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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

Status
Recruiting
Phases
NA
Study type
Interventional
Source
ClinicalTrials.gov
Registry ID
NCT06735118
Enrollment
120
Registered
2024-12-16
Start date
2023-12-25
Completion date
2024-12-30
Last updated
2024-12-16

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Non-Alcoholic Fatty Liver Disease

Brief 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.

Interventions

DRUGNeoadjuvant chemotherapy

Neoadjuvant chemotherapy

Sponsors

Yunnan Cancer Hospital
Lead SponsorOTHER

Study design

Allocation
RANDOMIZED
Intervention model
SINGLE_GROUP
Primary purpose
DIAGNOSTIC
Masking
DOUBLE (Subject, Outcomes Assessor)

Eligibility

Sex/Gender
ALL
Age
18 Years to 80 Years
Healthy volunteers
Yes

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

Design outcomes

Primary

MeasureTime frameDescription
Extract the whole liver fat fractionone yearExtract the whole liver fat fraction using the threshold extraction method.

Countries

China

Contacts

Primary ContactLizhu Liu, Graduate
liulizhu2022@163.com18287509587
Backup ContactZhenhui Li, MD
lizhenhui@kmmu.edu.cn13698736132

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026