Abstract
Objectives
To develop and validate a radiomics nomogram for preoperative prediction of microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC).
Methods
The study included 157 patients with histologically confirmed HCC with or without MVI, and 110 patients were allocated to the training dataset and 47 to the validation dataset. Baseline clinical factor (CF) data were collected from our medical records, and radiomics features were extracted from the artery phase (AP), portal venous phase (PVP) and delay phase (DP) of preoperatively acquired CT in all patients. Radiomics analysis included tumour segmentation, feature extraction, model construction and model evaluation. A final nomogram for predicting MVI of HCC was established. Nomogram performance was assessed via both calibration and discrimination statistics.
Results
Five AP features, seven PVP features and nine DP features were effective for MVI prediction in HCC radiomics signatures. PVP radiomics signatures exhibited better performance than AP and DP radiomics signatures in the validation datasets, with the AUC 0.793. In the clinical model, age, maximum tumour diameter, alpha-fetoprotein and hepatitis B antigen were effective predictors. The final nomogram integrated the PVP radiomics signature and four CFs. Good calibration was achieved for the nomogram in both the training and validated datasets, with respective C-indexes of 0.827 and 0.820. Decision curve analysis suggested that the proposed nomogram was clinically useful, with a corresponding net benefit of 0.357.
Conclusions
The above-described radiomics nomogram can preoperatively predict MVI in patients with HCC and may constitute a usefully clinical tool to guide subsequent personalised treatment.
Key Points
• No previously reported study has utilised radiomics nomograms to preoperatively predict the MVI of HCC using 3D contrast-enhanced CT imaging.
• The combined radiomics clinical factor (CF) nomogram for predicting MVI achieved superior performance than either the radiomics signature or the CF nomogram alone.
• Nomograms combing PVP radiomics and CF may be useful as an imaging marker for predicting MVI of HCC preoperatively and could guide personalised treatment.
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Abbreviations
- AFP:
-
Alpha-fetoprotein
- AP:
-
Arterial phase
- AUC:
-
Area under the curve
- CECT:
-
Contrast-enhanced computed tomography
- CF:
-
Clinical factor
- CI:
-
Confidence interval
- CT:
-
Computed tomography
- DP:
-
Delay phase
- HBsAg:
-
Hepatitis B surface antigen
- HCC:
-
Hepatocellular carcinoma
- ICC:
-
Intraclass correlation coefficient
- LASSO:
-
Least absolute shrinkage and selection operator
- LR:
-
Liver resection
- MR:
-
Magnetic resonance
- MTD:
-
Maximum tumour diameter
- MVI:
-
Microvascular invasion
- OR:
-
Odds ratio
- PACS:
-
Picture archiving and communication system
- PVP:
-
Portal venous phase
- ROI:
-
Region of interest
- RVI:
-
Radiogenomic venous invasion
- SVM:
-
Support vector machine
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Funding
This study has received funding from the CAMS Innovation Fund for Medical Sciences (CIFMS) (2016-I2M-1-001), PUMC Youth Fund (2017320010), Chinese Academy of Medical Sciences (CAMS) Research Fund (ZZ2016B01), National Natural Science Foundation of China (81227901, 61231004) and National Key R&D Program of China (2017YFA0205200, 2017YFC1309100).
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The scientific guarantor of this publication is Jie Tian.
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The authors declare that they have no conflict of interest.
Statistics and biometry
**gwei Wei kindly provided statistical advice for this manuscript.
One of the authors (Dongsheng Gu) has significant statistical expertise.
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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Institutional review board approval was obtained.
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• Retrospective
• Diagnostic or prognostic study
• Performed at one institution
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Ma, X., Wei, J., Gu, D. et al. Preoperative radiomics nomogram for microvascular invasion prediction in hepatocellular carcinoma using contrast-enhanced CT. Eur Radiol 29, 3595–3605 (2019). https://doi.org/10.1007/s00330-018-5985-y
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DOI: https://doi.org/10.1007/s00330-018-5985-y