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

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

Recruiting
18-80 years
All
Phase N/A

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Overview

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.

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.

Eligibility

Inclusion Criteria:

  1. CT/B ultrasound showed no fatty liver
  2. No MRI contraindications, including pacemaker, stent, metal implant, or claustrophobia
  3. Received neoadjuvant/adjuvant chemotherapy

Exclusion Criteria:

  1. Missing follow-up information
  2. Liver lesions (metastases, hemangioma, etc.)
  3. Poor image quality

Study details
    Non-Alcoholic Fatty Liver Disease

NCT06735118

Yunnan Cancer Hospital

21 October 2025

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