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Abstract #1987

Cluster analysis of quantitative ultrafast DCE-MRI for prediction of breast cancer response to neoadjuvant chemotherapy

Zhen Ren1, Xiaobing Fan1, Frederick M Howard2, Rita Nanda2, Hiroyuki Abe1, Kirti Kulkarni1, Nan Chen2, Anna Biernacka3, and Gregory S Karczmar1
1Department of Radiology, University of Chicago, Chicago, IL, United States, 2Section of Hematology and Oncology, Department of Medicine, University of Chicago, Chicago, IL, United States, 3Department of Pathology, University of Chicago, Chicago, IL, United States

Synopsis

Keywords: Breast, Breast, K-means clustering, ultrafast DCE-MRI, breast cancer, response to neoadjuvant chemotherapy, BPE, bilateral asymmetry

Motivation: Early prediction of pathologic complete response (pCR) enhances personalized breast cancer treatment.

Goal(s): This study aims to measure kinetic parameters from pre-neoadjuvant chemotherapy (NAC) ultrafast dynamic contrast-enhanced (DCE)-MRI using k-means clustering (KMC) to predict pCR.

Approach: Fifty-six patients undergoing NAC were enrolled. Tumor and normal parenchymal voxels were divided into five clusters based on maximum enhancement rate. Ipsilateral/contralateral (I/C) background parenchymal enhancement kinetics (kBPE) parameters were compared between pCR and non-pCR patients. A logistic regression model incorporating clinical features.

Results: MRI tumor kinetics, and kBPE I/Cs achieved an AUC of 0.94, with 0.91 sensitivity and specificity, for predicting breast cancer response to NAC.

Impact: K-means clustering analysis of ultrafast DCE-MRI is a stable technique to effectively predict treatment response in breast cancer patients prior to neoadjuvant chemotherapy, which facilitates personalized therapy adjustments and can improve clinical outcomes through individualized treatments.

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Keywords