Feasibility Study of Deep Learning-based MDixon Quant for Quantitative Assessment of Chemotherapy-induced Fatty Liver
NCT ID: NCT06735118
Last Updated: 2024-12-16
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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RECRUITING
NA
120 participants
INTERVENTIONAL
2023-12-25
2024-12-30
Brief Summary
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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
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Conditions
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Study Design
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RANDOMIZED
SINGLE_GROUP
DIAGNOSTIC
DOUBLE
Study Groups
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neoadjuvant chemotherapy group
Cancer patients undergoing chemotherapy.
Neoadjuvant chemotherapy
Neoadjuvant chemotherapy
Non-neoadjuvant chemotherapy group
Cancer patients not undergoing chemotherapy.
No interventions assigned to this group
Interventions
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Neoadjuvant chemotherapy
Neoadjuvant chemotherapy
Other Intervention Names
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Eligibility Criteria
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Inclusion Criteria
2. No MRI contraindications, including pacemaker, stent, metal implant, or claustrophobia
3. Received neoadjuvant/adjuvant chemotherapy
Exclusion Criteria
2. Liver lesions (metastases, hemangioma, etc.)
3. Poor image quality
18 Years
80 Years
ALL
Yes
Sponsors
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Yunnan Cancer Hospital
OTHER
Responsible Party
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Principal Investigators
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Lianhua Ye
Role: STUDY_DIRECTOR
Ethics Committee of Yunnan Provincial Cancer Hospital
Locations
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Yunnan Cancer Hospital
Kunming, Yunnan, China
Countries
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Central Contacts
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Facility Contacts
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Guojun Zhang, Professor
Role: primary
Other Identifiers
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KYLX2023-165
Identifier Type: -
Identifier Source: org_study_id