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Application of CT and MRI imaging features, radiomics and deep learning in predicting high-risk pathological types and poor prognosis of liver cancer

Application of CT and MRI imaging features, radiomics and deep learning in predicting high-risk pathological types and poor prognosis of liver cancer

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2300068879
Enrollment
Unknown
Registered
2023-03-01
Start date
2023-03-01
Completion date
Unknown
Last updated
2023-05-22

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

Conditions

liver cancer

Interventions

Gold Standard:Pathological results (HE staining and immunohistochemical staining)
Index test:CT and MRI imaging features, radiomics and deep learning models.

Sponsors

Shandong Provincial Hospital Affiliated to Shandong First Medical University
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: (a) Complete CT and MRI images before surgery; (b) Available results of pathological sections (HE staining and immunohistochemical staining). (c) Complete clinical data before surgery.

Exclusion criteria

Exclusion criteria: local interventional therapy and systemic chemotherapy was received before surgery.

Design outcomes

Primary

MeasureTime frame
Area under curve (AUC);Positive predictive value;Negative predictive value;Sensitivity;Specificity;—

Countries

china

Contacts

Public ContactXinya zhao

Shandong Provincial Hospital

zhaoxinya2000@126.com+86 15168887805

Outcome results

None listed

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