Exploring a simplified way to diagnose pelvic lipomatosis: prediction of pelvic fat volume using a single cross-sectional image.

Abstract

Pelvic lipomatosis (PL) is a rare disease characterized by the overgrowth of pelvic adipose tissue (AT). We investigated the relationships between areas of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) and pelvic fat volume (PFV), and analyzed the feasibility of diagnosing PL from a single cross-sectional image. The study included 50 patients and 50 controls. We used nnU-Net to segment SAT and VAT automatically. L3 vertebra was set as the zero point (L0), and a total of 201 slices were obtained with a 1 mm interval (L-100 - L+100). We selected 5 pelvic slices, including slices of the anterior superior margin of the S1-S4 vertebrae and the slice above the bilateral femoral head (FH). SAT areas, VAT areas, and PFVs were calculated by computational software. Areas and volumes of 2 groups were compared by t-test or rank-sum test. The correlations among areas and PFV were calculated. Logistic regression models were developed to identify the best slice for predicting PL. Receiver operating characteristic (ROC) curves were performed, and the area under the curve (AUC) and thresholds [with sensitivity (SEN) and specificity (SPE)] were calculated. VAT areas of L-94 - L-100, L+79 - L+100, S1-S4, and FH indicated statistical differences between patients and controls (P<0.05). The linear regression model with VAT area as the independent variable was established to estimate PFV (FH level: r=0.745, P<0.001, R2=0.555). Among the univariate logistic regression models, VAT area at FH as the independent variable had the highest performance in predicting PL (AUC: 0.893, SEN: 74%, SPE: 94%), followed by S4 level (AUC: 0.800, SEN: 88%, SPE: 66%). The overall accuracy of the logistic regression model including VAT areas at S4 and FH in predicting PL was 88% (AUC: 0.927, SEN: 90%, SPE: 88%). VAT areas at the level of FH can help estimate the value of PFV. VAT areas of S4 and FH provide greater power than a single image for the diagnosis of PL.