ArticleSkeletal radiology2020
Pilot study to differentiate lipoma from atypical lipomatous tumour/well-differentiated liposarcoma using MR radiomics-based texture analysis.
Article in Skeletal radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- 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
- End-to-end deep learning model for the diagnosis and segmentation of primary retroperitoneal neoplasm: a multicenter cohort study.EClinicalMedicine · 2025Article
- AI applications in musculoskeletal imaging: a narrative review.European radiology experimental · 2024Review
- Use of radiologic imaging to differentiate lipoma from atypical lipomatous tumor/well-differentiated liposarcoma: Systematic review.SAGE open medicine · 2024Review
- MRI radiomics-based machine learning for classification of deep-seated lipoma and atypical lipomatous tumor of the extremities.La Radiologia medica · 2023Article
- Tumor-to-bone distance and radiomic features on MRI distinguish intramuscular lipomas from well-differentiated liposarcomas.Journal of orthopaedic surgery and research · 2023Article
- Robustness of Radiomic Features: Two-Dimensional versus Three-Dimensional MRI-Based Feature Reproducibility in Lipomatous Soft-Tissue Tumors.Diagnostics (Basel, Switzerland) · 2023Article
- Increasing differential diagnosis between lipoma and liposarcoma through radiomics: a narrative review.Exploration of targeted anti-tumor therapy · 2023Review
- The diagnosis, classification, and treatment of sarcoma in this era of artificial intelligence and immunotherapy.Cancer communications (London, England) · 2022Review
- Prediction of lipomatous soft tissue malignancy on MRI: comparison between machine learning applied to radiomics and deep learning.European radiology experimental · 2022Article
- Radiomics of Musculoskeletal Sarcomas: A Narrative Review.Journal of imaging · 2022Review
- Inflammatory Variant of Atypical Lipomatous Tumor/Well-Differentiated Liposarcoma of the Buccal Mucosa: An Overview and Case Report with a 10-Year Follow-Up.Head and neck pathology · 2021Article
- CT and MRI radiomics of bone and soft-tissue sarcomas: a systematic review of reproducibility and validation strategies.Insights into imaging · 2021Article
- The recurrence of well-differentiated liposarcoma from benign giant intramuscular lipoma: A case (CARE-compliant) report.Medicine · 2021Article
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5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsThis pilot study aims to determine if tumour heterogeneity assessed using magnetic resonance imaging (MRI) radiomics-based texture analysis (TA) can differentiate between lipoma and atypical lipomatous tumour (ALT)/well-differentiated liposarcoma (WDL). MATERIALS AND
methodsThirty consecutive ALT/WDLs and 30 lipomas were included in the study, cases diagnosed both histologically and with murine double minute 2 (MDM2) gene amplification by fluorescence in situ hybridisation (FISH) in excision specimens. Multiple patient, MRI and MRTA factors were assessed. Heterogeneity was evaluated using a filtration-histogram technique-based textural analysis on single axial proton density (PD) and coronal T1-W images of the most homogenously fatty component of the lesion.
resultsThirty-three percent of the diagnoses of ALT/WDL vs lipoma were confirmed using FISH MDM2 analysis. ALT/WDLs were statistically different from lipomas in location (site in the body and depth from skin surface) and fat content, with p values of 0.021, 0.001, and 0.021 respectively. Nine of 36 (25%) texture parameters had significant differences between ALT/WDLs and lipomas on axial PD MRTA, with the most significant results at medium and coarse texture scales particularly mean intensity (p = 0.003) at SSF = 6, and kurtosis (p = 0.012) at SSF = 5. A cut-off value of < 304 for coarse-filtered texture on axial PD MRI identified ALT from lipoma with a sensitivity and specificity of 70% (AUC = 0.73, p = 0.003).
conclusionsTexture heterogeneity quantified at fine, medium, and coarse texture scales are significant differentiators of lipoma and ALT/WDL with the difference particularly marked in medium and coarse texture scales for two MR TA parameters: mean and kurtosis.
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