AI Models for Predicting Occult Pleural Dissemination in NSCLC

Army Medical University of People's Liberation Army

Status: Completed

Conditions: Non-Small Cell Lung Cancer

Study type: Observational

Funder types: Other

Identifiers

NCT07065422

GUOW

Details and patient eligibility

About

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.

Enrollment: 326 patients

Sex: All

Volunteers: No Healthy Volunteers

Inclusion criteria

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

Exclusion criteria

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

Trial design

326 participants in 1 patient group

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