Ignite Creation Date:
2026-03-26 @ 3:18 PM
Ignite Modification Date:
2026-03-31 @ 1:56 AM
Study NCT ID:
NCT07449858
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2026-03-04
First Post:
2026-02-26
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
18F-FDG PET/CT Radiomics Models for Precision Diagnosis and Prognosis in NSCLC
Sponsor:
Second Affiliated Hospital, School of Medicine, Zhejiang University
Organization:
Study Overview
Official Title:
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
Status:
ACTIVE_NOT_RECRUITING
Status Verified Date:
2026-02
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
PET-Rad-NSCLC
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.
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.
Study Oversight
Has Oversight DMC:
True
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?: