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Predicting microvascular invasion in hepatocellular carcinoma with a CT- and MRI-based multimodal deep learning model

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

Abstract

Purpose

To investigate the value of a multimodal deep learning (MDL) model based on computed tomography (CT) and magnetic resonance imaging (MRI) for predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC).

Methods

A total of 287 patients with HCC from our institution and 58 patients from another individual institution were included. Among these, 119 patients with only CT data and 116 patients with only MRI data were selected for single-modality deep learning model development, after which select parameters were migrated for MDL model development with transfer learning (TL). In addition, 110 patients with simultaneous CT and MRI data were divided into a training cohort (n = 66) and a validation cohort (n = 44). We input the features extracted from DenseNet121 into an extreme learning machine (ELM) classifier to construct a classification model.

Results

The area under the curve (AUC) of the MDL model was 0.844, which was superior to that of the single-phase CT (AUC = 0.706–0.776, P < 0.05), single-sequence MRI (AUC = 0.706–0.717, P < 0.05), single-modality DL model (AUCall-phase CT = 0.722, AUCall-sequence MRI = 0.731; P < 0.05), clinical (AUC = 0.648, P < 0.05), but not to that of the delay phase (DP) and in-phase (IP) MRI and portal venous phase (PVP) CT models. The MDL model achieved better performance than models described above (P < 0.05). When combined with clinical features, the AUC of the MDL model increased from 0.844 to 0.871. A nomogram, combining deep learning signatures (DLS) and clinical indicators for MDL models, demonstrated a greater overall net gain than the MDL models (P < 0.05).

Conclusion

The MDL model is a valuable noninvasive technique for preoperatively predicting MVI in HCC.

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Data availability

The study subjects or cohorts in our study have not been previously reported.

Abbreviations

ACC:

Accuracy

AFP:

α-Fetoprotein

ALP:

Alkaline phosphatase

ALT:

Alanine aminotransferase

AP:

Arterial phase

AST:

Aspartate aminotransaminase

AUC:

Area under the ROC curve

CA19-9:

Carbohydrate antigen 19-9

DBIL:

Direct bilirubin

DCA:

Decision curve analysis

DL:

Deep learning

DLS:

Deep learning signatures

DP:

Delayed phase

ELM:

Extreme learning machine

IBIL:

Indirect bilirubin

IDI:

Integrated discrimination improvement

IP:

In-phase

MMD:

Maximum mean discrepancy

NPV:

Negative predictive value

OP:

Opposed-phase

OS:

Overall survival

PCP:

Precontrast phase

PLT:

Platelet count

PPV:

Positive predictive value

PVP:

Portal venous phase

ROC:

Receiver operating characteristic

ROI:

Region of interest

SEN:

Sensitivity

SPC:

Specificity

T2WI:

T2-weighted imaging

TACE:

Transarterial chemoembolization

TBIL:

Total bilirubin

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Funding

This study is funded by Guangdong Basic and Applied Basic Research Foundation (Grant Number: 2021A1515220080).

Author information

Author notes

  1. Yan Lei and Bao Feng have contributed equally to this study.

Authors and Affiliations

  1. Department of Radiology, Jiangmen Central Hospital, 23 Beijie Haibang Street, Jiangmen, People’s Republic of China

    Yan Lei, Meiqi Wan, Jin Cui, Changyi Ma, Changyin Yao, Shiman Gan & Enming Cui

  2. Zunyi Medical University, 1 Xiaoyuan Road, Zunyi, People’s Republic of China

    Yan Lei, Meiqi Wan & Enming Cui

  3. Laboratory of Intelligent Detection and Information Processing, School of Electronic Information and Automation, Guilin University of Aerospace Technology, 2 Jinji Road, Guilin, People’s Republic of China

    Bao Feng, Kuncai Xu & Jiangfeng Shi

  4. Department of Radiology, Yuebei People’s Hospital, 133 Huimin Street, Shaoguan, People’s Republic of China

    Junqi Sun

  5. Guangdong Medical University, 2 Wenming East Road, Zhanjiang, People’s Republic of China

    Changyin Yao, Shiman Gan & Enming Cui

  6. Jiangmen Key Laboratory of Artificial Intelligence in Medical Image Computation and Application, 23 Beijie Haibang Street, Jiangmen, People’s Republic of China

    Enming Cui

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  1. Yan Lei
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Contributions

Conceptualization: EC, BF, YL; methodology: EC, BF, YL; formal analysis and investigation: KX, JC, CM; data collection and analysis: YL, MW, JS, CY, SG, JS; writing—original draft preparation: YL, KX; writing—review and editing: EC; funding acquisition: EC; resources: EC, BF; supervision: EC, BF. All authors contributed to the study conception and design. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Enming Cui.

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Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Ethical approval

This retrospective study was approved by institutional review board.

Informed consent

Our hospital ethics committee approved this retrospective study and waived patient informed consent.

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Lei, Y., Feng, B., Wan, M. et al. Predicting microvascular invasion in hepatocellular carcinoma with a CT- and MRI-based multimodal deep learning model. Abdom Radiol (2024). https://doi.org/10.1007/s00261-024-04202-1

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