NCT07820423Recruiting
Multimodal AI Predicts High-risk Pathology in Lung Cancer Associated With Cystic Airspaces
Interpretable Multimodal Artificial Intelligence Predicts High-risk Pathology in Lung Cancer Associated With Cystic Airspaces
Source: ClinicalTrials.gov · Last updated 15 Sept 2026
Brief summary (registry, in English)
The goal of this observational study is to develop and validate an artificial intelligence (AI)-based multimodal radiomics model that integrates preoperative CT imaging features and clinical data to predict pathological high-risk features in patients with lung cancer associated with cystic airspaces (LCCA). The main questions it aims to answer are: Can an AI-based multimodal radiomics model accurately predict pathological high-risk features in LCCA before surgery? Does the integration of CT imaging features and clinical variables improve preoperative risk stratification compared with imaging or clinical information alone?
- Status (registry)
- Recruiting
- Study type
- Observational
- Enrollment
- 600 (estimated)
- First submitted
- 3 Sept 2026
- Start date
- 1 May 2025
- Primary completion date
- 30 Dec 2026
- Lead sponsor
- Central South University
- Diseases
- Lung cancer
- NCT number
- NCT07820423
What you commit to
- Study type: Observational
- Planned participants: 600
- From 1 May 2025 to the primary completion date, 30 Dec 2026
- 1 site in 1 country
- Visits and tests: ask the trial team, the registry does not list them
Built from the registry fields of ClinicalTrials.gov. It describes the study, not your situation.
Who cannot take part
Copied verbatim from ClinicalTrials.gov and grouped by topic. Only the trial team decides who can take part.
Prior treatment
- 1\. Patients with a history of pulmonary diseases that may cause cystic lung lesions, such as pulmonary tuberculosis, pulmonary fungal infection, lymphangioleiomyomatosis (LAM), or Birt-Hogg-Dubé (BHD) syndrome. Emphysema will not be considered an exclusion criterion; however, patients with severe emphysema will be excluded if it significantly affects the identification, boundary delineation, or imaging feature assessment of the target lesion.
- 2\. Patients who received systemic antitumor therapy before enrollment, including chemotherapy, radiotherapy, targeted therapy, or immunotherapy.
- 3\. Patients with other primary malignancies. 4. Patients with missing preoperative chest CT images or CT images of insufficient quality for analysis.
- 5\. Patients without a definite pathological diagnosis or with incomplete pathological results.
- 6\. Patients with missing clinical data.
Who the study is looking for
- 1\. Patients with non-small cell lung cancer (NSCLC) confirmed by biopsy or postoperative pathological examination.
- 2\. Patients who underwent surgical resection of a pulmonary tumor, including lobectomy, segmentectomy, or wedge resection.
- 3\. Patients with complete preoperative chest CT imaging data. 4. Patients whose preoperative chest CT showed a well-defined air-containing cystic component within the tumor, consistent with the radiological features of lung cancer associated with cystic airspaces (LCCA).
- 5\. Patients with available clinical and pathological data required for analysis.
Age in the registry: 18 years to - · Sex: All · Does not accept healthy volunteers
Sites (1)
Sites in registry order. Pick your city on the disease page to sort them by distance.
| Site | City | Country | Site status |
|---|---|---|---|
| The Second Xiangya Hospital of Central South University Coordinator of this site? Confirm its status | Changsha | China | Recruiting |
This page is information, not medical advice. Discuss any trial with your doctor.