Abstract #0995

Prediction of Early Neoadjuvant Chemotherapy Response of Breast Cancer through Deep Learning-Based Pharmacokinetic Quantification of DCE-MRI

Chaowei Wu^1,2, Lixia Wang¹, Nan Wang^1,3, Stephen Shiao⁴, Tai Dou⁴, Yin-Chen Hsu¹, Anthony G. Christodoulou^1,2,5, Yibin Xie¹, and Debiao Li^1,2

¹Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States, ²Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States, ³Radiology Department, Stanford University, Stanford, CA, United States, ⁴Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA, United States, ⁵Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States

Synopsis

Keywords: Diagnosis/Prediction, Quantitative Imaging, DCE

Motivation: Accurate prediction of pCR to neoadjuvant chemotherapy in breast cancer is crucial for personalized treatment. However, the generalizability of prediction algorithms from DCE-MRI is often hindered by imaging protocol discrepancy.

Goal(s): This study aims to enhance the generalizability of pCR prediction through deep learning-based pharmacokinetic mapping.

Approach: Using a previously developed DL model, pharmacokinetic parameters were retrospectively estimated from clinical multi-phasic DCE-MRI data across four public datasets, which were subsequently applied in radiomic analysis to predict pCR.

Results: Our model demonstrated superior and consistent pCR prediction performance across datasets compared to conventional functional tumor volume (FTV) enhancement maps.

Impact: This study introduces a quantitative, generalizable approach to early prediction of neoadjuvant chemotherapy (NAC) response in breast cancer, unlocking noninvasive imaging biomarkers with enhanced predictive accuracy and generalizability, thereby facilitating personalized treatment decisions without modifying clinical imaging protocols.

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