A multicenter proof-of-concept study on deep learning-based intraoperative discrimination of primary central nervous system lymphoma.
Nat Commun
; 15(1): 3768, 2024 May 04.
Article
de En
| MEDLINE
| ID: mdl-38704409
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.
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Tumeurs du système nerveux central
/
Apprentissage profond
/
Coupes minces congelées
/
Gliome
/
Lymphomes
Limites:
Aged
/
Female
/
Humans
/
Male
/
Middle aged
Langue:
En
Journal:
Nat Commun
Sujet du journal:
BIOLOGIA
/
CIENCIA
Année:
2024
Type de document:
Article
Pays d'affiliation:
Chine
Pays de publication:
ENGLAND
/
ESCOCIA
/
GB
/
GREAT BRITAIN
/
INGLATERRA
/
REINO UNIDO
/
SCOTLAND
/
UK
/
UNITED KINGDOM