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).
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The scientific guarantor of this publication is Huijun Chen.
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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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DOI: https://doi.org/10.1007/s00330-023-09953-x