Cancer Imaging (Jul 2024)

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

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

DOI
https://doi.org/10.1186/s40644-024-00741-4
Journal volume & issue
Vol. 24, no. 1
pp. 1 – 11

Abstract

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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.

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