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Research on Prediction Ability of Radiomics Based Deep Learning Model on Diagnosing High-risk Lung Nodule Infiltration Type

Research on Prediction Ability of Radiomics Based Deep Learning Model on Diagnosing High-risk Lung Nodule Infiltration Type

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2400079796
Enrollment
Unknown
Registered
2024-01-12
Start date
2024-02-01
Completion date
Unknown
Last updated
2024-01-14

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

Conditions

Lung cancer, pulmonary nodules

Interventions

Training group:None
Test group:None

Sponsors

Sichuan Cancer Hospital
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 80 Years

Inclusion criteria

Inclusion criteria: (1) Patients with pulmonary nodules who undergo surgery in our hospital; (2) Having a clear postoperative pathological diagnosis; (3) Complete chest CT DICOM (within one month before surgery).

Exclusion criteria

Exclusion criteria: (1) Lung cancer = stage II ; (2) Metastatic lung cancer; (3) Congenital thoracic deformity or thoracic deformation caused by severe trauma visible on CT images; (4) Other factors that the investigator deemed inappropriate for inclusion in the study.

Design outcomes

Primary

MeasureTime frame
Receiver Operating Characteristic;—

Countries

China

Contacts

Public ContactLeng Xuefeng

SICHUAN CANCERE HOSPITAL

doc.leng@uestc.edu.cn+86 187 0287 0755

Outcome results

None listed

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