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<p>Dynamic dimension assessments of tumor tissues have broad relevance in clinical diagnosis and the treatment of patients. Current technologies for such a purpose include quasi-static measurements that lack microscale resolution and sensing sites, with limited capabilities for time-dependent, three-dimensional profiling of tumors, particularly at the early growth stage. Here, we report the conformal Hall-sensor-based systems for continuous monitoring of tumor morphological features such as growth rates and volumes. Such platforms incorporate ultrathin crystalline-silicon nanomembranes (200 nm thick) as a basis for displacement sensing via magnetic flux detection, in an array design that yields spatiotemporal information of tumor geometries at high sensitivity. Evaluation involves real-time measurements on a living mouse model with tumor tissues at various pathological conditions, where the integration with deep learning algorithms can further enable the system for large-scale tumor profile reconstruction across tissue surfaces. These microsystems provide the potential for monitoring of tumor progression and treatment guidance in patients.</p>

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Conformal, ultrathin crystalline-silicon-based Hall sensor arrays with deep learning models for early-stage monitoring of three-dimensional tumor tissues

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月27日

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  • Junhan Liu,
  • Zhongyuan Wu,
  • Lianjie Zhou,
  • Yanran Shen,
  • Xiaojun Wu,
  • Junling Liang,
  • Yuting Shao,
  • Pengchuan Liu,
  • Zhongzheng Li,
  • Bofan Hu,
  • Ming Wang,
  • Zengfeng Di,
  • Tianjun Cai,
  • Fan Xu,
  • Su Jiang,
  • Mengdi Han,
  • Ling Tao,
  • Yongfeng Mei,
  • Enming Song

摘要

Dynamic dimension assessments of tumor tissues have broad relevance in clinical diagnosis and the treatment of patients. Current technologies for such a purpose include quasi-static measurements that lack microscale resolution and sensing sites, with limited capabilities for time-dependent, three-dimensional profiling of tumors, particularly at the early growth stage. Here, we report the conformal Hall-sensor-based systems for continuous monitoring of tumor morphological features such as growth rates and volumes. Such platforms incorporate ultrathin crystalline-silicon nanomembranes (200 nm thick) as a basis for displacement sensing via magnetic flux detection, in an array design that yields spatiotemporal information of tumor geometries at high sensitivity. Evaluation involves real-time measurements on a living mouse model with tumor tissues at various pathological conditions, where the integration with deep learning algorithms can further enable the system for large-scale tumor profile reconstruction across tissue surfaces. These microsystems provide the potential for monitoring of tumor progression and treatment guidance in patients.