Evaluation of Magnetic Resonance Imaging Features to Distinguish Lipomas from Atypical Lipomatous Tumours in the Extremities and Back

Background/Objectives: Lipoma and atypical lipomatous tumours (ALTs) are the most common soft tissue tumours. Accurate differentiation is clinically important because management differs substantially between these two entities. Magnetic resonance imaging (MRI) has been widely investigated as a complementary diagnostic tool to histopathology confirmation. Nevertheless, the diagnostic accuracy and reproducibility of MRI in routine clinical practice remain debated. Methods: This study aims to first evaluate MRI features that differentiate lipomas and ALTs, using histopathology as the reference standard, and second, to evaluate interobserver agreement for each MRI feature. A cross-sectional study was conducted on 12 patients (eight males, four females; median age 67, ranging 25–75 years) with biopsy-proven ALTs and 36 patients (15 males, 21 females; median age 58, ranging 28–80 years) with biopsy-proven lipomas who underwent surgery between 2020 and 2025. MRI features, including tumour location, compartment involvement, margin, signal characteristics, septa thickness, nodular components, contrast enhancement, and maximum tumour diameter, were independently assessed by three radiology specialists who were blinded to the histopathological diagnosis. Interobserver reliability for each MRI feature was evaluated using Cronbach’s alpha and intraclass correlation coefficients (ICCs). Results: Incidence of tumour size greater than 100 mm was significantly higher in the ALT group (91.7%) compared to the lipoma group (51.4%). Fifty-eight percent of ALTs had septa greater than 2 mm compared to only 11.8% of lipomas. Contrast enhancement involving more than one-third of tumour volume was also significantly more frequent in the ALT group (33.3%) compared to the lipoma group (2%). All ALTs were located below the deep fascia. Strong interobserver agreement was seen with tumour location (ICC = 0.933), size (ICC = 0.954), compartment involved (ICC = 0.902), and septa thickness (ICC = 0.82). Conclusions: Tumours below the deep fascia with a maximum diameter more than 10 cm, septal thickness more than 2 mm, and contrast enhancement involving more than one-third of tumour volume are useful features that can differentiate ALTs from lipoma.

Authors

Institutions

Publication Details

Journal
Diagnostics
Published
2026-08-17
DOI
https://doi.org/10.3390/diagnostics16162598
Primary Topic
Sarcoma Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Evaluation of Magnetic Resonance Imaging Features to Distinguish Lipomas from Atypical Lipomatous Tumours in the Extremities and Back

Nor Hazla Mohamed Haflah, Suraya Aziz, Levin Kesu Belani, Emilia Rosniza Mohammed Rusli et al.
Diagnostics
Sarcoma Diagnosis and Treatment
article

Evaluation of Magnetic Resonance Imaging Features to Distinguish Lipomas from Atypical Lipomatous Tumours in the Extremities and Back

Nor Hazla Mohamed Haflah, Suraya Aziz, Levin Kesu Belani, Emilia Rosniza Mohammed Rusli, Juliana Fairuz Maktar, Mohamed Hafizudin Abdullah Sani, Muhamad Zhafir Nor Asikin
article en

Abstract

Background/Objectives: Lipoma and atypical lipomatous tumours (ALTs) are the most common soft tissue tumours. Accurate differentiation is clinically important because management differs substantially between these two entities. Magnetic resonance imaging (MRI) has been widely investigated as a complementary diagnostic tool to histopathology confirmation. Nevertheless, the diagnostic accuracy and reproducibility of MRI in routine clinical practice remain debated. Methods: This study aims to first evaluate MRI features that differentiate lipomas and ALTs, using histopathology as the reference standard, and second, to evaluate interobserver agreement for each MRI feature. A cross-sectional study was conducted on 12 patients (eight males, four females; median age 67, ranging 25–75 years) with biopsy-proven ALTs and 36 patients (15 males, 21 females; median age 58, ranging 28–80 years) with biopsy-proven lipomas who underwent surgery between 2020 and 2025. MRI features, including tumour location, compartment involvement, margin, signal characteristics, septa thickness, nodular components, contrast enhancement, and maximum tumour diameter, were independently assessed by three radiology specialists who were blinded to the histopathological diagnosis. Interobserver reliability for each MRI feature was evaluated using Cronbach’s alpha and intraclass correlation coefficients (ICCs). Results: Incidence of tumour size greater than 100 mm was significantly higher in the ALT group (91.7%) compared to the lipoma group (51.4%). Fifty-eight percent of ALTs had septa greater than 2 mm compared to only 11.8% of lipomas. Contrast enhancement involving more than one-third of tumour volume was also significantly more frequent in the ALT group (33.3%) compared to the lipoma group (2%). All ALTs were located below the deep fascia. Strong interobserver agreement was seen with tumour location (ICC = 0.933), size (ICC = 0.954), compartment involved (ICC = 0.902), and septa thickness (ICC = 0.82). Conclusions: Tumours below the deep fascia with a maximum diameter more than 10 cm, septal thickness more than 2 mm, and contrast enhancement involving more than one-third of tumour volume are useful features that can differentiate ALTs from lipoma.

DiagnosticsVol. 16(16)
University Kebangsaan Malaysia Medical Centre (MY), National University of Malaysia (MY)
Openalex Percentile: Top 10%
Sarcoma Diagnosis and Treatment
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.