Construction and Validation of Precision Diagnosis and Treatment Models for Non-Small Cell Lung Cancer (NSCLC) Based on 18F-FDG PET/CT Radiomics: A Multicenter Retrospective Clinical Study
Construction and Validation of Precision Diagnosis and Treatment Models for Non-Small Cell Lung Cancer (NSCLC) Based on 18F-FDG PET/CT Radiomics: A Multicenter Retrospective Clinical Study
This multicenter retrospective study aims to investigate the value of 18F-FDG PET/CT radiomics features in the preoperative precision staging, pathological typing, gene mutation status prediction, and prognostic risk stratification of patients with Non-Small Cell Lung Cancer (NSCLC). The study involves constructing and validating machine learning models to provide imaging-based evidence for individualized precision clinical decision-making.
The study consists of three main parts based on a multicenter retrospective cohort:
Staging and Typing: Developing radiomics models to distinguish histological subtypes (Adenocarcinoma vs. Squamous Cell Carcinoma) and predict TNM staging preoperatively.
Gene Mutation Prediction: Analyzing radiomics signatures to predict EGFR mutation status (Mutant vs. Wild-type) non-invasively.
Prognostic Assessment: Evaluating the prognostic value of radiomics features by analyzing their association with Disease-Free Survival (DFS) and Overall Survival (OS).
High-throughput radiomics features will be extracted from standardized PET/CT images and analyzed using machine learning algorithms.
Inclusion Criteria:
Exclusion Criteria: