Non Small Cell Lung Cancer
Conditions
Keywords
18F-FDG PET/CT, Radiomics, Artificial Intelligence, EGFR Mutation, Prognosis
Brief summary
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.
Detailed description
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.
Interventions
None listed
Sponsors
Second Affiliated Hospital, School of Medicine, Zhejiang University
First Hospital of China Medical University
West China Hospital
Guangdong Second Provincial General Hospital
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Age \>= 18 years. * Underwent standard whole-body 18F-FDG PET/CT scan within 30 days before surgery. * Histopathologically confirmed Non-Small Cell Lung Cancer (NSCLC) with clear histological subtyping and complete postoperative TNM staging. * Primary tumor SUVmax \> 2.5 and maximum diameter \> 1.0 cm on CT. * Complete clinical, pathological, and imaging data available. * (For Gene Sub-study) Known EGFR gene mutation status. * (For Prognosis Sub-study) Complete follow-up data available (minimum 12 months or until endpoint event).
Exclusion criteria
* History of other malignancies. * Received any anti-tumor treatment (chemotherapy, radiotherapy, targeted therapy, immunotherapy) prior to PET/CT. * Severe image artifacts or indistinct tumor boundaries affecting ROI delineation. * Missing key clinical or pathological data. * Baseline PET/CT evaluated recurrent or metastatic tumors instead of primary NSCLC. * Extremely short life expectancy due to severe comorbidities.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Diagnostic Performance for TNM Staging and Histological Subtyping | Baseline | Assessed by the Area Under the Receiver Operating Characteristic Curve (AUC), Sensitivity, and Specificity of the radiomics model in predicting T-stage, N-stage, and histological subtypes (ADC vs. SCC). |
| Predictive Accuracy for EGFR Mutation Status | Baseline | Assessed by the AUC, Sensitivity, and Specificity of the radiomics model in discriminating EGFR mutation status (positive vs. negative) compared to genetic testing results. |
| Prognostic Value | From date of surgery up to 5 years | Evaluation of Disease-Free Survival (DFS) and Overall Survival (OS). DFS is defined as time to recurrence or death. OS is defined as time to death from any cause. |
Countries
China
Contacts
PRINCIPAL_INVESTIGATORXiaohui Zhang
2nd Affiliated Hospital, School of Medicine, Zhejiang University, China
Outcome results
None listed