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已发表论文
基于对比增强超声的深度学习用于肝细胞癌肝切除术后早期复发的术前预测
Authors Liu D, Yang K, Zhang C , Hu Z, Cheng Y, Liu Y
Received 21 July 2025
Accepted for publication 15 October 2025
Published 21 October 2025 Volume 2025:18 Pages 6829—6841
DOI https://doi.org/10.2147/JMDH.S555110
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr David C. Mohr
Dan Liu,1,* Ke Yang,2,* Chunquan Zhang,1,* Zhen Hu,1 Yuan Cheng,1 Yunping Liu1
1Department of Ultrasound, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, People’s Republic of China; 2The First In-Patient Department, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, 330006, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Dan Liu, Department of Ultrasound, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, No. 1, Minde Road, Donghu District, Nanchang, Jiangxi, 330006, People’s Republic of China, Tel +86-15170076809, Email ultrald@163.com
Background: Hepatectomy is the primary curative treatment for early-stage hepatocellular carcinoma (HCC). However, post-operative early recurrence remains a significant challenge. The existing predictive approaches based on clinicopathological features lack sufficient accuracy. This study aimed to develop a deep learning (DL) framework using contrast-enhanced ultrasound (CEUS) to preoperatively predict early recurrence in patients with HCC undergoing hepatectomy.
Patients and Methods: In this retrospective study, a total of 115 patients with early-stage HCC who underwent preoperative CEUS were randomly divided into training (n=75) and validation (n=40) cohorts. Four DL models (CEUS-AP, CEUS-PP, CEUS-LP, and CEUS-MP) were developed using single-phase and multiphase CEUS cines. The CEUS-MP model, which integrated the arterial, portal venous, and late phases, was further combined with the clinical variables to construct a nomogram. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity.
Results: The CEUS-MP model demonstrated superior predictive performance, with AUCs of 0.922 and 0.840 in the training and validation cohorts, respectively, significantly outperforming single-phase models (all P < 0.05). The combined nomogram achieved AUCs of 0.945 and 0.871 in the training and validation cohorts, respectively, with a high sensitivity (88.9% and 83.0%, respectively) and specificity (98.1% and 82.5%, respectively). Decision curve analysis confirmed the nomogram’s clinical utility for threshold probabilities > 30%. Visualization maps highlighted heterogeneous enhancement patterns as the key predictive features.
Conclusion: The DL-based CEUS framework, particularly when integrated with clinical variables, provides a noninvasive and accurate tool for the preoperative prediction of early recurrence in patients with HCC undergoing hepatectomy.
Keywords: contrast-enhanced ultrasound, hepatocellular carcinoma, recurrence, deep learning
1Department of Ultrasound, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, People’s Republic of China; 2The First In-Patient Department, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, 330006, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Dan Liu, Department of Ultrasound, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, No. 1, Minde Road, Donghu District, Nanchang, Jiangxi, 330006, People’s Republic of China, Tel +86-15170076809, Email ultrald@163.com
Background: Hepatectomy is the primary curative treatment for early-stage hepatocellular carcinoma (HCC). However, post-operative early recurrence remains a significant challenge. The existing predictive approaches based on clinicopathological features lack sufficient accuracy. This study aimed to develop a deep learning (DL) framework using contrast-enhanced ultrasound (CEUS) to preoperatively predict early recurrence in patients with HCC undergoing hepatectomy.
Patients and Methods: In this retrospective study, a total of 115 patients with early-stage HCC who underwent preoperative CEUS were randomly divided into training (n=75) and validation (n=40) cohorts. Four DL models (CEUS-AP, CEUS-PP, CEUS-LP, and CEUS-MP) were developed using single-phase and multiphase CEUS cines. The CEUS-MP model, which integrated the arterial, portal venous, and late phases, was further combined with the clinical variables to construct a nomogram. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity.
Results: The CEUS-MP model demonstrated superior predictive performance, with AUCs of 0.922 and 0.840 in the training and validation cohorts, respectively, significantly outperforming single-phase models (all P < 0.05). The combined nomogram achieved AUCs of 0.945 and 0.871 in the training and validation cohorts, respectively, with a high sensitivity (88.9% and 83.0%, respectively) and specificity (98.1% and 82.5%, respectively). Decision curve analysis confirmed the nomogram’s clinical utility for threshold probabilities > 30%. Visualization maps highlighted heterogeneous enhancement patterns as the key predictive features.
Conclusion: The DL-based CEUS framework, particularly when integrated with clinical variables, provides a noninvasive and accurate tool for the preoperative prediction of early recurrence in patients with HCC undergoing hepatectomy.
Keywords: contrast-enhanced ultrasound, hepatocellular carcinoma, recurrence, deep learning