Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI

Breast Cancer Research : BCR
Nathaniel M BramanAnant Madabhushi

Abstract

In this study, we evaluated the ability of radiomic textural analysis of intratumoral and peritumoral regions on pretreatment breast cancer dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict pathological complete response (pCR) to neoadjuvant chemotherapy (NAC). A total of 117 patients who had received NAC were retrospectively analyzed. Within the intratumoral and peritumoral regions of T1-weighted contrast-enhanced MRI scans, a total of 99 radiomic textural features were computed at multiple phases. Feature selection was used to identify a set of top pCR-associated features from within a training set (n = 78), which were then used to train multiple machine learning classifiers to predict the likelihood of pCR for a given patient. Classifiers were then independently tested on 39 patients. Experiments were repeated separately among hormone receptor-positive and human epidermal growth factor receptor 2-negative (HR+, HER2-) and triple-negative or HER2+(TN/HER2+) tumors via threefold cross-validation to determine whether receptor status-specific analysis could improve classification performance. Among all patients, a combined intratumoral and peritumoral radiomic feature set yielded a maximum AUC of 0.78 ± 0...Continue Reading

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Methods Mentioned

BETA
feature extraction
Feature Selection
biopsy
biopsies

Software Mentioned

CoLlAGe
Feature Selection Toolbox for C ( FEAST
MATLAB
ConsensusClusterPlus
R
MAGNETOM

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