What question did this study set out to answer?

This research aims to evaluate the effectiveness of contrast-enhanced CT in differentiating fat-poor liposarcoma from leiomyosarcoma.

January 10, 2026BMC Medical Imaging0 citationsOpen Access

Can multi-phase contrast-enhanced CT be used to differentiate between intra-abdominal and retroperitoneal fat-poor liposarcoma and leiomyosarcoma?

YDYang DongDalian Medical University JSJiadong SongDalian Medical University JZJiaye ZhangZhejiang Chinese Medical University

Key Result

The dynamic nomogram achieved an AUC of 0.885, reliably distinguishing fat-poor liposarcoma from leiomyosarcoma using AEF, tumor size, and enhancement patterns.

Key Points

This research aims to evaluate the effectiveness of contrast-enhanced CT in differentiating fat-poor liposarcoma from leiomyosarcoma.
Conducted a retrospective analysis of pathologically confirmed intra-abdominal or retroperitoneal sarcomas.
Assessed twelve CT imaging features and calculated quantitative parameters like arterial enhancement fraction and extracellular volume fraction.
Performed multivariate logistic regression to identify independent predictors.
Constructed a nomogram model based on predictive features and assessed its performance with ROC analysis.
Identified independent predictors for distinguishing fat-poor liposarcoma from leiomyosarcoma, including arterial enhancement fraction, mean tumor diameter, and patchy enhancement.
Achieved excellent diagnostic accuracy with a nomogram model (AUC = 0.885) and effective calibration.
Confirmed that quantitative and qualitative CT features reliably differentiate the two types of tumors.

Structured PICO

Can multi-phase contrast-enhanced CT features and a derived nomogram accurately differentiate between intra-abdominal and retroperitoneal fat-poor liposarcoma and leiomyosarcoma?

Population

56 patients with pathologically confirmed intra-abdominal or retroperitoneal non-uterine leiomyosarcoma (LMS, n=27) and fat-poor liposarcoma (FPLPS, n=29).

Intervention

Multi-phase contrast-enhanced CT (CECT) imaging features, quantitative parameters (arterial enhancement fraction [AEF], extracellular volume fraction [ECV]), and a combined dynamic nomogram model

Outcome

Diagnostic performance (AUC, sensitivity, specificity, PPV, NPV, accuracy) in differentiating FPLPS from LMS

Quantitative and qualitative features from multi-phase contrast-enhanced CT, combined into a nomogram, can reliably and non-invasively differentiate fat-poor liposarcoma from leiomyosarcoma.

Main Result

Absolute Event Rate: 0% vs 0%

Abstract

This study aimed to assess the diagnostic performance of contrast-enhanced CT in differentiating intra-abdominal and retroperitoneal fat-poor liposarcoma (FPLPS) from leiomyosarcoma (LMS). A retrospective analysis was conducted on patients with pathologically confirmed intra-abdominal or retroperitoneal non-uterine LMS and FPLPS. Twelve CT imaging features were assessed, and quantitative parameters, including arterial enhancement fraction (AEF) and extracellular volume fraction (ECV), were calculated. Multivariate logistic regression identified independent predictors, and a combined nomogram model was constructed. Model performance was assessed using ROC analysis, calibration, and decision curve analysis. A total of 56 intra-abdominal or retroperitoneal sarcomas, including non-uterine LMS ( n = 27) and FPLPS ( n = 29). AEF (OR = 1.07, P = 0.006), mean tumor diameter (OR = 1.29, P = 0.040), and patchy enhancement (OR = 7.58, P = 0.021) were independent predictors for distinguishing FPLPS from LMS. The dynamic nomogram ( https://liexiantu1.shinyapps.io/dynom/ . ) demonstrated excellent diagnostic accuracy (AUC = 0.885) with satisfactory calibration, and clinical utility. Quantitative (AEF) and qualitative (tumor size and enhancement pattern) of CT features reliably distinguish FPLPS from LMS in intra-abdominal and retroperitoneal locations. The nomogram offers a practical, non-invasive tool to support preoperative diagnosis.

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