Author Correction: Automated Gleason grading of prostate cancer tissue microarrays via deep learning (Scientific Reports, (2018), 8, 1, (12054), 10.1038/s41598-018-30535-1);Automated Gleason grading of prostate cancer tissue microarrays via deep learning

Scientific Reports

Volumn 8, Issue 1, 2018, Pages

  Arvaniti, Eirini ^a,d   Fricker, Kim S ^b   Moret, Michael ^a   Rupp, Niels ^b   Hermanns, Thomas ^b   Fankhauser, Christian ^b   Wey, Norbert ^b   Wild, Peter J ^b,c   Rüschoff, Jan H ^b   Claassen, Manfred ^a,d  

^a ETH ZURICH   (Switzerland)

^b UNIVERSITY OF ZURICH   (Switzerland)

^c UNIVERSITY HOSPITAL FRANKFURT   (Germany)

^d SWISS INSTITUTE OF BIOINFORMATICS   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL MODEL; CANCER GRADING; COHORT ANALYSIS; FEASIBILITY STUDY; HUMAN; MALE; MIDDLE AGED; MORTALITY; PATHOLOGY; PROCEDURES; PROGNOSIS; PROSTATE; PROSTATE TUMOR; REPRODUCIBILITY; SURVIVAL ANALYSIS; TISSUE MICROARRAY;

COHORT STUDIES; DEEP LEARNING; FEASIBILITY STUDIES; HUMANS; MALE; MIDDLE AGED; MODELS, BIOLOGICAL; NEOPLASM GRADING; PROGNOSIS; PROSTATE; PROSTATIC NEOPLASMS; REPRODUCIBILITY OF RESULTS; SURVIVAL ANALYSIS; TISSUE ARRAY ANALYSIS;

EID: 85051675515     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/s41598-019-43989-8     Document Type: Erratum

References (42)

