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Deep Learning Based Multimodal Ultrasound Radiomics For Accurate Prediction of Micro-invasion of Hepatocellular Carcinoma

Deep Learning Based Multimodal Ultrasound Radiomics For Accurate Prediction of Micro-invasion of Hepatocellular Carcinoma

Status
Active, not recruiting
Phases
Early Phase 1
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR1900027858
Enrollment
Unknown
Registered
2019-11-30
Start date
2019-12-01
Completion date
Unknown
Last updated
2019-12-10

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

Conditions

Hepatocellular Carcinoma

Interventions

multimode&#32
ultrasound&#32
of&#32
HCC
Gold Standard:Pathological results

Sponsors

The First Affiliated Hospital of Sun Yat-Sen University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 80 Years

Inclusion criteria

Inclusion criteria: 1) Patients aged 18-80 years; 2) Diagnosed as primary hepatocellular carcinoma according to the practice guidelines for hepatocellular carcinoma of AASLD (2018 edition); 3) At least 1.0cm of tumor adjacent liver parenchyma; 4) The depth of the target tumor was no more than 10.0cm, which can be clearly displayed in 2D ultrasound imaging with no surrounding gas or rib shielding; 5) No macrovascular invasion according to preoperative imaging findings; 6) Approved by the ethics committee and signed by the subjects.

Exclusion criteria

Exclusion criteria: 1) The patient cannot cooperate with breath-holding; 2) The pation have received local or systemic anti-tumor therapy; 3) Failure to obtain tumor pathology; 4) Surgical margin < 1.0cm; 5) Pathological results showed non-hepatocellular carcinoma.

Design outcomes

Primary

MeasureTime frame
AUC;Sensitivity;specificity;

Countries

China

Contacts

Public ContactXie Xiaoyan

The First Affiliated Hospital of Sun Yat-Sen University

xiexyan@mail.sysu.edu.cn+86 13719210801

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026