Automated MRI Liver Segmentation For Accurate Quantification Of Hepatic Steatosis.

RATIONALE AND

OBJECTIVES

MRI-proton density fat fraction (MRI-PDFF) is widely applied in clinical practice for hepatic fat quantification. However, the conventional manual region of interest (ROI) method is time-consuming and operator-dependent, and the diagnostic accuracy of artificial intelligence (AI)-based models for hepatic steatosis assessment remains to be fully clarified. This study aimed to evaluate the diagnostic performance of an AI-based whole liver segmentation (WLS) model for histological hepatic steatosis grading, and to technically validate its segmentation performance and agreement with manual ROI-based PDFF measurement.

MATERIALS AND

METHODS

A total of 538 adults who underwent MRI-PDFF examinations were enrolled. A VBB-Net segmentation model was developed in a training cohort of 372 patients (418 examinations). Validation was performed in a consecutive cohort of 166 adults who underwent liver biopsy for suspected metabolic dysfunction-associated steatotic liver disease (MASLD). Histological steatosis grading (S0-S3) was used as the reference standard.

RESULTS

The mean Dice coefficients were 0.94 ± 0.05 in the training cohort and 0.93 ± 0.04 in the validation cohort. Margins of error were 0.794% for AI-WLS-PDFF and 0.821% for ROI-PDFF. The AUROCs (95% CI) of AI-WLS-PDFF for diagnosing S0-S3 were 0.995 (95% CI: 0.989, 1.000), 0.951 (95% CI: 0.921, 0.980), and 0.928 (95% CI: 0.888, 0.968), respectively. AI-WLS-PDFF showed excellent agreement with ROI-PDFF (ICC, 0.996; 95% CI: 0.995, 0.997) with minimal bias (mean difference, -0.06%; 95% agreement limits: -1.59% to 1.47%).

CONCLUSION

The AI-WLS-PDFF model showed high diagnostic performance for histological steatosis grading and excellent agreement with manual ROI-PDFF, supporting its potential as an automated approach for whole-liver PDFF quantification in patients with MASLD. The proposed cutoff values should be considered exploratory and require further validation.