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Multidimensional Analysis of Lung Cancer Based on Radiomics, Deep Learning, and Clinical Features and Its Application Research in Prediction of Pathological Subtypes, Gene Mutations, and Metastasis

Multidimensional Analysis of Lung Cancer Based on Radiomics, Deep Learning, and Clinical Features and Its Application Research in Prediction of Pathological Subtypes, Gene Mutations, and Metastasis

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500099687
Enrollment
Unknown
Registered
2025-03-27
Start date
2025-04-01
Completion date
Unknown
Last updated
2025-03-31

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

Conditions

Primary pulmonary malignant tumor

Interventions

Gold Standard:Pathological results
Index test:Multidimensional Analysis Model of Lung Cancer Based on Radiomics, Deep Learning, and Clinical Features

Sponsors

The First Affiliated Hospital of Army Medical University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
1 Years to 100 Years

Inclusion criteria

Inclusion criteria: 1.Confirmed primary pulmonary malignant tumor; 2.Surgical treatment for lung cancer; 3.Chest enhanced CT images taken within 1 month before surgery; 4.Post - operative pathological report available;

Exclusion criteria

Exclusion criteria: 1.Having received radiotherapy, chemotherapy, targeted therapy, immunotherapy, or radiofrequency therapy before surgery. 2.Pathologically confirmed as Hodgkin lymphoma or primary malignant lymphoma. 3.The slice thickness of CT images is not less than 5mm. 4.Severe artifacts in CT images, resulting in poor image quality.

Design outcomes

Primary

MeasureTime frame
Is there mediastinal lymph node metastasis or not?;Accuracy;Sensitivity;Specificity;

Secondary

MeasureTime frame
STAS Status, P53 Mutation, Types of Gene Mutation;

Countries

China

Contacts

Public ContactHaidong Wang

The First Affiliated Hospital of Army Medical University

wanghd@tmmu.edu.cn+86 23 68765821

Outcome results

None listed

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