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• Clinical Article •
Radiomics based on combined machine learning models for prediction of the response to neoadjuvant chemotherapy in mass enhancement breast cancer using magnetic resonance imaging
YUE Wenyi  ZHANG Hongtao  GAO Shen  ZHOU Juan  CAI Jianming  TIAN Ning  DONG Jinghui  LIU Yuan  BAI Xu  SHENG Fugeng 

Cite this article as: YUE W Y, ZHANG H T, GAO S, et al. Radiomics based on combined machine learning models for prediction of the response to neoadjuvant chemotherapy in mass enhancement breast cancer using magnetic resonance imaging[J]. Chin J Magn Reson Imaging, 2024, 15(3): 93-99, 106. DOI:10.12015/issn.1674-8034.2024.03.016.


[Abstract] Objective To investigate the value of radiomics based on combined machine learning models in predicting the response to neoadjuvant chemotherapy (NAC) in mass enhancement breast cancer.Materials and Methods The clinical and imaging data of ninety-seven patients with mass enhancement breast cancer confirmed by histopathology and underwent NAC from January 2018 to October 2021 in the Fifth Medical Center of Chinese PLA General Hospital were retrospectively analyzed in our study. Based on the results of Response Evaluation Criteria in Solid Tumors (RECIST), the patients were classified into effective group and ineffective group. Based on the radiomics features extracted on the first dynamic contrast-enhanced MRI (DCE-MRI) subtraction images before treatment, a high-pass or low-pass wavelet filter and a Laplace-Gaussian filter with different parameters were also introduced to preprocess the original MR images. For feature screening, feature selection methods based on univariate analysis and multivariate analysis were used. The univariate analysis included F-test, chi-square test and mutual information. The multivariate analysis used the least absolute shrinkage and selection operator (LASSO). Support vector machine (SVM), random forest (RM), and logistic regression (LR) were used for modeling, and finally a total of twelve combinations of feature filters and classifiers were combined by crossover. Ten repetitions of five-fold cross-validation were used for training. Finally, area under the curve (AUC), sensitivity, specificity, accuracy, positive prediction value and negative prediction value were used to evaluate the prediction performance.Results Among all cross-combined schemes, the feature screening method that achieved the best classification performance was the F-test method in univariate analysis, and the best classifier was the SVM. The combination screened a total of 191 imaging features with an overall mean AUC of 0.83 [95% confidence interval (CI): 0.80-0.86] in predicting NAC response, and the accuracy of the model was 77% (95% CI: 74%-80%). Specificity was 81% (95% CI: 78%-84%), sensitivity was 71% (95% CI: 65%-77%), positive predictive value was 67% (95% CI: 62%-72%), and negative predictive value was 85% (95% CI: 83%-87%).Conclusions A combined machine learning model based on F-test and SVM validated good performance of radiomics in predicting the response to NAC for mass enhancement breast cancer patients.
[Keywords] breast cancer；neoadjuvant chemotherapy；radiomics；machine learning；magnetic resonance imaging

YUE Wenyi1, 2   ZHANG Hongtao1   GAO Shen1   ZHOU Juan1   CAI Jianming1   TIAN Ning1   DONG Jinghui1   LIU Yuan1   BAI Xu1   SHENG Fugeng1*  

1 Department of Radiology, the Fifth Medical Center of Chinese PLA General Hospital, Beijing 100071, China

2 Graduate School, Chinese PLA General Medical School, Beijing 100853, China

Corresponding author: SHENG F G, E-mail: fugeng_sheng@163.com

Conflicts of interest   None.

Received  2023-06-13
Accepted  2024-02-26
DOI: 10.12015/issn.1674-8034.2024.03.016
Cite this article as: YUE W Y, ZHANG H T, GAO S, et al. Radiomics based on combined machine learning models for prediction of the response to neoadjuvant chemotherapy in mass enhancement breast cancer using magnetic resonance imaging[J]. Chin J Magn Reson Imaging, 2024, 15(3): 93-99, 106. DOI:10.12015/issn.1674-8034.2024.03.016.

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