Integrating Multi-Omics Data for Enhanced Prognosis Prediction in Gastric Cancer Post-Neoadjuvant Therapy
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 179
- 试验地点
- 1
- 主要终点
- the Area Under the Curve
研究概览
简要总结
Study Protocol: Integrating Multi-Omics Data for Prognosis Prediction in Gastric Cancer Post-Neoadjuvant Therapy
Objective:
To develop and validate an integrative prognostic nomogram for patients with locally advanced gastric cancer (LAGC) undergoing neoadjuvant therapy, combining deep learning-derived radiomic features (DeepScore), transcriptome-based immune scores (ImmuneScore), and ypTNM staging.
Study Design:
A retrospective, single-center cohort study.
Participants:
A total of 179 LAGC patients who received neoadjuvant therapy followed by radical gastrectomy at Fujian Medical University Union Hospital between January 2019 and December 2022. Patients were divided into a training cohort (n = 125) and an independent validation cohort (n = 54).
Data Collection:
Baseline contrast-enhanced CT scans prior to neoadjuvant therapy were used for radiomic analysis. Postoperative tumor RNA sequencing data were used for immune profiling. Clinical and pathological data, including ypTNM stage, were collected from medical records.
Methods:
DeepScore: Extracted from CT images using a ResNet18-based deep learning model. Significant features were selected via univariate Cox and LASSO regression.
ImmuneScore: Calculated from RNA-seq data using the ESTIMATE algorithm to assess tumor immune infiltration.
Nomogram Construction: A multi-omics nomogram was developed using multivariate Cox regression incorporating DeepScore, ImmuneScore, and ypTNM stage.
Validation: Model performance was evaluated using time-dependent ROC analysis (AUC) and Kaplan-Meier survival analysis with log-rank tests in both cohorts.
Primary Outcomes:
Disease-free survival (DFS) and overall survival (OS).
Statistical Analysis:
Survival analyses were performed using Kaplan-Meier and Cox regression models. AUC values were computed for 1-, 2-, and 3-year DFS predictions. All analyses were conducted in R (v4.4.3).
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Gastric adenocarcinoma confirmed pathologically via gastroscopy;
- •Clinical staging of cT3/T4N0/+M0 with a history of receiving at least two cycles of neoadjuvant therapy
- •No prior history of other malignant tumors
- •Completion of radical gastrectomy
排除标准
- •Gastric cancer originating from the remnant stomach
- •Absence of baseline computed tomography (CT) data prior to treatment or suboptimal CT image quality that could compromise the accuracy of radiomic information extraction
- •Absence of postoperative transcriptome data
结局指标
主要结局
the Area Under the Curve
时间窗: 2023.01.31-2025.05.31
The model's predictive accuracy was evaluated by computing the Area Under the Curve for predicting 1-year, 2-year, and 3-year disease-free survival.
次要结局
- Disease-free survival(2023.01.31-2025.05.31)
研究者
Chang-Ming Huang, Prof.
Prof.
Fujian Medical University