原发性中枢神经系统淋巴瘤
医学
冰冻切片程序
胶质瘤
淋巴瘤
H&E染色
病理
放射科
免疫组织化学
癌症研究
作者
Xinke Zhang,Zihan Zhao,Ruixuan Wang,Zilin Li,Xueyi Zheng,Li‐Li Liu,Lilong Lan,Peng Li,Shuyang Wu,Qinghua Cao,Rongzhen Luo,Wanming Hu,Shangru Lyu,Zhengyu Zhang,Dan Xie,Dan Zhang,Yu Wang,Mengli Cai
标识
DOI:10.1038/s41467-024-48171-x
摘要
Abstract Accurate intraoperative differentiation of primary central nervous system lymphoma (PCNSL) remains pivotal in guiding neurosurgical decisions. However, distinguishing PCNSL from other lesions, notably glioma, through frozen sections challenges pathologists. Here we sought to develop and validate a deep learning model capable of precisely distinguishing PCNSL from non-PCNSL lesions, especially glioma, using hematoxylin and eosin (H&E)-stained frozen whole-slide images. Also, we compared its performance against pathologists of varying expertise. Additionally, a human-machine fusion approach integrated both model and pathologic diagnostics. In external cohorts, LGNet achieved AUROCs of 0.965 and 0.972 in distinguishing PCNSL from glioma and AUROCs of 0.981 and 0.993 in differentiating PCNSL from non-PCNSL lesions. Outperforming several pathologists, LGNet significantly improved diagnostic performance, further augmented to some extent by fusion approach. LGNet’s proficiency in frozen section analysis and its synergy with pathologists indicate its valuable role in intraoperative diagnosis, particularly in discriminating PCNSL from glioma, alongside other lesions.
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