Deep learning-based simulated contrast-enhanced MRI for rectal cancer evaluation: a multicenter study.

Journal: Abdominal radiology (New York)
Published Date: Dec 19, 2025

Abstract

OBJECTIVES: To assess the feasibility and accuracy of using deep learning to generate simulated contrast-enhanced T1-weighted rectal MRI scans from pre-contrast MRI sequences in rectal cancer patients. METHODS: This study included 514 patients with pathologically confirmed rectal carcinoma who underwent contrast-enhanced MRI at three academic institutions. A two-dimensional generator adversarial network was used to simulate contrast-enhanced MRI scans from pre-contrast T1-weighted images. Quantitative assessments of image similarity, error metrics, and Dice coefficient for tumor overlap were performed. Three radiologists, blinded to the contrast method, evaluated image quality, tumor enhancement, and extramural vascular invasion (EMVI) in 104 paired real and simulated scans. Tumor size correlation was analyzed with intraclass correlation coefficients (ICCs), and modified Bland-Altman plots were used to assess agreement. ROC curves evaluated diagnostic performance of EMVI detection, and quality scores were compared using the McNemar test. RESULTS: Simulated scans demonstrated high similarity to real scans (structural similarity index: 0.82, Dice coefficient: 0.86 ± 0.14 for tumor overlap). Tumor size measurements correlated strongly (ICC: 0.76-0.88), with minimal differences in length (0.11 mm) and depth (0.10 mm). ROC analysis revealed slightly higher AUC values for real scans (0.702, 0.772, 0.713) compared to simulated scans (0.685, 0.694, 0.661) for three radiologists, respectively. Nearly all synthetic scans (98%) were rated diagnostically suitable. CONCLUSION: Deep learning-generated simulated contrast-enhanced MRI scans demonstrated comparability to real scans in terms of tumor size, enhancement area, and image quality, suggesting their potential as a supportive tool for diagnosis and treatment evaluation.

Authors

  • Piao Yang
    Department of Radiology, First Affiliated Hospital Zhejiang University, Hangzhou, China.
  • Nuo Tong
    Key Lab of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi'an, Shaanxi, 710071, China.
  • Zhi Li
    Department of Nursing, Zhongshan Hospital of Traditional Chinese Medicine Affiliated to Guangzhou University of Traditional Chinese Medicine, Zhongshan, China.
  • Zhan Feng
    Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, No. 79 Qingchun Road, Hangzhou, 310003, China.
  • Mei Ruan
    Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, No. 261, Huansha Road, Hangzhou, Zhejiang, China.
  • Wen Xu
    Xiangyang Central HospitalAffiliated Hospital of Hubei University of Arts and Science Xiangyang 441000 China.
  • Qi Zhong
    School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China.
  • Zhuoxin Fu
    School of Artificial Intelligence and Data Science, Hebei University of Technology, Tiajin, China.
  • Tianye Niu
    Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
  • Feng Chen
    Department of Integrated Care Management Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China.

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