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Study on the Deep Learning Method for Predicting Microvascular Invasion of Small Hepatocellular Carcinoma Using Nanoprobe (Fe3O4@PEG) Combined with R3D-18

Study on the Deep Learning Method for Predicting Microvascular Invasion of Small Hepatocellular Carcinoma Using Nanoprobe (Fe3O4@PEG) Combined with R3D-18

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2600128868
Enrollment
Unknown
Registered
2026-07-27
Start date
2026-07-30
Completion date
Unknown
Last updated
2026-08-03

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

Conditions

Hepatocellular Carcinoma?

Interventions

MVI(-):None
MVI(+):None

Sponsors

The First Hospital Of Jiaxing
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 98 Years

Inclusion criteria

Inclusion criteria: 1. Underwent surgical resection for liver tumor, with pathologically confirmed hepatocellular carcinoma. 2. Received contrast-enhanced MRI examination within 1 month preoperatively, with complete DICOM-format MRI images available for analysis. 3. No concurrent other malignancies or prior treatment history for other hepatic malignant tumors.

Exclusion criteria

Exclusion criteria: 1.Incomplete clinical and laboratory examination data. 2.Poor-quality MRI images that cannot be used for analysis. 3.Patients who received preoperative anti-tumor therapies such as targeted therapy, immunotherapy, radiotherapy and chemotherapy, as well as local treatments including transcatheter arterial chemoembolization, seed implantation, radiofrequency, microwave and cryoablation.

Design outcomes

Primary

MeasureTime frame
Diagnostic accuracy of MVI(+);—

Countries

China

Contacts

Public ContactJianbing Ma

The First Hospital Of Jiaxing

413947372@qq.cpm+86 573 82082937

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Aug 10, 2026