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作者

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

作者单位

⁷Shenzhen Institute of Advanced Technology, Chinese Academy Sciences, Shenzhen, China ¹¹Department of Neurosurgery, The Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China ⁵Department of Research and Development, United Imaging Intelligence Co Ltd, Shanghai, China ¹Department of Neurosurgery, Huashan Hospital Affiliated to Fudan University, Shanghai, China ⁹Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China ¹⁰Wuhan Zhongji Biotechnology Co Ltd, Wuhan, China ²National Center for Neurological Disorders, Shanghai, China ⁶Department of Pathology, Huashan Hospital Affiliated to Fudan University, Shanghai, China ³Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai, China ⁸Department of Laws and Regulations, The Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China ⁴Neurosurgical Institute of Fudan University, Shanghai, China

刊名

Modern Pathology

年份

2025

卷号

Vol.38 No.7

页码

100749

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

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