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作者

Liu, Xi Sun, Tianyang Chen, Hong Wu, Shuai Cheng, Haixia Liu, Xiaojia Lai, Qi Wang, Kun Chen, Lin Lu, Junfeng Zhang, Jun Zou, Yaping Chen, Yi Liu, Yingchao Shi, Feng Jin, Lei Shen, Dinggang Wu, Jinsong

作者单位

^cShanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai, China ^eDepartment of Research and Development, United Imaging Intelligence Co Ltd, Shanghai, China ^jWuhan Zhongji Biotechnology Co Ltd, Wuhan, China ^kDepartment of Neurosurgery, The Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China ^hDepartment of Laws and Regulations, The Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China ^aDepartment of Neurosurgery, Huashan Hospital Affiliated to Fudan University, Shanghai, China ^dNeurosurgical Institute of Fudan University, Shanghai, China ^fDepartment of Pathology, Huashan Hospital Affiliated to Fudan University, Shanghai, China ⁱDepartment of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China ^gShenzhen Institute of Advanced Technology, Chinese Academy Sciences, Shenzhen, China ^bNational Center for Neurological Disorders, Shanghai, China

刊名

Modern Pathology

年份

2025

卷号

Vol.38 No.7

ISSN

0893-3952

关键词

cryosectioned image deep learning glioma multicenter neuropathology

摘要

Intraoperative glioma grading remains a significant challenge primarily due to the diminished diagnostic attributable to the suboptimal quality of cryosectioned slides. Precise intraoperative diagnosis is instrumental in guiding the surgical strategy to balance resection extent and neurologic function preservation, thereby optimizing patient prognoses. This study developed a model for intraoperative glioma grading via deep learning on cryosectioned images, termed intraoperative glioma grading on...更多

Intraoperative glioma grading remains a significant challenge primarily due to the diminished diagnostic attributable to the suboptimal quality of cryosectioned slides. Precise intraoperative diagnosis is instrumental in guiding the surgical strategy to balance resection extent and neurologic function preservation, thereby optimizing patient prognoses. This study developed a model for intraoperative glioma grading via deep learning on cryosectioned images, termed intraoperative glioma grading on cryosection . The model was trained and validated on The Cancer Genome Atlas data sets and 1 cohort , and tested on 5 cohorts . The IGGC model achieved an area under the receiver operating characteristic curve value of 0.99 in differentiating between high-grade glioma and low-grade glioma, and an area under the receiver operating characteristic curve value of 0.96 in identifying grade 4 glioma. Integrated into the clinical workflow, the IGGC model-assisted pathologists of varying experience levels in reducing interobserver variability and enhancing diagnostic consistency. This integrated diagnostic model possesses the potential for clinical implementation, offering a time-efficient and highly accurate method for the 3-grade classification of adult-type diffuse gliomas based on intraoperative cryosectioned slides.收起

文献类型

期刊