Rate My Professor Lydia Min-Ying Su

Su has published extensively in leading journals, contributing to advancements in non-invasive imaging diagnostics. Key publications include "Nomogram for reducing unnecessary biopsies of breast lesions based on MRI and clinical features: a multi-center retrospective cohort study" (Cancer Imaging, 2025), "MRI-Based Radiomics Model for Classifying Axillary Lymph Node Burden and Disease-Free Survival in Patients With Early-Stage Breast Cancer" (Journal of Magnetic Resonance Imaging, 2026), "MRI-based Habitat Analysis for the Prediction of Progression-Free Survival in Primary Spinal Tumors" (Radiology, 2025), and "Deep-learning convolutional neural networks accurately classify genetic mutations in gliomas" (American Journal of Neuroradiology, 2018). Highly cited earlier works feature "Quantitative analysis of lesion morphology and texture features for diagnostic prediction in breast MRI" (Academic Radiology, 2008) and "MRI evaluation of pathologically complete response and residual tumors in breast cancer after neoadjuvant chemotherapy" (Cancer, 2008). She provided editorial contributions for "Multisequence MRI Enables High-Fidelity FDG-PET Synthesis for Epilepsy Using GANs" (Journal of Magnetic Resonance Imaging, 2026). Su is a member of the International Society of Magnetic Resonance in Medicine and the American Association of Physicists in Medicine. In 2025, she chaired a grant review panel for the Department of Defense Breast Cancer Research Program, and in 2026 for the Melanoma Research Program. She hosted scholars from Korea in 2024.