AI‐Assisted Tumor Boundary Delineation via Targeted Ultrasmall Iron Oxide Nanoprobe for High‐Contrast HER2‐Positive Tumor Imaging
TL;DR
A strategic platform that combines a novel ultrasensitive magnetic resonance (MR) contrast agent with deep learning to significantly enhance the tumor‐to‐normal ratio (TNR) and enabled the reconstruction of three‐dimensional tumor models, offering clinicians an intuitive visualization of tumor structure for precise diagnosis and surgical guidance.
Abstract
ABSTRACT Breast cancer continues to be a leading cause of cancer‐related mortality in women globally, where precise diagnosis and clear tumor demarcation are critical for effective treatment. Herein, we developed a strategic platform that combines a novel ultrasensitive magnetic resonance (MR) contrast agent with deep learning to significantly enhance the tumor‐to‐normal ratio (TNR). We designed ultrasmall iron oxide nanoparticles (USIO NPs) conjugated with trastuzumab (Tmab) for targeted MR imaging of HER2‐positive breast cancer. The USIO@Tmab nanoprobe demonstrated excellent HER2 specificity and pH‐responsive activation. The relaxivity of the nanoprobe shifted from a low T1‐weighted intensity (r1 = 1.43 mM− 1s− 1) under physiological conditions to an enhanced value (r1 = 4.07 mM− 1s− 1) in the acidic tumor microenvironment due to the detachment of Tmab protein. Additionally, we employed the 3D nnU‐Net deep learning framework as a post‐processing visualization aid to enhance tumor boundary detection via image fusion, rather than to amplify the underlying MRI signal. This approach yielded high segmentation accuracy, with an Intersection‐over‐Union (IoU) of 0.88 and a Dice coefficient of 0.93. This strategy provided an additional 2.59‐fold increase in TNR and enabled the reconstruction of three‐dimensional (3D) tumor models, offering clinicians an intuitive visualization of tumor structure for precise diagnosis and surgical guidance.
