oncology · 2024Article
- The impact of radiomics in the management of soft tissue sarcoma.Discover oncology · 2024Review
- CT and MRI radiomics of bone and soft-tissue sarcomas: an updated systematic review of reproducibility and validation strategies.Insights into imaging · 2024Article
- MR histology reveals tissue features beneath heterogeneous MRI signal in genetically engineered mouse models of sarcoma.Frontiers in oncology · 2024Article
- Current Status and Future of Artificial Intelligence in MM Imaging: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Predicting Soft Tissue Sarcoma Response to Neoadjuvant Chemotherapy Using an MRI-Based Delta-Radiomics Approach.Molecular imaging and biology · 2023Article
- Development of PSMA-PET-guided CT-based radiomic signature to predict biochemical recurrence after salvage radiotherapy.European journal of nuclear medicine and molecular imaging · 2023Article
- Radiomics and Radiogenomics in Pelvic Oncology: Current Applications and Future Directions.Current oncology (Toronto, Ont.) · 2023Review
- Development and Evaluation of MR-Based Radiogenomic Models to Differentiate Atypical Lipomatous Tumors from Lipomas.Cancers · 2023Article
- Spatial assessments in texture analysis: what the radiologist needs to know.Frontiers in radiology · 2023Review
- Article
- The diagnosis, classification, and treatment of sarcoma in this era of artificial intelligence and immunotherapy.Cancer communications (London, England) · 2022Review
- Review
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19 authors.
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Abstract
backgroundIn patients with soft-tissue sarcomas of the extremities, the treatment decision is currently regularly based on tumor grading and size. The imaging-based analysis may pose an alternative way to stratify patients' risk. In this work, we compared the value of MRI-based radiomics with expert-derived semantic imaging features for the prediction of overall survival (OS).
methodsFat-saturated T2-weighted sequences (T2FS) and contrast-enhanced T1-weighted fat-saturated (T1FSGd) sequences were collected from two independent retrospective cohorts (training: 108 patients; testing: 71 patients). After preprocessing, 105 radiomic features were extracted. Semantic imaging features were determined by three independent radiologists. Three machine learning techniques (elastic net regression (ENR), least absolute shrinkage and selection operator, and random survival forest) were compared to predict OS.
resultsENR models achieved the best predictive performance. Histologies and clinical staging differed significantly between both cohorts. The semantic prognostic model achieved a predictive performance with a C-index of 0.58 within the test set. This was worse compared to a clinical staging system (C-index: 0.61) and the radiomic models (C-indices: T1FSGd: 0.64, T2FS: 0.63). Both radiomic models achieved significant patient stratification.
conclusionsT2FS and T1FSGd-based radiomic models outperformed semantic imaging features for prognostic assessment.
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