NCT07820423 Multimodal AI Predicts High-risk Pathology in Lung Cancer Associated With Cystic Airspaces
| NCT ID | NCT07820423 |
| Status | Recruiting |
| Phase | — |
| Sponsor | Central South University |
| Condition | Lung Cancer Associated With Cystic Airspaces |
| Study Type | OBSERVATIONAL |
| Enrollment | 600 participants |
| Start Date | 2025-05-01 |
| Primary Completion | 2026-12-30 |
Eligibility & Interventions
Eligibility Fast-Check
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What to Expect as a Participant
This is an observational study. You will not receive an experimental treatment; researchers will collect data based on your existing condition or standard treatment.
This trial targets 600 participants in total. It began in 2025-05-01 with a primary completion date of 2026-12-30.
⚠ This information is for research awareness only. Always consult your physician before joining any clinical trial. Participation is voluntary and you may withdraw at any time.
Brief Summary
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?
Eligibility Criteria
Inclusion Criteria: * 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. Exclusion Criteria: * 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.
Contact & Investigator
Chen Chen, M.D.
STUDY DIRECTOR
Second Xiangya Hospital of Central South University
Frequently Asked Questions
Who can join the NCT07820423 clinical trial?
This trial is open to participants of all sexes, aged 18 Years or older, studying Lung Cancer Associated With Cystic Airspaces. Full inclusion and exclusion criteria are listed in the Eligibility Criteria section. Always confirm your eligibility with the research team before applying.
Is NCT07820423 currently recruiting?
Yes, NCT07820423 is actively recruiting participants. Contact the research team at chenchen1981412@csu.edu.cn for enrollment information.
Where is the NCT07820423 trial being conducted?
This trial is being conducted at Changsha, China.
Who is sponsoring the NCT07820423 clinical trial?
NCT07820423 is sponsored by Central South University. The principal investigator is Chen Chen, M.D. at Second Xiangya Hospital of Central South University. The trial plans to enroll 600 participants.
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