Dynamic dimension assessments of tumor tissues have broad relevance in clinical diagnosis and treatments of patients.Current technologies for such purpose include quasi-static measurements that lack microscale resolut...Dynamic dimension assessments of tumor tissues have broad relevance in clinical diagnosis and treatments of patients.Current technologies for such 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 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 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.展开更多
文摘Dynamic dimension assessments of tumor tissues have broad relevance in clinical diagnosis and treatments of patients.Current technologies for such 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 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 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.
