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Pre- and Post-treatment Double-Sequential-Point Dynamic Radiomic Model in the Response Prediction of Gastric Cancer to Neoadjuvant Chemotherapy: 3-Year Survival Analysis

  • Gastrointestinal Oncology
  • Published:
Annals of Surgical Oncology Aims and scope Submit manuscript

Abstract

Background

Prognosis prediction of patients with gastric cancer after neoadjuvant chemotherapy is suboptimal. This study aims to develop and validate a dynamic radiomic model for prognosis prediction of patients with gastric cancer on the basis of baseline and posttreatment features.

Patients and methods

This single-center cohort study included patients with gastric adenocarcinoma treated with neoadjuvant chemotherapy from June 2009 to July 2015 in the Gastrointestinal Cancer Center of Peking University Cancer Hospital. Their clinicopathological data, pre-treatment and post-treatment computed tomography (CT) images, and pathological reports were retrieved and analyzed. Four prediction models were developed and validated using tenfold cross-validation, with death within 3 years as the outcome. Model discrimination was compared by the area under the curve (AUC). The final radiomic model was evaluated for calibration and clinical utility using Hosmer–Lemeshow tests and decision curve analysis.

Results

The study included 205 patients with gastric adenocarcinoma [166 (81%) male; mean age 59.9 (SD 10.3) years], with 71 (34.6%) deaths occurring within 3 years. The radiomic model alone demonstrated better discrimination than the pathological T stage (ypT) stage model alone (cross-validated AUC 0.598 versus 0.516, P = 0.009). The final radiomic model, which incorporated both radiomic and clinicopathological characteristics, had a significantly higher cross-validated AUC (0.769) than the ypT stage model (0.516), the radiomics alone model (0.598), and the ypT plus other clinicopathological characteristics model (0.738; all P < 0.05). Decision curve analysis confirmed the clinical utility of the final radiomic model.

Conclusions

The developed radiomic model had good accuracy and could be used as a decision aid tool in clinical practice to differentiate prognosis of patients with gastric cancer.

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Funding

This study is supported by the National Natural Science Foundation of China (31870805), Beijing Municipal Health Commission (DFL20181103), Beijing Natural Science Foundation (no. Z180001), PKU-Baidu Fund (no. 2020BD027), and Beijing Nova Program (20220484111).

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Authors and Affiliations

  1. Gastrointestinal Cancer Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Hai-Dian District, Beijing, People’s Republic of China

    Yinkui Wang MD, Fei Shan MD, Shuangxi Li MD, Yongning Jia MD, Kan Xue MD, Rulin Miao MD, Zhemin Li MD, Ziyu Li MD & Jiafu Ji MD, PhD, DrPH

  2. Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, People’s Republic of China

    Lei Tang MD & Jiazheng Li MD

  3. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

    Xiangji Ying MPH. MS

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  1. Yinkui Wang MD
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  2. Lei Tang MD
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Correspondence to Ziyu Li MD or Jiafu Ji MD, PhD, DrPH.

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Yinkui Wang, Lei Tang, Xiangji Ying and Jiazheng Li are shared first author.

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Wang, Y., Tang, L., Ying, X. et al. Pre- and Post-treatment Double-Sequential-Point Dynamic Radiomic Model in the Response Prediction of Gastric Cancer to Neoadjuvant Chemotherapy: 3-Year Survival Analysis. Ann Surg Oncol 31, 774–782 (2024). https://doi.org/10.1245/s10434-023-14478-0

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