F-FDG PET/CT Radiomics-Based Multimodality Fusion Model for Preoperative Individualized Noninvasive Prediction of Peritoneal Metastasis in Advanced Gastric Cancer.

Journal: Annals of surgical oncology
PMID: 38976160

Abstract

PURPOSE: This study was designed to develop and validate a machine learning-based, multimodality fusion (MMF) model using F-fluorodeoxyglucose (FDG) PET/CT radiomics and kernelled support tensor machine (KSTM), integrated with clinical factors and nuclear medicine experts' diagnoses to individually predict peritoneal metastasis (PM) in advanced gastric cancer (AGC).

Authors

  • Hao Chen
    The First School of Medicine, Wenzhou Medical University, Wenzhou, China.
  • Yi Chen
    Department of Anesthesiology and Perioperative Medicine, General Hospital of Ningxia Medical University, Yinchuan, China.
  • Ye Dong
    Nanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China.
  • Longfei Gou
    Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China.
  • Yanfeng Hu
    Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China.
  • Quanshi Wang
    Nanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China.
  • Guoxin Li
    Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China. gzliguoxin@163.com caishirong@yeah.net ehbhltj@hotmail.com keekee77@126.com.
  • Shulong Li
    Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China.
  • Jiang Yu
    Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China.

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