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计算病理学应用研究进展与挑战

焦一平 王向学 徐军

焦一平, 王向学, 徐军. 计算病理学应用研究进展与挑战[J]. 协和医学杂志. doi: 10.12290/xhyxzz.2022-0287
引用本文: 焦一平, 王向学, 徐军. 计算病理学应用研究进展与挑战[J]. 协和医学杂志. doi: 10.12290/xhyxzz.2022-0287
JIAO Yiping, WANG Xiangxue, XU Jun. Advances and Challenges in Applied Computational Pathology[J]. Medical Journal of Peking Union Medical College Hospital. doi: 10.12290/xhyxzz.2022-0287
Citation: JIAO Yiping, WANG Xiangxue, XU Jun. Advances and Challenges in Applied Computational Pathology[J]. Medical Journal of Peking Union Medical College Hospital. doi: 10.12290/xhyxzz.2022-0287

计算病理学应用研究进展与挑战

doi: 10.12290/xhyxzz.2022-0287
基金项目: 

国家自然科学基金( U1809205, 62171230, 92159301,61771249, 91959207, 81871352)

详细信息
    通讯作者:

    徐军,E-mail:jxu@nuist.edu.cn

  • 中图分类号: R36;TP183

Advances and Challenges in Applied Computational Pathology

  • 摘要: 计算病理学是病理学与人工智能、计算机视觉等信息技术交叉形成的细分领域,其研究范畴已从病灶检测、免疫组化染色细胞计数迅速拓展至分子病理、预后以及治疗响应等标签的预测中。计算病理模型已在临床研究中得到较为深入的应用,将成为精准医疗背景下重要的辅助诊疗工具。本文对计算病理领域最新应用进展进行综述,并探讨相关研究与应用中的主要挑战与对策。
  • [1] 步宏,李一雷.病理学(第9版)[M].北京:人民卫生出版社, 2018:371.
    [2] Louis DN, Feldman M, Carter AB, et al. Computational Pathology:A Path Ahead[J]. Arch Pathol Lab Med, 2016, 140:41-50.
    [3] Gurcan MN, Boucheron LE, Can A, et al. Histopathological Image Analysis:A Review[J]. IEEE Rev Biomed Eng, 2009, 2:147-171.
    [4] van der Laak J, Litjens G, Ciompi F. Deep learning in histopathology:the path to the clinic[J]. Nat Med, 2021, 27:775-784.
    [5] Salvi M, Acharya UR, Molinari F, et al. The impact of pre-and post-image processing techniques on deep learning frameworks:A comprehensive review for digital pathology image analysis[J]. Comput Biol Med, 2021, 128:104129
    [6] Srinidhi CL, Ciga O, Martel AL. Deep neural network models for computational histopathology:A survey[J]. Med Image Anal, 2021, 67:101813.
    [7] Bejnordi BE, Veta M, Van Diest PJ, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer[J]. JAMA, 2017, 318:2199-2210.
    [8] Steiner DF, MacDonald R, Liu Y, et al. Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast Cancer[J]. Am J Surg Pathol, 2018, 42:1636-1646.
    [9] Song Z, Zou S, Zhou W, et al. Clinically applicable histopathological diagnosis system for gastric cancer detection using deep learning[J]. Nat Commun, 2020, 11:4294.
    [10] Chen CL, Chen CC, Yu WH, et al. An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning[J]. Nat Commun, 2021, 12:1193.
    [11] Wei JW, Tafe LJ, Linnik YA, et al. Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks[J]. Sci Rep, 2019, 9:1-8.
    [12] Bulten W, Pinckaers H, van Boven H, et al. Automated deep-learning system for Gleason grading of prostate cancer using biopsies:a diagnostic study[J]. Lancet Oncol, 2020, 21:233-241.
    [13] Lu MY, Chen TY, Williamson DF, et al. AI-based pathology predicts origins for cancers of unknown primary[J]. Nature, 2021, 594:106-110.
    [14] Zhao K, Li Z, Yao S, et al. Artificial intelligence quantified tumour-stroma ratio is an independent predictor for overall survival in resectable colorectal cancer[J]. EBioMedicine, 2020, 61:103054.
    [15] Saltz J, Gupta R, Hou L, et al. Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images[J]. Cell Rep, 2018, 23:181-193.e7.
    [16] Corredor G, Wang X, Zhou Y, et al. Spatial Architecture and Arrangement of Tumor-Infiltrating Lymphocytes for Predicting Likelihood of Recurrence in Early-Stage Non-Small Cell Lung Cancer[J]. Clin Cancer Res, 2019, 25:1526-1534.
    [17] Graham S, Vu QD, Raza SEA, et al. Hover-Net:Simultaneous segmentation and classification of nuclei in multi-tissue histology images[J]. Med Image Anal, 2019, 58:101563.
    [18] Gamper J, Koohbanani NA, Benes K, et al. PanNuke dataset extension, insights and baselines[J]. arXiv preprint arXiv:2003.10778, 2020.
    [19] Graham S, Jahanifar M, Azam A, et al. Lizard:A Large-Scale Dataset for Colonic Nuclear Instance Segmentation and Classification[C]. Proceedings of the IEEE/CVF International Conference on Computer Vision,2021:684-693.
    [20] Phillip JM, Han KS, Chen WC, et al.A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei[J]. Nat Protoc, 2021, 16:754-774.
    [21] Wang X, Bera K, Barrera C, et al. A prognostic and predictive computational pathology image signature for added benefit of adjuvant chemotherapy in early stage non-small-cell lung cancer[J]. EBioMedicine, 2021, 69:103481.
    [22] Sun P, He J, Chao X, et al. A Computational Tumor-Infiltrating Lymphocyte Assessment Method Comparable with Visual Reporting Guidelines for Triple-Negative Breast Cancer[J]. EBioMedicine, 2021, 70:103492.
    [23] Naik N, Madani A, Esteva A, et al. Deep learning-enabled breast cancer hormonal receptor status determination from base-level H&E stains[J]. Nat Commun, 2020, 11:1-8.
