AI Models for Predicting Occult Pleural Dissemination in NSCLC

NCT07065422 | Completed

• 1 other identifier: GUOW
• Study Type: observational
• Target: 326
• Locations: 1 country
• Sites: 1

Brief Summary

Occult pleural dissemination (PD) in non-small cell lung cancer (NSCLC) patients is likely to be missed on computed tomography (CT) scans, associated with poor survival, and generally contraindicated for radical surgery. This study aimed to develop and compare the performance of radiomics-based machine learning (ML), deep learning (DL), and fusion models to preoperatively identify occult PDs in NSCLC patients. Patients from three Chinese high-volume medical centers (2016-2023) were retrospectively collected and divided into training, internal test, and external test cohorts. Ten radiomics-based ML models and eight DL models were trained using CT plain scan images at the maximum cross-sectional areas of the primary tumor. Moreover, another two fusion models (prefusion and postfusion) were developed using feature-based and decision-based methods. The receiver operating characteristic curve (ROC) and area under the curve (AUC) were mainly used to compare the predictive performance of the models.

Conditions

Non-Small Cell Lung Cancer

Outcome Measures

Primary Outcomes (1)

• The area under the receiver operating characteristic curve (AUC)

  through study completion, an average of 6 months.

Study Arms (1)

non-small cell lung cancer (NSCLC) patients with or without occult pleural dissemination.

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

From January 2016 to December 2023, NSCLC patients with or without surgically confirmed occult PD from three high-volume centers in China were enrolled.

You may qualify if:

• pathologically confirmed primary NSCLC with malignant pleural dissemination;
• no preoperative treatment;
• clinicopathological data were complete.

You may not qualify if:

• pleural effusion detected preoperatively;
• preoperatively diagnosed with PD;
• poor CT quality or no CT scans within 1 month before surgery.

Sponsors & Collaborators

• Daping Hospital and the Research Institute of Surgery of the Third Military Medical University: lead
• First Affiliated Hospital of Chongqing Medical University: collaborator
• Xinqiao hospital of the third military medical university: collaborator

Study Sites (1)

Daping hospital

Chongqing, China

Location

MeSH Terms

Conditions

Carcinoma, Non-Small-Cell Lung

Condition Hierarchy (Ancestors)

Carcinoma, Bronchogenic; Bronchial Neoplasms; Lung Neoplasms; Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms; Lung Diseases; Respiratory Tract Diseases

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: CROSS SECTIONAL
• Target Duration: 1 Month
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: July 3, 2025
• First Posted: July 15, 2025
• Study Start: December 13, 2023
• Primary Completion: January 1, 2025
• Study Completion: January 1, 2025
• Last Updated: August 6, 2025

Record last verified: 2025-05

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP, ICF, CSR, ANALYTIC CODE

Locations

CN (1)