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. 2020 Mar/Apr;44(2):275-283.
doi: 10.1097/RCT.0000000000000978.

Combining Dynamic Contrast-Enhanced Magnetic Resonance Imaging and Apparent Diffusion Coefficient Maps for a Radiomics Nomogram to Predict Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Patients

Xiangguang Chen  1 , Xiaofeng Chen, Jiada Yang, Yulin Li, Weixiong Fan, Zhiqi Yang
Affiliations

Combining Dynamic Contrast-Enhanced Magnetic Resonance Imaging and Apparent Diffusion Coefficient Maps for a Radiomics Nomogram to Predict Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Patients

Xiangguang Chen et al. J Comput Assist Tomogr. 2020 Mar/Apr.

Abstract

Objective: The objective of this study was to develop a nomogrom for prediction of pathological complete response (PCR) to neoadjuvant chemotherapy in breast cancer patients.

Methods: Ninety-one patients were analyzed. A total of 396 radiomics features were extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and apparent diffusion coefficient (ADC) maps. The least absolute shrinkage and selection operator was selected for data dimension reduction to build a radiomics signature. Finally, the nomogram was built to predict PCR.

Results: The radiomics signature of the model that combined DCE-MRI and ADC maps showed a higher performance (area under the receiver operating characteristic curve [AUC], 0.848) than the models with DCE-MRI (AUC, 0.750) or ADC maps (AUC, 0.785) alone in the training set. The proposed model, which included combined radiomics signature, estrogen receptor, and progesterone receptor, yielded a maximum AUC of 0.837 in the testing set.

Conclusions: The combined radiomics features from DCE-MRI and ADC data may serve as potential predictor markers for predicting PCR. The nomogram could be used as a quantitative tool to predict PCR.

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