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A Prospective, Multicenter Diagnostic Trial of Deep Neural Network Model LTI-Net for Predicting Aggressiveness of Lung Ground Glass Nodules

A Prospective, Multicenter Diagnostic Trial of Deep Neural Network Model LTI-Net for Predicting Aggressiveness of Lung Ground Glass Nodules

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2300068201
Enrollment
Unknown
Registered
2023-02-09
Start date
2022-06-01
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

Pulmonary ground glass nodules

Interventions

Gold Standard:Pathological diagnosis
Index test:Deep Neural Network Model LTI-Net

Sponsors

West China Hospital, Sichuan University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 75 Years

Inclusion criteria

Inclusion criteria: 1. Male or female aged 18-75; 2. Patients with GGO diagnosed by CT or other imaging tests within 3 months; 3. Patients with an ECOG score of 0 or 1; 4. Patients with pathological or no contraindications to surgery are advisable; 5. Patients with no previous history of lung cancer.

Exclusion criteria

Exclusion criteria: 1. Patients with evidence of metastasis; 2. History of other types of cancer; 3. Patients who have received intrathoracic radiation therapy and chemotherapy in the past; 4. Patients with severe mental illness; 5. Patients who are pregnant or breastfeeding.

Design outcomes

Primary

MeasureTime frame
sensitivity;specificity;positive predictive value;negative predictive value;positive likelihood ratio;negative likelihood ratio;diagnostic odds ratio;Area Under Curve;—

Countries

China

Contacts

Public ContactLunxu Liu

West China Hospital of Sichuan University

lunxu_liu@aliyun.com+86 18980601525

Outcome results

None listed

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