A multicenter proof-of-concept study on deep learning-based intraoperative discrimination of primary central nervous system lymphoma

• Published in: Nature communications Vol. 15; no. 1; pp. 3768 - 15
• Main Authors: Zhang, Xinke, Zhao, Zihan, Wang, Ruixuan, Chen, Haohua, Zheng, Xueyi, Liu, Lili, Lan, Lilong, Li, Peng, Wu, Shuyang, Cao, Qinghua, Luo, Rongzhen, Hu, Wanming, lyu, Shanshan, Zhang, Zhengyu, Xie, Dan, Ye, Yaping, Wang, Yu, Cai, Muyan
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 04.05.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/67/1922; 692/4028/67/2321; Aged; Central nervous system; Central Nervous System Neoplasms - diagnosis; Central Nervous System Neoplasms - pathology; Central Nervous System Neoplasms - surgery; Concept learning; Deep Learning; Diagnosis; Diagnosis, Differential; Female; Frozen Sections; Glioma; Glioma - pathology; Glioma - surgery; Humanities and Social Sciences; Humans; Intraoperative Period; Lesions; Lymphoma; Lymphoma - diagnosis; Lymphoma - pathology; Lymphoma - surgery; Male; Medical imaging; Middle Aged; multidisciplinary; Nervous system; Neurosurgery; Pathology; Proof of Concept Study; Science; Science (multidisciplinary)
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-024-48171-x

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. Correct diagnosis of primary central nervous system lymphoma is key in determining treatment, however, this depends on pathology analysis. Here, the authors develop a deep learning method to diagnose primary nervous system lymphoma from stained whole-slide images.