Artificial intelligence-assisted magnetic resonance lymphography for evaluation of micro- and macro-sentinel lymph node metastasis in breast cancer

人工智能辅助磁共振淋巴造影术用于评估乳腺癌微前哨淋巴结和宏前哨淋巴结转移

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Abstract

Contrast-enhanced magnetic resonance lymphography (CE-MRL) plays a crucial role in preoperative diagnostic for evaluating tumor metastatic sentinel lymph node (T-SLN), by integrating detailed lymphatic information about lymphatic anatomy and drainage function from MR images. However, the clinical gadolinium-based contrast agents for identifying T-SLN is seriously limited, owing to their small molecular structure and rapid diffusion into the bloodstream. Herein, we propose a novel albumin-modified manganese-based nanoprobes enhanced MRL method for accurately assessing micro- and macro-T-SLN. Specifically, the inherent concentration gradient of albumin between blood and interstitial fluid aids in the movement of nanoprobes into the lymphatic system. The micro-T-SLN exhibits a notably higher MR signal due to the formation of new lymphatic vessels and increased lymphatic flow, allowing for a greater influx of nanoprobes. In contrast, the macro-T-SLN shows a lower MR signal as a result of tumor cell proliferation and damage to the lymphatic vessels. Additionally, a highly accurate and sensitive machine learning model has been developed to guide the identification of micro- and macro-T-SLN by analyzing manganese-enhanced MR images. In conclusion, our research presents a novel comprehensive assessment framework utilizing albumin-modified manganese-based nanoprobes for a highly sensitive evaluation of micro- and macro-T-SLN in breast cancer.

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