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Teacher-student collaborated multiple instance learning for pan-cancer PDL1 expression prediction from histopathology slides

Author

Listed:
  • Darui Jin

    (Beihang University
    German Cancer Research Center (DKFZ)
    Beihang University)

  • Shangying Liang

    (Beihang University)

  • Artem Shmatko

    (German Cancer Research Center (DKFZ))

  • Alexander Arnold

    (Institute of Pathology)

  • David Horst

    (Institute of Pathology
    a partnership between DKFZ and Charité-Universitätsmedizin Berlin)

  • Thomas G. P. Grünewald

    (Heidelberg University Hospital
    German Cancer Consortium (DKTK)
    Hopp Children’s Cancer Center (KiTZ) Heidelberg
    NCT Heidelberg, a partnership between DKFZ and Heidelberg University Hospital)

  • Moritz Gerstung

    (German Cancer Research Center (DKFZ))

  • Xiangzhi Bai

    (Beihang University
    Beihang University
    Beihang University)

Abstract

Programmed cell death ligand 1 (PDL1), as an important biomarker, is quantified by immunohistochemistry (IHC) with few established histopathological patterns. Deep learning aids in histopathological assessment, yet heterogeneity and lacking spatially resolved annotations challenge precise analysis. Here, we present a weakly supervised learning approach using bulk RNA sequencing for PDL1 expression prediction from hematoxylin and eosin (H&E) slides. Our method extends the multiple instance learning paradigm with the teacher-student framework, which assigns dynamic pseudo-labels for intra-slide heterogeneity and retrieves unlabeled instances using temporal ensemble model distillation. The approach, evaluated on 12,299 slides across 20 solid tumor types, achieves a weighted average area under the curve of 0.83 on fresh-frozen and 0.74 on formalin-fixed specimens for 9 tumors with PDL1 as an established biomarker. Our method predicts PDL1 expression patterns, validated by IHC on 20 slides, offering insights into histologies relevant to PDL1. This demonstrates the potential of deep learning in identifying diverse histological patterns for molecular changes from H&E images.

Suggested Citation

  • Darui Jin & Shangying Liang & Artem Shmatko & Alexander Arnold & David Horst & Thomas G. P. Grünewald & Moritz Gerstung & Xiangzhi Bai, 2024. "Teacher-student collaborated multiple instance learning for pan-cancer PDL1 expression prediction from histopathology slides," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-46764-0
    DOI: 10.1038/s41467-024-46764-0
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    References listed on IDEAS

    as
    1. James A. Diao & Jason K. Wang & Wan Fung Chui & Victoria Mountain & Sai Chowdary Gullapally & Ramprakash Srinivasan & Richard N. Mitchell & Benjamin Glass & Sara Hoffman & Sudha K. Rao & Chirag Mahesh, 2021. "Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    2. Gang Yu & Kai Sun & Chao Xu & Xing-Hua Shi & Chong Wu & Ting Xie & Run-Qi Meng & Xiang-He Meng & Kuan-Song Wang & Hong-Mei Xiao & Hong-Wen Deng, 2021. "Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    3. Gil Shamai & Amir Livne & António Polónia & Edmond Sabo & Alexandra Cretu & Gil Bar-Sela & Ron Kimmel, 2022. "Deep learning-based image analysis predicts PD-L1 status from H&E-stained histopathology images in breast cancer," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    4. 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.
    5. Nikhil Naik & Ali Madani & Andre Esteva & Nitish Shirish Keskar & Michael F. Press & Daniel Ruderman & David B. Agus & Richard Socher, 2020. "Deep learning-enabled breast cancer hormonal receptor status determination from base-level H&E stains," Nature Communications, Nature, vol. 11(1), pages 1-8, December.
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