Multimodal AI Predicts High-risk Pathology in Lung Cancer Associated With Cystic Airspaces
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18+
SponsorCentral South University
About this trial
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
This trial does not accept healthy volunteersQualifiers
1. Patients with non-small cell lung cancer (NSCLC) confirmed by biopsy or postoperative pathological examination.
Disqualifiers
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.
Trial population
This observational study will include patients with pathologically confirmed non-small cell lung cancer (NSCLC) who underwent pulmonary tumor resection and had preoperative CT features consistent with lung cancer associated with cystic airspaces (LCCA). Patients with complete imaging, clinical, and pathological data will be included for model development and validation.
Trial design
Design model
Cohort
Time perspective
Prospective
Treatments tested in this trial
Not listed
Trial groups
600 Participants
are grouped into 2 trial groupsGroup A: Group 1
Group B: Group 2
Trial outcomes
Primary outcomes
1
Predictive performance of the AI-based multimodal radiomics model for pathological high-risk features in LCCA
Pathological high-risk features will be determined based on the final postoperative pathological examination. The predictive performance of the AI-based multimodal model will be quantified using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value.
Time frame
Within 30 days after surgerySecondary outcomes
1
Area under the receiver operating characteristic curve of the multimodal, CT imaging, and tabular models for pathological high-risk features in LCCA
The predictive performance of the AI-based multimodal model will be compared with that of the CT imaging model and the tabular model for predicting pathological high-risk features. Model performance will be quantified using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value. Differences in AUC between models will be evaluated using DeLong's test.
Time frame
Within 30 days after surgerySponsors and contacts
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Source ClinicalTrials.gov