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

Author

Listed:
  • Xinke Zhang

    (Sun Yat-sen University Cancer Center)

  • Zihan Zhao

    (Sun Yat-sen University Cancer Center)

  • Ruixuan Wang

    (Sun Yat-sen University)

  • Haohua Chen

    (Sun Yat-sen University Cancer Center)

  • Xueyi Zheng

    (Sun Yat-sen University Cancer Center)

  • Lili Liu

    (Sun Yat-sen University Cancer Center)

  • Lilong Lan

    (Sun Yat-sen University Cancer Center)

  • Peng Li

    (Sun Yat-sen University Cancer Center)

  • Shuyang Wu

    (Sun Yat-sen University Cancer Center)

  • Qinghua Cao

    (Sun Yat-sen University)

  • Rongzhen Luo

    (Sun Yat-sen University Cancer Center)

  • Wanming Hu

    (Sun Yat-sen University Cancer Center)

  • Shanshan lyu

    (Guangdong Provincial People’s Hospital)

  • Zhengyu Zhang

    (Soutern Medical University)

  • Dan Xie

    (Sun Yat-sen University Cancer Center)

  • Yaping Ye

    (Soutern Medical University)

  • Yu Wang

    (Soutern Medical University)

  • Muyan Cai

    (Sun Yat-sen University Cancer Center)

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.

Suggested Citation

  • Xinke Zhang & Zihan Zhao & Ruixuan Wang & Haohua Chen & Xueyi Zheng & Lili Liu & Lilong Lan & Peng Li & Shuyang Wu & Qinghua Cao & Rongzhen Luo & Wanming Hu & Shanshan lyu & Zhengyu Zhang & Dan Xie & , 2024. "A multicenter proof-of-concept study on deep learning-based intraoperative discrimination of primary central nervous system lymphoma," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-48171-x
    DOI: 10.1038/s41467-024-48171-x
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    References listed on IDEAS

    as
    1. Chi-Long Chen & Chi-Chung Chen & Wei-Hsiang Yu & Szu-Hua Chen & Yu-Chan Chang & Tai-I Hsu & Michael Hsiao & Chao-Yuan Yeh & Cheng-Yu Chen, 2021. "An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    2. Xueyi Zheng & Ruixuan Wang & Xinke Zhang & Yan Sun & Haohuan Zhang & Zihan Zhao & Yuanhang Zheng & Jing Luo & Jiangyu Zhang & Hongmei Wu & Dan Huang & Wenbiao Zhu & Jianning Chen & Qinghua Cao & Hong , 2022. "A deep learning model and human-machine fusion for prediction of EBV-associated gastric cancer from histopathology," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
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