Abstract
Purpose
To compare the efficacy of radiomics models via five machine learning algorithms in predicting the histological grade of hepatocellular carcinoma (HCC) before surgery and to develop the most stable model to classify high-risk HCC patients.
Methods
Contrast-enhanced computed tomography (CECT) images of 175 HCC patients before surgery were analysed, and radiomics features were extracted from CECT images (including arterial and portal phases). Five machine learning models, including Bayes, random forest (RF), k-nearest neighbors (KNN), logistic regression (LR), and support vector machine (SVM), were applied to establish the model. The stability of the five models was weighed by the relative standard deviation (RSD), and the lowest RSD value was chosen as the most stable model to predict the histological grade of HCC. The area under the curve (AUC) and Delong tests were devoted to assessing the predictive efficacy of the models.
Results
High-grade HCC accounted for 28.57% (50/175) of the 175 patients. The RSD value of AUC via the RF machine learning model was the lowest (2.3%), followed by Bayes (3.2%), KNN (6.4%), SVM (8.7%) and LR (31.3%). In addition, the RF model (AUC = 0.995) was better than the other four models in the training set (p < 0.05), as well as obtained good predictive performance in the test set (AUC = 0.837).
Conclusion
Among the five machine learning models, the RF-based radiomics model was the most stable and performed excellently in identifying high histological grade of HCC.
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Funding
This research was supported by the Zhejiang Provincial Natural Science Foundation of China (Grant NO. LTGY23H180018), and the Medical Science and Technology Project of Zhejiang Province (Grant NOs. 2020KY019, 2021PY036, 2023KY485).
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CW and XD analyzed data, selected study, wrote the preliminary draft, reviewed of final draft. YZ collected data and conducted radiomics analysis. LZ, YC and YW performed study selection and statistical analysis. YL and JC proposed the concept and design of the study. All authors contributed to the article and approved the final version of the manuscript for submission.
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Wu, C., Du, X., Zhang, Y. et al. Five machine learning-based radiomics models for preoperative prediction of histological grade in hepatocellular carcinoma. J Cancer Res Clin Oncol 149, 15103–15112 (2023). https://doi.org/10.1007/s00432-023-05327-4
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DOI: https://doi.org/10.1007/s00432-023-05327-4