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Radioproteomics in Breast Cancer: Prediction of Ki-67 Expression With MRI-based Radiomic Models

Published:March 17, 2021DOI:https://doi.org/10.1016/j.acra.2021.02.001

      Rationale and Objectives

      We aimed to investigate the value of magnetic resonance image (MRI)-based radiomics in predicting Ki-67 expression of breast cancer.

      Methods

      In this retrospective study, 159 lesions from 154 patients were included. Radiomic features were extracted from contrast-enhanced T1-weighted MRI (C+MRI) and apparent diffusion coefficient (ADC) maps, with open-source software. Dimension reduction was done with reliability analysis, collinearity analysis, and feature selection. Two different Ki-67 expression cut-off values (14% vs 20%) were studied as reference standard for the classifications. Input for the models were radiomic features from individual MRI sequences or their combination. Classifications were performed using a generalized linear model.

      Results

      Considering Ki-67 cut-off value of 14%, training and testing AUC values were 0.785 (standard deviation [SD], 0.193) and 0.849 for ADC; 0.696 (SD, 0.150) and 0.695 for C+MRI; 0.755 (SD, 0.171) and 0.635 for the combination of both sequences, respectively. Regarding Ki-67 cut-off value of 20%, training and testing AUC values were 0.744 (SD, 0.197) and 0.617 for ADC; 0.629 (SD, 0.251) and 0.741 for C+MRI; 0.761 (SD, 0.207) and 0.618 for the combination of both sequences, respectively.

      Conclusion

      ADC map-based selected radiomic features coupled with generalized linear modeling might be a promising non-invasive method to determine the Ki-67 expression level of breast cancer.

      KEY WORDS

      Abbreviations:

      ADC (Apparent diffusion coefficient), AUC (Area under the receiver operating characteristic curve), BC (Breast cancer), C+MRI (Contrast-enhanced T1-weighted MRI), D (Dimensional), DCE-MRI (Dynamic contrast-enhanced magnetic resonance imaging), DWI (Diffusion-weighted echo-planar images), FLASH (Fast low angle shot), GLM (Generalized linear model), HER-2 (Human epithelial receptor-2), ICC (Intra-class correlation coefficient), LASSO (The least absolute shrinkage and selection operator), LoG (Laplacian of Gaussian), MCC (Matthews correlation coefficient), mi RNA (micro RNA), MRI (Magnetic resonance imaging), NAC (Neoadjuvant chemotherapy), TNBC (Triple negative breast cancer), PACS (Picture Archiving and Communication System), RNA (Ribonucleic acid), ROI (Region of interest), SD (Standard deviation), SUBT (Subtraction)
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