Internal medicine

Radiology

Mann–Whitney U test

Breast MRI

Nuclear medicine

Mathematics

Intraclass correlation

Cancer

Statistics

World Wide Web

Magnetic resonance imaging

Logistic regression

Wilcoxon signed-rank test

Radiomics

Lasso (programming language)

Computer science

Mammography

Breast cancer

Receiver operating characteristic

Effective diffusion coefficient

Confidence interval

Medicine

Reproducibility

Journal of Magnetic Resonance Imaging

Radiomics has been applied to breast magnetic resonance imaging (MRI) for gene status prediction. However, the features of peritumoral regions were not thoroughly investigated.To evaluate the use of intratumoral and peritumoral regions from functional parametric maps based on breast dynamic contrast-enhanced MRI (DCE-MRI) for prediction of HER-2 and Ki-67 status.Retrospective.A total of 351 female patients (average age, 51 years) with pathologically confirmed breast cancer were assigned to the training (n = 243) and validation (n = 108) cohorts.3.0T, T1 gradient echo.Radiomic features were extracted from intratumoral and peritumoral regions on six functional parametric maps calculated using time-intensity curves of DCE-MRI. The intraclass correlation coefficients (ICCs) were used to determine the reproducibility of feature extraction. Based on the intratumoral, peritumoral, and combined intra- and peritumoral regions, three radiomics signatures (RSs) were built using the least absolute shrinkage and selection operator (LASSO) logistic regression model, respectively.Wilcoxon rank-sum test, minimum redundancy maximum relevance, LASSO, receiver operating characteristic curve (ROC) analysis, and DeLong test.The intratumoral and peritumoral RSs for prediction of HER-2 and Ki-67 status achieved areas under the ROC (AUCs) of 0.683 (95% confidence interval [CI], 0.574-0.793) and 0.690 (95% CI, 0.577-0.804), and 0.714 (95% CI, 0.616-0.812) and 0.692 (95% CI, 0.590-0.794) in the validation cohort, respectively. The combined RSs yielded AUCs of 0.713 (95% CI, 0.604-0.823) and 0.749 (95% CI, 0.656-0.841), respectively. There were no significant differences in prediction performance among intratumoral, peritumoral, and combined RSs. Most (69.7%) of the features had good agreement (ICCs >0.8).Radiomic features of intratumoral and peritumoral regions on functional parametric maps based on breast DCE-MRI had the potential to identify HER-2 and Ki-67 status.3 Technical Efficacy Stage: 2.

Intratumoral and Peritumoral Radiomics Based on Functional Parametric Maps from Breast <scp>DCE‐MRI</scp> for Prediction of <scp>HER</scp>‐2 and Ki‐67 Status

Intratumoral and Peritumoral Radiomics Based on Functional Parametric Maps from Breast DCE‐MRI for Prediction of HER‐2 and Ki‐67 Status