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A deep learning model based on MRI for prediction of vessels encapsulating tumour clusters and prognosis in hepatocellular carcinoma

  • Hepatobiliary
  • Published:
Abdominal Radiology Aims and scope Submit manuscript

Abstract

Purpose

This study aimed to build and evaluate a deep learning (DL) model to predict vessels encapsulating tumor clusters (VETC) and prognosis preoperatively in patients with hepatocellular carcinoma (HCC).

Methods

320 pathologically confirmed HCC patients (58 women and 262 men) from two hospitals were included in this retrospective study. Institution 1 (n = 219) and Institution 2 (n = 101) served as the training and external test cohorts, respectively. Tumors were evaluated three-dimensionally and regions of interest were segmented manually in the arterial, portal venous, and delayed phases (AP, PP, and DP). Three ResNet-34 DL models were developed, consisting of three models based on a single sequence. The fusion model was developed by inputting the prediction probability of the output from the three single-sequence models into logistic regression. The area under the receiver operating characteristic curve (AUC) was used to compare performance, and the Delong test was used to compare AUCs. Early recurrence (ER) was defined as recurrence within two years of surgery and early recurrence-free survival (ERFS) rate was evaluated by Kaplan–Meier survival analysis.

Results

Among the 320 HCC patients, 227 were VETC− and 93 were VETC+ . In the external test cohort, the fusion model showed an AUC of 0.772, a sensitivity of 0.80, and a specificity of 0.61. The fusion model-based prediction of VETC high-risk and low-risk categories exhibits a significant difference in ERFS rates, akin to the outcomes observed in VETC + and VETC− confirmed through pathological analyses (p < 0.05).

Conclusions

A DL framework based on ResNet-34 has demonstrated potential in facilitating non-invasive prediction of VETC as well as patient prognosis.

Graphical abstract

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Abbreviations

HCC:

Hepatocellular carcinoma

VETC:

Vessels encapsulating tumor cluster

DCE-MRI:

Dynamic contrast-enhanced MRI

DL:

Deep learning

AUC:

Area under the receiver operating characteristic curve

AFP:

Alpha-fetoprotein

AP:

Arterial phase

DP:

Delayed phase

PP:

Portal-venous phase

ALB:

Serum albumin

ALT:

Alanine aminotransferase

AST:

Aspartate aminotransferase

GGT:

Gamma-glutamyl transferase

HBV:

Hepatitis B virus

PT:

Prothrombin time

BCLC:

Barcelona clinic liver cancer

CI:

Confidence interval

IQR:

Interquartile range

ROC:

Receiver operating characteristic

ROI:

Region of interest

DSC:

Dice similarity coefficient

Gd-EOB-DTPA:

Gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid

HBP:

Hepatobiliary phase

ER:

Early recurrence

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Funding

This study was supported by the National Natural Science Foundation of China (82071988), the Scientific Research Fund of Enze Medical Center (KY201912270039), Provincial Medical and Health Plan Class A (KY202003020073) and the Special Fund for Basic Scientific Research Business Expenses of Zhejiang University (2021FZZX003-02-17 and 226-2022-00160).

Author information

Author notes

  1. Wenbin Ji

    Present address: Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, 150 Ximen St, Linhai, Taizhou, 317000, Zhejiang, China

  2. Jiawen Yang and Xue Dong have contributed equally to this work.

  3. Hongjie Hu and Wenbin Ji have contributed equally to the work.

Authors and Affiliations

  1. Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, 150 Ximen St, Linhai, Taizhou, 317000, Zhejiang, China

    Jiawen Yang, Binhao Zhang, Huangqi Zhang & Wenting Pan

  2. Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China

    Xue Dong

  3. Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, People’s Republic of China

    Fang Wang & Hongjie Hu

  4. Department of Radiology, Taizhou Hospital of Zhejiang Province, Shaoxing University, Taizhou, 318000, Zhejiang, China

    Shengze Jin

  5. Department of Pathology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, 317000, Zhejiang, China

    Meifu Gan

  6. GE Healthcare, Precision Health Institution, Pudong New Town, No.1, Huatuo Road, Shanghai, 210000, China

    Shaofeng Duan

  7. Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China

    Limin Zhang

  8. Department of Radiology, Taizhou Hospital, Zhejiang University, Taizhou, 318000, Zhejiang, China

    Wenbin Ji

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  1. Jiawen Yang
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Correspondence to Wenbin Ji.

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Yang, J., Dong, X., Wang, F. et al. A deep learning model based on MRI for prediction of vessels encapsulating tumour clusters and prognosis in hepatocellular carcinoma. Abdom Radiol 49, 1074–1083 (2024). https://doi.org/10.1007/s00261-023-04141-3

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  • DOI: https://doi.org/10.1007/s00261-023-04141-3

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