Clinical trial · Observational
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
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 12, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260912-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (3)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Artificial Intelligence (AI) | — | UNRESOLVED | — |
| Lung Neoplasms | Lung Neoplasm | ONTOLOGY_EXACT | 0.98 |
| Pulmonary Nodules | — | UNRESOLVED | — |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (2)
- label
- Benign Pulmonary Nodule Group
- description
- Participants with pulmonary nodules diagnosed as benign, confirmed by either pathological examination or stability on follow-up imaging. No intervention is applied.
- label
- Malignant Pulmonary Nodule Group
- description
- Participants with pulmonary nodules diagnosed as malignant, confirmed by pathological examination. No intervention is applied.
Primary outcomes (1)
- measure
- Diagnostic performance of the AI model for pulmonary nodule malignancy
- timeFrame
- Up to 24 months after enrollment
- description
- 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 outcomes (1)
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
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. -
References
Publications (0)
Data not yet available