MedTrace Logo

AI Models for Predicting Occult Pleural Dissemination in NSCLC

NCT07065422 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 326

Last updated 2025-08-06

No results posted yet for this study

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

Sponsors & Collaborators

  • First Affiliated Hospital of Chongqing Medical University

    collaborator OTHER

  • Xinqiao hospital of the third military medical university

    collaborator UNKNOWN

  • Daping Hospital and the Research Institute of Surgery of the Third Military Medical University

    lead OTHER

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2023-12-13
Primary Completion
2025-01-01
Completion
2025-01-01

Countries

  • China

Study Locations

More Related Trials

Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07065422 on ClinicalTrials.gov