Dynamic Contrast-enhanced MRI for Evaluating Breast Cancer Chemotherapy Response Using Conditional Generative Adversarial Networks

Purpose To develop and evaluate an image-to-image conditional generative adversarial network (cGAN) for translating dynamic contrast-enhanced (DCE) MRI data to vascular pharmacokinetic permeability maps. Materials and Methods Retrospective breast cancer DCE MR images from The Cancer Imaging Archive acquired between April 1996 and January 1998 were used to assess the developed cGAN. The extended Tofts model (ETM) was applied to establish reference standard volume transfer constant (<i>K</i>^trans) maps. The cGAN was trained to learn relationships between DCE MR data and ETM <i>K</i>^trans maps. Linear regression was applied to determine agreement between the ETM and cGAN. Logistic regression and paired <i>t</i> tests were used to assess predictive capabilities of pathologic response. Results Twenty DCE MRI scans (<i>n</i> = 2400 sections) from 10 female patients (mean age, 45 years ± 12 [SD]) were analyzed. Computation time was reduced over 1000-fold using the cGAN compared with the ETM. The cGAN <i>K</i>^trans maps exhibited excellent spatial agreement and high structural similarity to the ETM, with low errors (normalized root mean squared error ≤0.32; normalized mean absolute error ≤0.16) and a strong correlation (<i>R</i>² ≥ 0.98). Patients with pathologic complete response demonstrated a 60% reduction in cGAN <i>K</i>^trans (<i>P</i> = .01) after the first cycle of neoadjuvant chemotherapy, closely matching ETM <i>K</i>^trans (59%, <i>P</i> = .02). In contrast, patients without pathologic complete response showed a modest reduction in cGAN <i>K</i>^trans (17%, <i>P</i> = .13), still in good agreement with the ETM (15%, <i>P</i> = .19). Percentage of <i>K</i>^trans change effectively distinguished patients with or without pathologic complete response (C statistic = 1.0) for both models. Conclusion The DCE to pharmacokinetic cGAN offers promise for standardizing pharmacokinetic analysis and reducing computational complexity at DCE MRI. Moreover, this approach demonstrated potential for early prediction of breast cancer responses to neoadjuvant chemotherapy. <b>Keywords:</b> Dynamic Contrast-enhanced MRI, Vascular Permeability, Image-to-Image Conditional Generative Adversarial Network, Breast Cancer, Neoadjuvant Chemotherapy © RSNA, 2025.