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Automatic prediction of hepatic arterial infusion chemotherapy response in advanced hepatocellular carcinoma with deep learning radiomic nomogram

  • Hepatobiliary-Pancreas
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

Hepatic arterial infusion chemotherapy (HAIC) using the FOLFOX regimen (oxaliplatin plus fluorouracil and leucovorin) is a promising option for advanced hepatocellular carcinoma (Ad-HCC). As identifying patients with Ad-HCC who would obtain objective response (OR) to HAIC preoperatively remains a challenge, we aimed to develop an automatic and non-invasive model for predicting HAIC response.

Methods

A total of 458 patients with Ad-HCC who underwent HAIC were retrospectively included from three hospitals (310 for training, 77 for internal validation, and 71 for external validation). The deep learning and radiomic features were extracted from the automatically segmented liver region on contrast-enhanced computed tomography images. Then, a deep learning radiomic nomogram (DLRN) was constructed by integrating deep learning scores, radiomic scores, and significant clinical variables with multivariate logistic regression. Model performance was assessed by AUC and Kaplan-Meier estimator.

Results

After automatic segmentation, only a few modifications were needed (less than 30 min for 458 patients). The DLRN achieved an AUC of 0.988 in the training cohort, 0.915 in the internal validation cohort, and 0.896 in the external validation cohort, respectively, outperforming other models in HAIC response prediction. Moreover, survival risk stratification was also successfully performed by the DLRN. The overall survival (OS) of the predictive OR group was significantly longer than that of the predictive non-OR group (median OS: 26.0 vs. 12.3 months, p < 0.001).

Conclusions

The DLRN provided a satisfactory performance for predicting HAIC response, which is essential to identify Ad-HCC patients for HAIC and may potentially benefit personalized pre-treatment decision-making.

Clinical relevance statement

This study presents an accurate and automatic method for predicting response to hepatic arterial infusion chemotherapy in patients with advanced hepatocellular carcinoma, and therefore help in defining the best candidates for this treatment.

Key Points

• Deep learning radiomic nomogram (DLRN) based on automatic segmentation of CECT can accurately predict hepatic arterial infusion chemotherapy (HAIC) response of advanced HCC patients.

• The proposed prediction model can perform survival risk stratification and is an easy-to-use tool for personalized pre-treatment decision-making for advanced HCC patients.

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Abbreviations

Ad-HCC:

Advanced hepatocellular carcinoma

AP:

Arterial phase

APE:

Arterial peritumoral enhancement

CECT:

Contrast-enhanced computed tomography

DLRN:

Deep learning radiomic nomogram

FCNN:

Fully connected neural network

FOLFOX:

Oxaliplatin plus fluorouracil and leucovorin

HAIC:

Hepatic arterial infusion chemotherapy

LASSO:

Least absolute shrinkage and selection operator

mRECIST:

Modified Response Evaluation Criteria in Solid Tumors

OR:

Objective response

OS:

Overall survival

PP:

Portal phase

ROC:

Receiver operating characteristic

ROI:

Region of interest

RVI:

Radiogenomic venous invasion

TKIs:

Tyrosine kinase inhibitors

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Acknowledgements

We thank the Department of Radiology of the Sun Yat-sen University Cancer Center for assistance with the collection of CT data.

Funding

This study received funding from the Beijing Municipal Natural Science Foundation (Z190024) and the Key Program of the National Natural Science Foundation of China (81930119).

Author information

Author notes

  1. Ziming Xu, Chao An, and Feng Shi contributed equally to this work.

Authors and Affiliations

  1. Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, No. 30 Shuangqing Road, Haidian District, Beijing, 100084, China

    Ziming Xu, Yuze Li, Jiaqi Dou, Yajie Wang & Huijun Chen

  2. Department of Minimal Invasive Intervention, Sun Yat-sen University Cancer Center, Guangzhou, China

    Chao An

  3. Department of Minimal Invasive Intervention, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China

    Feng Shi

  4. Department of Ultrasound, The Sixth Medical Center of Chinese PLA General Hospital, Beijing, China

    He Ren

  5. Department of Minimal Invasive Intervention, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China

    Song Chen

  6. Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, No. 45, Changchun Street, Xicheng District, Beijing, 100053, China

    Shaozhen Yan & Jie Lu

Authors
  1. Ziming Xu
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Corresponding authors

Correspondence to Jie Lu or Huijun Chen.

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Guarantor

The scientific guarantor of this publication is Huijun Chen.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

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Written informed consent was waived by the Institutional Review Board.

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• retrospective

• diagnostic or prognostic study

• multicenter study

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Xu, Z., An, C., Shi, F. et al. Automatic prediction of hepatic arterial infusion chemotherapy response in advanced hepatocellular carcinoma with deep learning radiomic nomogram. Eur Radiol 33, 9038–9051 (2023). https://doi.org/10.1007/s00330-023-09953-x

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