SynthesisEuropean radiology2023
Diagnostic accuracy of CT and MR features for detecting atypical lipomatous tumors and malignant liposarcomas: a systematic review and meta-analysis.
Synthesis in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 17 citations in OpenAlex.
- Soft Tissue Reporting and Data System (Soft Tissue-RADS): preliminary validation among fellowship-trained musculoskeletal radiologists.European radiology · 2026Article
- MRI of soft tissue tumours: a structured "8S framework" aligned with WHO 2020 classification.Skeletal radiology · 2026Review
- Fat-containing soft-tissue tumors: Imaging findings and pathologic correlation.Skeletal radiology · 2026Review
- Artificial Intelligence in Oncology: A Comprehensive Cross-Cancer Translational Readiness Analysis Across 18 Malignancies.Cancers · 2026Review
- [Soft tissue sarcoma of the extremities-imaging, biopsy planning, and pitfalls in radiology].Radiologie (Heidelberg, Germany) · 2026Review
- Case Report: A giant dedifferentiated liposarcoma in the retroperitoneum.Frontiers in oncology · 2026Article
- Atypical lipomatous tumor/well-differentiated liposarcoma in the subcostal space of the chest wall: a case report and review of the literature.Journal of cardiothoracic surgery · 2025Review
- Incidental Hibernoma With an Atypical Presentation: Diagnostic Approach, Pathologic Correlation, and Lessons Learned.Cureus · 2025Article
- Bayesian additive regression trees for machine learning to classify benign vs atypical lipomatous tumors on MRI.Radiology advances · 2025Article
- Diagnostic work-up of lipomatous tumors: a decision-making analysis among European sarcoma centers.Insights into imaging · 2025Article
- CT-based radiomics nomogram for differentiating dedifferentiated liposarcoma from well-differentiated liposarcoma.Frontiers in oncology · 2025Article
- Multi-center external validation of an automated method segmenting and differentiating atypical lipomatous tumors from lipomas using radiomics and deep-learning on MRI.EClinicalMedicine · 2024Article
- MRI Differential Diagnosis and Guidance for Puncture Biopsy of Musculoskeletal Dedifferentiated Liposarcoma and Well Differentiated Liposarcoma.Cancer management and research · 2024Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis systematic review and meta-analysis evaluated the diagnostic accuracy of CT and MRI for differentiating atypical lipomatous tumors and malignant liposarcomas from benign lipomatous lesions.
methodsMEDLINE, EMBASE, Scopus, the Cochrane Library, and the gray literature from inception to January 2022 were systematically evaluated. Original studies with > 5 patients evaluating the accuracy of CT and/or MRI for detecting liposarcomas with a histopathological reference standard were included. Meta-analysis was performed using a bivariate mixed-effects regression model. Risk of bias was evaluated using Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). This study is registered on PROSPERO, number CRD42022306479.
resultsTwenty-six studies with a total of 2613 patients were included. Mean/median reported patient ages ranged between 50 and 63 years. The summary sensitivity and specificity of radiologist gestalt for detecting liposarcomas was 85% (79-90% 95% CI) and 63% (52-72%), respectively. Deep depth to fascia, thickened septations, enhancing components, and lesion size (≥ 10 cm) all demonstrated sensitivities ≥ 85%. Other imaging characteristics including heterogenous/amorphous signal intensity, irregular tumor margin, and nodules present demonstrated lower sensitivities ranging from 43 to 65%. Inter-reader reliability for radiologist gestalt within studies ranged from fair to substantial (k = 0.23-0.7). Risk of bias was predominantly mixed for patient selection, low for index test and reference standard, and unclear for flow and timing.
conclusionHigher sensitivities for detecting liposarcomas were achieved with radiologist gestalt, deep depth to fascia, thickened septations, enhancing components, and large size. Combined clinical and imaging scoring and/or radiomics both show promise for optimal performance, though require further analysis with prospective study designs. CLINICAL RELEVANCE: This pooled analysis evaluates the accuracy of CT and MRI for detecting atypical lipomatous tumors and malignant liposarcomas. Radiologist gestalt, deep depth to fascia, thickened septations, enhancing components, and large size demonstrate the highest overall sensitivities. KEY POINTS: • The summary sensitivity and specificity of radiologist gestalt for detecting liposarcomas was 85% (79-90% 95% CI) and 63% (52-72%), respectively. • Radiologist gestalt, deep depth to fascia, thickened septations, enhancing components, and large tumor size (≥ 10 cm) showed the highest sensitivities for detecting atypical lipomatous tumors/well-differentiated liposarcomas and malignant liposarcomas. • A combined clinical and imaging scoring system and/or radiomics is likely to provide the best overall diagnostic accuracy, although currently proposed scoring systems and radiomic feature analysis require further study with prospective study designs.
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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.