NCT06735118

Feasibility Study of Deep Learning-based MDixon Quant for Quantitative Assessment of Chemotherapy-induced Fatty Liver

• Status: Recruiting
• Phase: N/A
• Study type: Interventional
• Enrollment: 120 (estimated)
• Sponsor: Yunnan Cancer Hospital · Academic / Other
• Sex: All
• Age: 18 Years – 80 Years
• Healthy volunteers: Accepted

Summary

The purpose of this study is to quantitatively assess the changes in liver fat content in cancer patients before and after treatment. The main questions it aims to answer are:How does the liver fat fraction change before and after chemotherapy? In this study, patients undergoing mDixon Quant scanning are subjected to fully automated segmentation and measurement of liver fat content using artificial intelligence.

Detailed description

Regarding the extraction of liver fat fraction, the traditional axial ROI method involves selecting several regions of interest (ROIs) at the largest cross-sectional level or across multiple continuous sections, and taking the average value as the whole-liver fat fraction. This method is complex, time-consuming, and cannot obtain the whole-liver fat fraction. In this study, a threshold extraction method is used to obtain the whole-liver fat fraction, enabling a 2D-to-3D conversion, which is more time-efficient and labor-saving, and provides a more accurate measurement.

Conditions

• Non-Alcoholic Fatty Liver Disease

Interventions

Type	Name	Description
DRUG	Neoadjuvant chemotherapy	Neoadjuvant chemotherapy

Timeline

• Start date: 2023-12-25
• Primary completion: 2024-12-30
• Completion: 2024-12-30
• First posted: 2024-12-16
• Last updated: 2024-12-16

Locations

1 site across 1 country: China