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

N/ANot Yet RecruitingNCT07815483

Shanghai East Hospital16,880 enrolled

Overview

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.

Study Type

OBSERVATIONAL

Enrollment

16,880

Conditions

Pulmonary Nodules Lung Neoplasms Artificial Intelligence (AI)

Eligibility

Sex: ALLMin age: 18 Years

Medical Language ↔ Plain English

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. -

Outcomes

Primary Outcomes

Diagnostic performance of the AI model for pulmonary nodule malignancy

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.

Time frame: Up to 24 months after enrollment

Secondary Outcomes

Risk stratification accuracy across different nodule sizes

The ability of the AI model to correctly stratify nodules into low, intermediate, and high-risk categories compared to clinical judgement.

Time frame: Up to 24 months

Data from ClinicalTrials.gov

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