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Research on Differential Diagnosis of Pulmonary Nodules Based on Radiomics and Artificial Intelligence

Research on Differential Diagnosis of Pulmonary Nodules Based on Radiomics and Artificial Intelligence

Status
Recruiting
Phases
Early Phase 1
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2100043351
Enrollment
Unknown
Registered
2021-02-11
Start date
2021-03-01
Completion date
Unknown
Last updated
2021-06-07

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

Conditions

Lung cancer

Interventions

Gold Standard:Pathological diagnosis
Index test:Imaging,&#32
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deep&#32
learning&#32

Sponsors

Sun Yat-Sen University Cancer Center
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: (1) Pathological results (granuloma or lung cancer) confirmed by radical surgical resection; (2) chest CT within 2 months prior to surgery; (3) presence of SSPNs <= 30 mm in size; (4) presence of SSPNs with one or more of the following signs: spiculation, lobulation, or pleural indentation; (5) no lymph node or distant metastasis confirmed by pathological biopsy of lung cancer nodules.

Exclusion criteria

Exclusion criteria: (1) Lesions with indeterminate histological results from an inadequate biopsy sample; (2) lymph nodes with a diameter of >= 10 mm or distant metastasis based on radiologic evaluation; (3) patients with nodules that were highly suspected as benign lesions, such as tuberculosis balls with caseous necrosis, cryptococcus with an obvious halo sign, coarse calcifications (> 2mm) or fat; (4) a history of lung cancer or other malignancies.

Design outcomes

Primary

MeasureTime frame
radiomics diagnosis;imaging diagnosis;radiological diagnosis;

Countries

China

Contacts

Public ContactLi Sheng

Department of Medical Imaging, Sun Yat-Sen University Cancer Center

lisheng@sysucc.org.cn+86 15989063181

Outcome results

None listed

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