胶质瘤
医学
边距(机器学习)
癌症手术
外科切除术
癌症
代谢性酸中毒
酸中毒
癌症研究
生物医学工程
外科
内科学
计算机科学
机器学习
作者
Ziyi Jin,Yuanlin Qi,Wenjia Duan,An Sui,Bingjie Zhao,Yinhui Deng,Yuting Zhai,Yuwen Zhang,Tao Sun,Guang‐Ping Zhang,Limei Han,Ying Mao,Jinhua Yu,Xiao‐Yong Zhang,Cong Li
标识
DOI:10.1002/advs.202104935
摘要
Surgeons face challenges in intraoperatively defining margin of brain tumors due to its infiltrative nature. Extracellular acidosis caused by metabolic reprogramming of cancer cells is a reliable marker for tumor infiltrative regions. Although the acidic margin-guided surgery shows promise in improving surgical prognosis, its clinical transition is delayed by having the exogenous probes approved by the drug supervision authority. Here, an intelligent surface-enhanced Raman scattering (SERS) navigation system delineating glioma acidic margins without administration of exogenous probes is reported. With assistance of this system, the metabolites at the tumor cutting edges can be nondestructively transferred within a water droplet to a SERS chip with pH sensitivity. Homemade deep learning model automatically processes the Raman spectra collected from the SERS chip and delineates the pH map of tumor resection bed with increased speed. Acidity correlated cancer cell density and proliferation level are demonstrated in tumor cutting edges of animal models and excised tissues from glioma patients. The overall survival of animal models post the SERS system guided surgery is significantly increased in comparison to the conventional strategy used in clinical practice. This SERS system holds the promise in accelerating clinical transition of acidic margin-guided surgery for solid tumors with infiltrative nature.
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