ORIGINAL RESEARCH article

Front. Oncol.

Sec. Gastrointestinal Cancers: Gastric and Esophageal Cancers

Volume 15 - 2025 | doi: 10.3389/fonc.2025.1613972

Combining Radiomics and Deep Learning to Predict Liver Metastasis of Gastric Cancer on CT Image

Provisionally accepted
Yimin  GuoYimin Guo1,2Haixiang  YinHaixiang Yin2,3Hanyue  ZhangHanyue Zhang1,2Pan  LiangPan Liang1,2Jianbo  GaoJianbo Gao1,2Ming  ChengMing Cheng3,4*
  • 1Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
  • 2Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, Zhengzhou, Henan Province, China
  • 3Department of Medical Information, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
  • 4Institute of Interconnected Intelligent Health Management of Henan Province, Zhengzhou, China

The final, formatted version of the article will be published soon.

Objective: Our study aimed to explore the potential of deep learning (DL) radiomics features from CT images of primary gastric cancer (GC) in predicting gastric cancer liver metastasis (GCLM) by establishing and verifying a prediction model based on clinical factors, classical radiomics and DL features. Methods: We retrospectively analyzed 1001 pathologically confirmed GC patients from June 2014 to May 2024, divided into non-LM (n=689) and LM groups (n=312). CT-based classic radiomics and DL features were extracted and screened to construct a DL-radiomics score. This score, along with statistically significant clinical factors, was used to build a fused model which visualized as a nomogram. The model's predictive performance, calibration, and clinical utility were assessed and compared against a clinical model. Additionally, the DL-radiomics score's role in distinguishing between synchronous and metachronous GCLM was evaluated.Results: The fused model showed good predictive performance [AUC: 0.796 (95% CI: 0.766-0.826) in training cohort and 0.787 (95 % CI: 0.741-0.834) in test cohort], outperforming the clinical model, radiomics score and DL score (P<0.05). In addition, the decision curve confirmed that the model provided the largest clinical net benefit compared with all other models in the relevant threshold. DL-radiomics score showed moderate predictive performance in distinguishing between synchronous GCLM and metachronous GCLM, with an AUC of 0.665 (95% CI, 0.613-0.718).The CT-based fused model has demonstrated significant value in predicting the occurrence of GCLM, and can provide a reference for the personalized follow-up and treatment of patients.

Keywords: deep learning, radiomics nomogram, gastric cancer, liver metastasis, computed tomography

Received: 18 Apr 2025; Accepted: 03 Jun 2025.

Copyright: © 2025 Guo, Yin, Zhang, Liang, Gao and Cheng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Ming Cheng, Department of Medical Information, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China

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