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

Journal: Materials today. Bio
Published Date: Mar 22, 2025

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.

Authors

  • Zizhen Yang
    Department of Radiology, Ningbo No.2 Hospital, Ningbo 315012, China.
  • Jianer Ling
    Department of Radiology, Ningbo No.2 Hospital, Ningbo, 315012, China.
  • Wei Sun
    Sutra Medical Inc, Lake Forest, CA.
  • Chunshu Pan
    Department of Radiology, Ningbo No.2 Hospital, Ningbo, 315012, China.
  • Tianxiang Chen
    Ningbo Key Laboratory of Biomedical Imaging Probe Materials and Technology, Zhejiang International Cooperation Base of Biomedical Materials Technology and Application, Chinese Academy of Sciences (CAS) Key Laboratory of Magnetic Materials and Devices, Ningbo Cixi Institute of Biomedical Engineering, Zhejiang Engineering Research Center for Biomedical Materials, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, PR China; Cixi Biomedical Research Institute, Wenzhou Medical University, Zhejiang, PR China; University of Chinese Academy of Sciences, Beijing, 100049, PR China.
  • Chen Dong
    College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou, China.
  • Xiaojun Zhou
    Key Laboratory of Advanced Light Conversion Materials and Biophotonics, School of Chemistry and Life Resources, Renmin University of China, Beijing, 100872, P. R. China.
  • Jingfeng Zhang
    Department of Radiology, Ningbo No. 2 Hospital, Ningbo, 315010, China (J.Z.).
  • Jianjun Zheng
    HwaMei Hospital, University of Chinese Academy of Sciences, 41 Xibei Street, Ningbo, 315010, China. Electronic address: zhengjianjun@ucas.ac.cn.
  • Xuehua Ma
    Laboratory of Advanced Theranostic Materials and Technology, Ningbo Key Laboratory of Biomedical Imaging Probe Materials and Technology, Chinese Academy of Sciences (CAS) Key Laboratory of Magnetic Materials and Devices, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China.

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