Development and validation of a machine learning-based 18F-fluorodeoxyglucose PET/CT radiomics signature for predicting gastric cancer survival

Author:

Zhi Huaiqing,Xiang Yilan,Chen Chenbin,Zhang Weiteng,Lin Jie,Gao Zekan,Shen Qingzheng,Shao Jiancan,Yang Xinxin,Yang Yunjun,Chen Xiaodong,Zheng Jingwei,Lu Mingdong,Pan Bujian,Dong Qiantong,Shen Xian,Ma Chunxue

Abstract

Abstract Background Survival prognosis of patients with gastric cancer (GC) often influences physicians’ choice of their follow-up treatment. This study aimed to develop a positron emission tomography (PET)-based radiomics model combined with clinical tumor-node-metastasis (TNM) staging to predict overall survival (OS) in patients with GC. Methods We reviewed the clinical information of a total of 327 patients with pathological confirmation of GC undergoing 18 F-fluorodeoxyglucose (18 F-FDG) PET scans. The patients were randomly classified into training (n = 229) and validation (n = 98) cohorts. We extracted 171 PET radiomics features from the PET images and determined the PET radiomics scores (RS) using the least absolute shrinkage and selection operator (LASSO) and random survival forest (RSF). A radiomics model, including PET RS and clinical TNM staging, was constructed to predict the OS of patients with GC. This model was evaluated for discrimination, calibration, and clinical usefulness. Results On multivariate COX regression analysis, the difference between age, carcinoembryonic antigen (CEA), clinical TNM, and PET RS in GC patients was statistically significant (p < 0.05). A radiomics model was developed based on the results of COX regression. The model had the Harrell’s concordance index (C-index) of 0.817 in the training cohort and 0.707 in the validation cohort and performed better than a single clinical model and a model with clinical features combined with clinical TNM staging. Further analyses showed higher PET RS in patients who were older (p < 0.001) and those who had elevated CEA (p < 0.001) and higher clinical TNM (p < 0.001). At different clinical TNM stages, a higher PET RS was associated with a worse survival prognosis. Conclusions Radiomics models based on PET RS, clinical TNM, and clinical features may provide new tools for predicting OS in patients with GC.

Funder

China University-Industry Research and Innovation Fund - Huatong Guokang Medical Research Special Project

Zhejiang Provincial Health Department Medical Support Discipline-Nutrition

the Special Fund of Zhejiang Upper Gastrointestinal Tumor Diagnosis and Treatment Technology Research Center

the Fund of the Society of Parenteral and Enteral Nutrition of Chinese Medical Association

the National Natural Science Foundation of China

the Project of Zhejiang Provincial Health Technology

Publisher

Springer Science and Business Media LLC

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