ArticleThe British journal of surgery2019
Radiomics approach to distinguish between well differentiated liposarcomas and lipomas on MRI.
Article in The British journal of surgery, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 3 of them syntheses that pooled it.
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Who cites it
52 citing papers in PubMed, 3 syntheses or guidelines pooled it, 84 citations in OpenAlex.
- Diagnostic accuracy of CT and MR features for detecting atypical lipomatous tumors and malignant liposarcomas: a systematic review and meta-analysis.European radiology · 2023Pooled it
- Radiomics-based approaches outperform visual analysis for differentiating lipoma from atypical lipomatous tumors: a review.Skeletal radiology · 2023Pooled it
- A Bayesian approach for diagnostic accuracy of malignant peripheral nerve sheath tumors: a systematic review and meta-analysis.Neuro-oncology · 2021Pooled it
- Automatic quantification of thermographic images of complex regional pain syndrome using radiomics and deep learning.Pain reports · 2026Article
- Predicting Failure of Active Surveillance in Desmoid-Type Fibromatosis Using Radiomics: An International Multi-center Cohort Study.Annals of surgical oncology · 2026Article
- Evaluation of Magnetic Resonance Imaging Features to Distinguish Lipomas from Atypical Lipomatous Tumours in the Extremities and Back.Diagnostics (Basel, Switzerland) · 2026Article
- Clinical Course and Dedifferentiation of Atypical Lipomatous Tumors: A Retrospective Analysis of Surveillance and Surgical Management.Annals of surgical oncology · 2026Article
- Fat-containing soft-tissue tumors: Imaging findings and pathologic correlation.Skeletal radiology · 2026Review
- Preoperative prediction of Ki-67 expression and prognosis in breast cancer based on contrast-enhanced cone-beam breast CT: a two-centre study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Laparoscopic resection for a mesenteric lipoma of the ascending colon: a case report.International journal of surgery case reports · 2026Article
- Giant Asymptomatic Intermuscular Lipoma of Right Thigh: Case Report and Review of Literature.Clinical case reports · 2026Article
- Evaluating shear wave elastography for differentiating lipomas from low to intermediate grade liposarcomas: is it reproducible and reliable?Skeletal radiology · 2025Article
- B-mode ultrasound and contrast-enhanced ultrasound-based radiomics interpretable analysis for the prediction of macrotrabecular-massive subtype of hepatocellular carcinoma.The ultrasound journal · 2025Article
- Evaluation of Diagnostic Accuracy of Preoperative CT-Based Radiomics in Primary Retroperitoneal Sarcoma.Annals of surgical oncology · 2025Article
- Predicting progression-free survival in sarcoma using MRI-based automatic segmentation models and radiomics nomograms: a preliminary multicenter study.Skeletal radiology · 2025Article
- Giant lipoma in the anterior region of the neck: A case report of a rare condition and review of literature.Radiology case reports · 2025Article
- Diagnostic work-up of lipomatous tumors: a decision-making analysis among European sarcoma centers.Insights into imaging · 2025Article
- Minimally interactive segmentation of soft-tissue tumors on CT and MRI using deep learning.European radiology · 2025Article
- Case Report: Giant Colonic Lipoma, a Rare Benign Tumor Mimicking Malignancy.Reports (MDPI) · 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
Corrections and comments
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Authors and funding
13 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWell differentiated liposarcoma (WDLPS) can be difficult to distinguish from lipoma. Currently, this distinction is made by testing for MDM2 amplification, which requires a biopsy. The aim of this study was to develop a noninvasive method to predict MDM2 amplification status using radiomics features derived from MRI.
methodsPatients with an MDM2-negative lipoma or MDM2-positive WDLPS and a pretreatment T1-weighted MRI scan who were referred to Erasmus MC between 2009 and 2018 were included. When available, other MRI sequences were included in the radiomics analysis. Features describing intensity, shape and texture were extracted from the tumour region. Classification was performed using various machine learning approaches. Evaluation was performed through a 100 times random-split cross-validation. The performance of the models was compared with the performance of three expert radiologists.
resultsThe data set included 116 tumours (58 patients with lipoma, 58 with WDLPS) and originated from 41 different MRI scanners, resulting in wide heterogeneity in imaging hardware and acquisition protocols. The radiomics model based on T1 imaging features alone resulted in a mean area under the curve (AUC) of 0·83, sensitivity of 0·68 and specificity of 0·84. Adding the T2-weighted imaging features in an explorative analysis improved the model to a mean AUC of 0·89, sensitivity of 0·74 and specificity of 0·88. The three radiologists scored an AUC of 0·74 and 0·72 and 0·61 respectively; a sensitivity of 0·74, 0·91 and 0·64; and a specificity of 0·55, 0·36 and 0·59.
conclusionRadiomics is a promising, non-invasive method for differentiating between WDLPS and lipoma, outperforming the scores of the radiologists. Further optimization and validation is needed before introduction into clinical practice.
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