Abstract
Purpose
To assess the methodological quality and to evaluate the predictive performance of radiomics studies for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC).
Methods
Publications between 2017 and 2021 on radiomic MVI prediction in HCC based on CT, MR, ultrasound, and PET/CT were included. The risk of bias was assessed using the prediction model risk of bias assessment tool (PROBAST). Methodological quality was assessed through the radiomics quality score (RQS). Fourteen studies classified as TRIPOD Type 2a or above were used for meta-analysis using random-effects model. Further analyses were performed to investigate the technical factors influencing the predictive performance of radiomics models.
Results
Twenty-three studies including 4947 patients were included. The risk of bias was mainly related to analysis domain. The RQS reached an average of (37.7 ± 11.4)% with main methodological insufficiencies of scientific study design, external validation, and open science. The pooled areas under the receiver operating curve (AUC) were 0.85 (95% CI 0.82–0.89), 0.87 (95% CI 0.83–0.92), and 0.74 (95% CI 0.67–0.80), respectively, for CT, MR, and ultrasound radiomics models. The pooled AUC of ultrasound radiomics model was significantly lower than that of CT (p = 0.002) and MR (p < 0.001). Portal venous phase for CT and hepatobiliary phase for MR were superior to other imaging sequences for radiomic MVI prediction. Segmentation of both tumor and peritumor regions showed better performance than tumor region.
Conclusion
Radiomics models show promising prediction performance for predicting MVI in HCC. However, improvements in standardization of methodology are required for feasibility confirmation and clinical translation.
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Abbreviations
- MVI:
-
Microvascular invasion
- HCC:
-
Hepatocellular carcinoma
- PROBAST:
-
Prediction model risk of bias assessment tool
- RQS:
-
Radiomics quality score
- AUC:
-
Area under the receiver operating characteristic
- PRISMA:
-
The preferred reporting items for systematic reviews and meta-analyses
- TP:
-
True positive
- TN:
-
True negative
- FP:
-
False positive
- FN:
-
False negative
- CT:
-
Computed tomography
- MRI:
-
Magnetic resonance imaging
- US:
-
Ultrasound
- PET:
-
Positron emission tomography
- TRIPOD:
-
Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis
- N/l:
-
Number of training patterns of the smallest class/the number of selected features
- AP:
-
Arterial phase
- PVP:
-
Portal venous phase
- HBP:
-
Hepatobiliary phase
- ROI:
-
Region of interest
- ROB:
-
Risk of bias
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Funding
This study has received funding by National Natural Science Foundation of China (No. 81530055 and 81901768).
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Zhong, X., Long, H., Su, L. et al. Radiomics models for preoperative prediction of microvascular invasion in hepatocellular carcinoma: a systematic review and meta-analysis. Abdom Radiol 47, 2071–2088 (2022). https://doi.org/10.1007/s00261-022-03496-3
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DOI: https://doi.org/10.1007/s00261-022-03496-3