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Establishment and validation of a CT-based clinical deep learning radiomics nomogram for predicting the response to transcatheter arterial chemoembolization in patients with hepatocellular carcinoma

Establishment and validation of a CT-based clinical deep learning radiomics nomogram for predicting the response to transcatheter arterial chemoembolization in patients with hepatocellular carcinoma

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500096411
Enrollment
Unknown
Registered
2025-01-23
Start date
2024-09-25
Completion date
Unknown
Last updated
2025-02-10

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

Conditions

HCC

Interventions

Training group:None

Sponsors

Shangluo Central Hospital
Lead Sponsor

Eligibility

Sex/Gender
All
Age
25 Years to 87 Years

Inclusion criteria

Inclusion criteria: (1) All patients were diagnosed according to histopathological diagnosis or according to the Chinese HCC diagnosis and treatment guidelines and BCLC staging system; (2) Patients with complete, clear contrast-enhanced CT image data; (3) Patients with CT images obtained within 1 week prior to TACE treatment; (4) patients receiving TACE as initial therapy; (5) Patients with follow-up for more than 6 months.

Exclusion criteria

Exclusion criteria: (1) Patients with missing CT imaging data before TACE treatment; (2) Patients who have received other prior systemic or local anti-tumor therapy, such as surgical resection, radiotherapy, chemotherapy, targeted therapy, immunotherapy, or ablation; (3) patients with a follow-up of less than 6 months.

Design outcomes

Primary

MeasureTime frame
TACE response (TR);no-TACE response (nTR);

Countries

China

Contacts

Public ContactBo Li

Shangluo Central Hospital

978788334@qq.com+86 183 9190 3766

Outcome results

None listed

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