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Tumor Invasiveness Estimation of Artificial Intelligence System for Subsolid Nodules on Computed Tomography: Diagnostic Performance and Utility Verification in Clinical Practice

Tumor Invasiveness Estimation of Artificial Intelligence System for Subsolid Nodules on Computed Tomography: Diagnostic Performance and Utility Verification in Clinical Practice

Status
Recruiting
Phases
Early Phase 1
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2000030925
Enrollment
Unknown
Registered
2020-03-17
Start date
2019-12-01
Completion date
Unknown
Last updated
2020-03-23

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

Conditions

pulmonary subsolid nodules

Interventions

Sponsors

Shanghai Pulmonary Hospital
Lead Sponsor

Eligibility

Sex/Gender
All
Age
20 Years to 75 Years

Inclusion criteria

Inclusion criteria: (1) the maximum diameter of lesion 3cm with thin-section CT images (1.25mm); (2) pulmonary nodules were histopathological confirmed as atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IA) according to the lung adenocarcinoma classification; (3) patients without a history of malignancy in 5 years; (4) obtained writtent informed consent.

Exclusion criteria

Exclusion criteria: 1. The number of lung nodules exceeds 20; 2. The quality of CT images is poor, or patients with comorbidities including extensive pulmonary infection, massive pleural effusion, and diffused interstitial pneumonia, etc. 3. Patients with incomplete clinical information; 4. Patients with a history of thoracic surgery.

Design outcomes

Primary

MeasureTime frame
Accuracy;Sensitivity;Specificity;AUC;

Countries

China

Contacts

Public ContactChen Chang

Shanghai Pulmonary Hospital

1831228@tongji.edu.cn+86 15618977421

Outcome results

None listed

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