Clinical Decision and Prognosis Prediction of Hepatocellular Carcinoma by Deep Learning
Study Details
Study Description
Brief Summary
Developing a deep learning model based on contrast-enhanced ultrasound (CEUS) to predict the prognosis of hepatocellular carcinoma (HCC) and aid choose operation decisions
Condition or Disease | Intervention/Treatment | Phase |
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Detailed Description
Collecting CEUS and clinical data of HCC from different institutions retrospectively.
Developing a deep learning model based on CEUS to predict the prognosis of HCC. Developing a deep learning model based on CEUS to choose a better operation (ablation or surgery) of HCC patients.
Then, validating the deep learning model in the prospective data.
Study Design
Outcome Measures
Primary Outcome Measures
- Recurrence-free survival (RFS) [Immediately after the surgery or ablation]
Recurrence-free survival is defined as the time elapsed between a predefined point in time (the date of diagnosis, randomization or the intervention) and any recurrence (local, regional, or distant) or death due to any cause (death is an event).
Secondary Outcome Measures
- Recurrence [Immediately after the surgery or ablation]
Recurrence included local tumor progression, regional recurrence, or distant recurrence.
Eligibility Criteria
Criteria
Inclusion Criteria:
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patients with HCC (Ia, Ib, IIa stage) China liver cancer staging who underwent resection or ablation
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without macro-vascular invasion
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Child-Pugh A/B grade
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HCC is proved by pathological examination or two enhanced imaging
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CEUS (Sonovue or Sonozoid) images are performed two weeks before the operation
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CEUS images are included in at least three stages (Arterial phase, Portal phase, and Late phase)
Exclusion Criteria:
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postop follow-up loss or expired less than 3 months
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patients with co-malignancy
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poor images quality for analyzing
Contacts and Locations
Locations
Site | City | State | Country | Postal Code | |
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1 | Chinese PLA General Hospital | Beijing | Beijing | China | 100853 |
Sponsors and Collaborators
- Ping Liang
Investigators
None specified.Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- S2022-002