A Stacked Multimodality Model Based on Functional MRI Features and Deep Learning Radiomics for Predicting the Early Response to Radiotherapy in Nasopharyngeal Carcinoma.

Journal: Academic radiology
PMID: 39496536

Abstract

BACKGROUND: This study aimed to construct and assess a comprehensive model that integrates MRI-derived deep learning radiomics, functional imaging (fMRI), and clinical indicators to predict early efficacy of radiotherapy in nasopharyngeal carcinoma (NPC).

Authors

  • Xiaowen Wang
    Bioresources Green Transformation Collaborative Innovation Center of Hubei Province, College of Life Sciences, Hubei University, Wuhan 430062, Hubei, China.
  • Jian Song
    School of International Studies, Sun Yat-sen University, Guangzhou, China.
  • Qingtao Qiu
    Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, China.
  • Ya Su
    Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.
  • Lizhen Wang
    School of Biological Science and Medical Engineering, Beihang University, Xueyuan Road No. 37, Haidian District, Beijing 100191, China.
  • Xiujuan Cao
    Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China (X.W., X.C.). Electronic address: cxsj2008@163.com.

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