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Prediction of HER2 expression in breast cancer by combining PET/CT radiomic analysis and machine learning
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Prediction of HER2 expression in breast cancer by combining PET/CT radiomic analysis and machine learning
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Journal Article
Prediction of HER2 expression in breast cancer by combining PET/CT radiomic analysis and machine learning
2022
Overview
BackgroundHuman epidermal growth factor receptor 2 (HER2) expression status determination significantly contributes to HER2-targeted therapy in breast cancer (BC). The purpose of this study was to evaluate the role of radiomics and machine learning based on PET/CT images in HER2 status prediction, and to identify the most effective combination of machine learning model and radiomic features.MethodsA total of 217 BC patients who underwent PET/CT examination were involved in the study and randomly divided into a training set (n = 151) and a testing set (n = 66). For all four models, the model parameters were determined using a threefold cross-validation in the training set. Each model’s performance was evaluated on the independent testing set using the receiver operating characteristic (ROC) curve, and AUC was calculated to get a quantified performance measurement of each model.ResultsAmong the four developed machine learning models, the XGBoost model outperformed other machine learning models in HER2 status prediction. Furthermore, compared to the XGBoost model based on PET alone or CT alone radiomic features, the predictive power for HER2 status by using XGBoost model based on PET/CTmean or PET/CTconcat radiomic fusion features was dramatically improved with an AUC of 0.76 (95% confidence interval [CI] 0.69–0.83) and 0.72 (0.65–0.80), respectively.ConclusionsThe established machine learning classifier based on PET/CT radiomic features is potentially predictive of HER2 status in BC.
