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Volume 13 Issue 4
Jul.  2022
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Article Navigation >  Medical Journal of Peking Union Medical College Hospital  > 2022  >  13(4): 535-541
JIAO Yiping, WANG Xiangxue, XU Jun. Advances and Challenges in Applied Computational Pathology[J]. Medical Journal of Peking Union Medical College Hospital, 2022, 13(4): 535-541. 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, 2022, 13(4): 535-541. doi: 10.12290/xhyxzz.2022-0287
shu

Advances and Challenges in Applied Computational Pathology

doi: 10.12290/xhyxzz.2022-0287
  • 1.

    Institute for Artificial Intelligence in Medicine, Nanjing University of Information Science and Technology, Nanjing 210044, China

  • 2.

    School of Artificial Intelligence/School of Future Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China

Funds:

National Natural Science Foundation of China U1809205

National Natural Science Foundation of China 62171230

National Natural Science Foundation of China 92159301

National Natural Science Foundation of China 61771249

National Natural Science Foundation of China 91959207

National Natural Science Foundation of China 81871352

More Information
  • Corresponding author: XU Jun, E-mail: jxu@nuist.edu.cn
  • Received Date: 2022-05-23
  • Accepted Date: 2022-06-17
    • Available Online: 2022-06-20
  • Publish Date: 2022-07-30
    Abstract
  • Computational pathology (CP) is an interdiscipline formed by pathology and informatics techniques such as artificial intelligence and computer vision. The research field of CP has been rapidly broadened from lesion detection and immunohistochemistry quantification to predictions of molecular and prognostic properties. CP models, applied intensively in clinics and research, will become an essential assistant tool for diagnosis and disease management. In this paper, we aim to review recent developments in the field of CP, and discuss major challenges and potential solutions in practice and research.
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  • [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. doi:  10.5858/arpa.2015-0093-SA
    [3] Gurcan MN, Boucheron LE, Can A, et al. Histopathological Image Analysis: A Review[J]. IEEE Rev Biomed Eng, 2009, 2: 147-171. doi:  10.1109/RBME.2009.2034865
    [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. doi:  10.1038/s41591-021-01343-4
    [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 doi:  10.1016/j.compbiomed.2020.104129
    [6] Srinidhi CL, Ciga O, Martel AL. Deep neural network models for computational histopathology: A survey[J]. Med Image Anal, 2021, 67: 101813. doi:  10.1016/j.media.2020.101813
    [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. doi:  10.1001/jama.2017.14585
    [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. doi:  10.1097/PAS.0000000000001151
    [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. doi:  10.1038/s41467-020-18147-8
    [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. doi:  10.1038/s41467-021-21467-y
    [11] Wei JW, Tafe LJ, Linnik YA, et al. Pathologist-level classification of histologic patterns on resected lung adenocarc-inoma slides with deep neural networks[J]. Sci Rep, 2019, 9: 1-8. doi:  10.1038/s41598-018-37186-2
    [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. doi:  10.1016/S1470-2045(19)30739-9
    [13] Lu MY, Chen TY, Williamson DF, et al. AI-based patho-logy predicts origins for cancers of unknown primary[J]. Nature, 2021, 594: 106-110. doi:  10.1038/s41586-021-03512-4
    [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. doi:  10.1016/j.ebiom.2020.103054
    [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. doi:  10.1016/j.celrep.2018.03.086
    [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. doi:  10.1158/1078-0432.CCR-18-2013
    [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. doi:  10.1016/j.media.2019.101563
    [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 unsupervi-sed machine-learning method to quantify the morphological heterogeneity of cells and nuclei[J]. Nat Protoc, 2021, 16: 754-774. doi:  10.1038/s41596-020-00432-x
    [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. doi:  10.1016/j.ebiom.2021.103481
    [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. doi:  10.1016/j.ebiom.2021.103492
    [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. doi:  10.1038/s41467-019-13993-7
    [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. doi:  10.1038/s41551-020-0578-x
    [25] Yamashita R, Long J, Longacre T, et al. Deep learning model for the prediction of microsatellite instability in colore-ctal cancer: a diagnostic study[J]. Lancet Oncol, 2021, 22: 132-141. doi:  10.1016/S1470-2045(20)30535-0
    [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. doi:  10.1002/path.5800
    [27] Jain MS, Massoud TF. Predicting tumour mutational burden from histopathological images using multiscale deep learning[J]. Nat Mach Intell, 2020, 2: 356-362. doi:  10.1038/s42256-020-0190-5
    [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. doi:  10.1038/s41591-018-0177-5
    [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. doi:  10.1038/s42256-019-0068-6
    [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. doi:  10.1038/s41591-019-0583-3
    [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. doi:  10.1007/s10549-020-06093-4
    [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. doi:  10.1186/s12967-021-03020-z
    [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. doi:  10.1016/j.tranon.2020.100921
    [35] Tang Z, Chuang KV, DeCarli C, et al. Interpretable classification of Alzheimer's disease pathologies with a convolu-tional neural network pipeline[J]. Nat Commun, 2019, 10: 2173. doi:  10.1038/s41467-019-10212-1
    [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. doi:  10.1093/eurheartj/ehab241
    [37] Salvi M, Molinaro L, Metovic J, et al. Fully automated quantitative assessment of hepatic steatosis in liver trans-plants[J]. Comput Biol Med, 2020, 123: 103836. doi:  10.1016/j.compbiomed.2020.103836
    [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. doi:  10.1016/j.media.2020.101835
    [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, observa-tional 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: 325-336.
    [47] Diao JA, Wang JK, Chui WF, et al. Human-interpretable image features derived from densely mapped cancer patho-logy 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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