    [24] He B, Bergenstråhle L, Stenbeck L, et al. Integrating spatial gene expression and breast tumour morphology via deep learning[J]. Nat Biomed Eng, 2020, 4:827-834.
    [25] Yamashita R, Long J, Longacre T, et al. Deep learning model for the prediction of microsatellite instability in colorectal cancer:a diagnostic study[J]. Lancet Oncol, 2021, 22:132-141.
    [26] Schrammen PL, Ghaffari Laleh N, Echle A, et al. Weakly supervised annotation-free cancer detection and prediction of genotype in routine histopathology[J]. J Pathol, 2022, 256:50-60.
    [27] Jain MS, Massoud TF. Predicting tumour mutational burden from histopathological images using multiscale deep learning[J]. Nat Mach Intell, 2020, 2:356-362.
    [28] Coudray N, Ocampo PS, Sakellaropoulos T, et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning[J]. Nat Med, 2018, 24:1559-1567.
    [29] Faust K, Bala S, van Ommeren R, et al. Intelligent feature engineering and ontological mapping of brain tumour histomorphologies by deep learning[J]. Nat Mach Intell, 2019, 1:316-321.
    [30] Courtiol P, Maussion C, Moarii M, et al. Deep learning-based classification of mesothelioma improves prediction of patient outcome[J]. Nat Med, 2019, 25:1519-1525.
    [31] Dodington DW, Lagree A, Tabbarah S, et al. Analysis of tumor nuclear features using artificial intelligence to predict response to neoadjuvant chemotherapy in high-risk breast cancer patients[J]. Breast Cancer Res Treat, 2021, 186:379-389.
    [32] Li F, Yang Y, Wei Y, et al. Deep learning-based predictive biomarker of pathological complete response to neoadjuvant chemotherapy from histological images in breast cancer[J]. J Transl Med, 2021, 19:348.
    [33] Zhang F, Yao S, Li Z, et al. Predicting treatment response to neoadjuvant chemoradiotherapy in local advanced rectal cancer by biopsy digital pathology image features[J]. Clin Transl Med, 2020,10:e110.
    [34] Hu J, Cui C, Yang W, et al. Using deep learning to predict anti-PD-1 response in melanoma and lung cancer patients from histopathology images[J]. Transl Oncol, 2021, 14:100921.
    [35] Tang Z, Chuang KV, DeCarli C, et al. Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline[J]. Nat Commun, 2019, 10:2173.
    [36] Peyster EG, Arabyarmohammadi S, Janowczyk A, et al. An automated computational image analysis pipeline for histological grading of cardiac allograft rejection[J]. Eur Heart J, 2021, 42:2356-2369.
    [37] Salvi M, Molinaro L, Metovic J, et al. Fully automated quantitative assessment of hepatic steatosis in liver transplants[J]. Comput Biol Med, 2020, 123:103836.
    [38] Xu J, Lu H, Li H, et al. Computerized spermatogenesis staging (CSS) of mouse testis sections via quantitative histomorphological analysis[J]. Med Image Anal, 2021, 70:101835.
    [39] Krijgsman D, van Leeuwen MB, van der Ven J, et al. Quantitative Whole Slide Assessment of Tumor-Infiltrating CD8-Positive Lymphocytes in ER-Positive Breast Cancer in Relation to Clinical Outcome[J]. IEEE J Biomed Health Inform, 2021, 25:381-392.
    [40] Wang X, Wang L, Bu H, et al. How can artificial intelligence models assist PD-L1 expression scoring in breast cancer:results of multi-institutional ring studies[J]. Npj Breast Cancer, 2021, 7:61.
    [41] Valkonen M, Isola J, Ylinen O, et al. Cytokeratin-Supervised Deep Learning for Automatic Recognition of Epithelial Cells in Breast Cancers Stained for ER, PR, and Ki-67[J]. IEEE Trans Med Imaging, 2020, 39:534-542.
    [42] Bao H, Bi H, Zhang X, et al. Artificial intelligence-assisted cytology for detection of cervical intraepithelial neoplasia or invasive cancer:A multicenter, clinical-based, observational study[J]. Gynecol Oncol, 2020, 159:171-178.
    [43] Xie W, Reder NP, Koyuncu C, et al. Prostate Cancer Risk Stratification via Nondestructive 3D Pathology with Deep Learning-Assisted Gland Analysis[J]. Cancer Res, 2022, 82:334-345.
    [44] Tellez D, Balkenhol M, Otte-Holler I, et al. Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks[J]. IEEE Trans Med Imaging, 2018, 37:2126-2136.
    [45] Campanella G, Hanna MG, Geneslaw L, et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images[J]. Nat Med, 2019, 25:1301-1309.
    [46] Stacke K, Eilertsen G, Unger J, et al. Measuring Domain Shift for Deep Learning in Histopathology[J]. IEEE J Biomed Health Inform, 2021, 25(2):325-336.
    [47] Diao JA, Wang JK, Chui WF, et al. Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes[J]. Nat Commun, 2021, 12:1613.
    [48] Kalra S, Tizhoosh HR, Shah S, et al. Pan-cancer diagnostic consensus through searching archival histopathology images using artificial intelligence[J]. Npj Digit Med, 2020, 3:31.
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出版历程
  • 收稿日期:  2022-05-23
  • 录用日期:  2022-06-17
  • 网络出版日期:  2022-06-23

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