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已发表论文
基于体素内不相干运动成像的 Transformer 深度学习模型用于预测肝细胞癌肝切除术后早期复发
Authors Li H, Qiu Z , Zhang J, Chen Y, Liu B, Zheng Z, Qin X, Yan C, Zhou W, Xu Y
Received 12 September 2025
Accepted for publication 24 January 2026
Published 4 February 2026 Volume 2026:13 564217
DOI https://doi.org/10.2147/JHC.S564217
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr David Gerber
Hongxiang Li,1,* Zehong Qiu,2,* Jing Zhang,1,* Yang Chen,2 Baoer Liu,1 Zeyu Zheng,1 Xiang Qin,1 Chenggong Yan,1 Wu Zhou,2 Yikai Xu1
1Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China; 2School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Yikai Xu, Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China, Email yikai.xu@163.com Wu Zhou, School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, People’s Republic of China, Email zhouwu@gzucm.edu.cn
Background: This study aimed to develop and validate a transformer framework-based deep learning (DL) network using intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) to predict early recurrence in hepatocellular carcinoma (HCC).
Materials and Methods: This retrospective study included 122 patients with HCC who underwent magnetic resonance imaging examination, including an IVIM-DWI sequence with nine b-values, before resection. These were divided into training (n=85) and test (n=37) sets. A vision transformer (ViT) framework-based DL was developed to predict early recurrence in HCC. Deep features were extracted from nine b-value DWI images and IVIM parametric maps and fused to construct the fused DL (ViT-fDL) prediction model. A clinical model was constructed using multivariate logistic regression analysis. A combined model was constructed using deep features from the ViT-fDL model and clinical independent features. The performances of the models were evaluated by discrimination, calibration, and clinical applicability.
Results: Among 122 patients (108 males,14 females; mean age, 51.0 ± 11.9 years), 49 (40.1%) experienced early recurrence. The respective areas under the curve for the training and test sets were 0.755 (95% Confidence interval (CI), 0.650– 0.842) and 0.764 (95% CI, 0.596– 0.887) using the clinical model, 0.968 (95% CI, 0.905– 0.994) and 0.815 (95% CI, 0.653– 0.923) using the ViT-fDL model, and 0.991 (95% CI, 0.940– 1.000) and 0.821 (95% CI, 0.660– 0.927) using the combined model.
Conclusion: The ViT-fDL model based on IVIM can be useful for preoperative prediction early recurrence in HCC. The combined model was a more effective and precise prediction tool than other models, promising to guide individualized postoperative monitoring.
Keywords: hepatocellular carcinoma, early recurrence, deep learning, vision transformer
1Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China; 2School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Yikai Xu, Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China, Email yikai.xu@163.com Wu Zhou, School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, People’s Republic of China, Email zhouwu@gzucm.edu.cn
Background: This study aimed to develop and validate a transformer framework-based deep learning (DL) network using intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) to predict early recurrence in hepatocellular carcinoma (HCC).
Materials and Methods: This retrospective study included 122 patients with HCC who underwent magnetic resonance imaging examination, including an IVIM-DWI sequence with nine b-values, before resection. These were divided into training (n=85) and test (n=37) sets. A vision transformer (ViT) framework-based DL was developed to predict early recurrence in HCC. Deep features were extracted from nine b-value DWI images and IVIM parametric maps and fused to construct the fused DL (ViT-fDL) prediction model. A clinical model was constructed using multivariate logistic regression analysis. A combined model was constructed using deep features from the ViT-fDL model and clinical independent features. The performances of the models were evaluated by discrimination, calibration, and clinical applicability.
Results: Among 122 patients (108 males,14 females; mean age, 51.0 ± 11.9 years), 49 (40.1%) experienced early recurrence. The respective areas under the curve for the training and test sets were 0.755 (95% Confidence interval (CI), 0.650– 0.842) and 0.764 (95% CI, 0.596– 0.887) using the clinical model, 0.968 (95% CI, 0.905– 0.994) and 0.815 (95% CI, 0.653– 0.923) using the ViT-fDL model, and 0.991 (95% CI, 0.940– 1.000) and 0.821 (95% CI, 0.660– 0.927) using the combined model.
Conclusion: The ViT-fDL model based on IVIM can be useful for preoperative prediction early recurrence in HCC. The combined model was a more effective and precise prediction tool than other models, promising to guide individualized postoperative monitoring.
Keywords: hepatocellular carcinoma, early recurrence, deep learning, vision transformer