A Multicenter Cohort Study of AI-Based Methods for Pulmonary Nodule Diagnosis and Follow-up

A Prospective, Multicentre Cohort Study of Artificial Intelligence-based Methods for Pulmonary Nodule Segmentation, Benign-Malignant Risk Stratification, and Follow-up

Status

Not yet recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT07815483

Enrollment

16880

Registered

2026-09-11

Start date

2026-10-01

Completion date

2029-10-01

Last updated

2026-09-11

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

Conditions

Artificial Intelligence (AI), Lung Neoplasms, Pulmonary Nodules

Keywords

Pulmonary Nodules, Lung Neoplasms, Diagnostic Imaging, Artificial Intelligence, Deep Learning

Brief summary

This is an observational, multicentre study. The primary objective of this study was to evaluate the diagnostic performance and clinical applicability of artificial intelligence models for pulmonary nodule segmentation, benign-malignant risk stratification, and follow-up management. We will collect CT images and medical data from participants at several hospitals. Participants will not receive any drugs or medical interventions.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to No maximum

Healthy volunteers

Inclusion criteria

(1) Age ≥18 years; (2) At least one non-calcified pulmonary nodule (diameter ≥3 mm and ≤30 mm) detected on chest CT; (3) Agreement to participate in the study and provision of written informed consent. \-

Exclusion criteria

1. Poor CT image quality with severe artifacts that preclude AI-based analysis; 2. Expected survival \<12 months or inability to complete follow-up; 3. History of prior ipsilateral lung malignancy or local treatment. -

Design outcomes

Primary

Measure	Time frame	Description
Diagnostic performance of the AI model for pulmonary nodule malignancy	Up to 24 months after enrollment	Area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of the AI model for classifying benign and malignant nodules, using pathology results or longitudinal stability as the reference standard.

Secondary

Measure	Time frame	Description
Risk stratification accuracy across different nodule sizes	Up to 24 months	The ability of the AI model to correctly stratify nodules into low, intermediate, and high-risk categories compared to clinical judgement.

Outcome results