1. WHO Classification of Tumours of the Urinary System and Male Genital Organs. International Agency for Research on Cancer (IARC) (2016)
2. Gleason, D. F. & Mellinger, G. T. Prediction of prognosis for prostatic adenocarcinoma by combined histological grading and clinical staging. J. Urol. 111, 58–64 (1974)
3. Faraj, S. F. et al. Clinical Validation of the 2005 ISUP Gleason Grading System in a Cohort of Intermediate and High Risk Men Undergoing Radical Prostatectomy. PLoS One 11, e0146189 (2016)
4. Gordetsky, J. & Epstein, J. Grading of prostatic adenocarcinoma: current state and prognostic implications. Diagn. Pathol. 11, 25 (2016)
5. Epstein, J. I. Prostate cancer grading: a decade after the 2005 modified system. Mod. Pathol. 31, S47–63 (2018)
6. Fuchs, T. J. & Buhmann, J. M. Computational pathology: Challenges and promises for tissue analysis. Comput. Med. Imaging Graph. 35, 515–530 (2011)
7. Ghaznavi, F., Evans, A., Madabhushi, A. & Feldman, M. Digital imaging in pathology: whole-slide imaging and beyond. Annu. Rev. Pathol. 8, 331–359 (2013)
8. Beck, A. H. et al. Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Sci. Transl. Med. 3, 108ra113 (2011)
9. Nguyen, K., Sabata, B. & Jain, A. K. Prostate cancer grading: Gland segmentation and structural features. Pattern Recognit. Lett. 33, 951–961 (2012)
10. Doyle, S., Feldman, M. D., Shih, N., Tomaszewski, J. & Madabhushi, A. Cascaded discrimination of normal, abnormal, and confounder classes in histopathology: Gleason grading of prostate cancer. BMC Bioinformatics 13, 282 (2012)
11. LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015)
12. Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017)
13. Rajpurkar, P. et al. CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning. arXiv preprint arXiv 1711, 05225 (2017)
14. Poplin, R. et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nature Biomedical Engineering 2, 158–164 (2018)
15. Angermueller, C., Pärnamaa, T., Parts, L. & Stegle, O. Deep learning for computational biology. Mol. Syst. Biol. 12, 878 (2016)
16. Cireşan, D. C., Giusti, A., Gambardella, L. M. & Schmidhuber, J. Mitosis detection in breast cancer histology images with deep neural networks. Med. Image Comput. Comput. Assist. Interv. 16, 411–418 (2013)
17. Cruz-Roa, A. et al. Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks. Medical Imaging 2014: Digital Pathology 9041. International Society for Optics and Photonics (2014)
18. Litjens, G. et al. Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci. Rep. 6, 26286 (2016)
19. Liu, Y. et al. Detecting Cancer Metastases on Gigapixel Pathology Images. arXiv preprint arXiv:1703.02442 (2017)
20. Schaumberg, A. J., Rubin, M. A. & Fuchs, T. J. H & E-stained Whole Slide Deep Learning Predicts SPOP Mutation State in Prostate Cancer. bioRxiv preprint, https://doi.org/10.1101/064279 (2016)
21. Kumar, N. et al. Convolutional Neural Networks for Prostate Cancer Recurrence Prediction. Medical Imaging 2017: Digital Pathology 10140. International Society for Optics and Photonics (2017)
22. Zerhouni, E., Prisacari, B., Zhong, Q., Wild, P. & Gabrani, M. Heterogeneity characterization of immunohistochemistry stained tissue using convolutional autoencoder. Medical Imaging 2017: Digital Pathology 10140. International Society for Optics and Photonics (2017)
23. Källén, H., Molin, J., Heyden, A., Lundström, C. & Åström, K. Towards grading gleason score using generically trained deep convolutional neural networks. IEEE 13th International Symposium on Biomedical Imaging (ISBI) 1163–1167 (2016)
24. Zhou, N., Fedorov, A., Fennessy, F., Kikinis, R. & Gao, Y. Large scale digital prostate pathology image analysis combining feature extraction and deep neural network. arXiv preprint arXiv:1705.02678 (2017)
25. Cancer Genome Atlas Research Network et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat. Genet. 45, 1113–1120 (2013)
26. del Toro, O. J. et al. Convolutional neural networks for an automatic classification of prostate tissue slides with high-grade Gleason score. Medical Imaging 2017: Digital Pathology 10140. International Society for Optics and Photonics (2017)
27. Zhong, Q. et al. A curated collection of tissue microarray images and clinical outcome data of prostate cancer patients. Sci Data 4, 170014 (2017)
28. Russakovsky, O. et al. ImageNet Large Scale Visual Recognition Challenge. Int. J. Comput. Vis. 115, 211–252 (2015)
29. Simonyan, K. & Zisserman, A. Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv preprint arXiv 1409, 1556 (2014)
30. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J. & Wojna, Z. Rethinking the Inception Architecture for Computer Vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)
31. He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)
32. Zhuang, G., Huang, L., Weinberger, K. Q. & van der Maaten, L. Densely Connected Convolutional Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)
33. Howard, A. G. et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv preprint arXiv:1704.04861 (2017)
34. Cohen, J. A Coefficient of Agreement for Nominal Scales. Educ. Psychol. Meas. 20, 37–46 (1960)
35. Zhou, B., Khosla, A., Lapedriza, A., Oliva, A. & Torralba, A. Learning deep features for discriminative localization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2921–2929 (2016)
36. Benjamini, Y. & Hochberg, J. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the royal statistical society: Series B (Methodological) 289–300 (1995)
37. Epstein, J. I. et al. A Contemporary Prostate Cancer Grading System: A Validated Alternative to the Gleason Score. Eur. Urol. 69, 428–435 (2016)
38. Zhong, Q. et al. Image-based computational quantification and visualization of genetic alterations and tumour heterogeneity. Sci Rep. 6, 24146 (2016)
39. Mortezavi, A. et al. KPNA2 expression is an independent adverse predictor of biochemical recurrence after radical prostatectomy. Clin. Cancer Res. 17, 1111–1121 (2011)
40. Fankhauser, C. D. et al. Comprehensive immunohistochemical analysis of PD-L1 shows scarce expression in castration-resistant prostate cancer. Oncotarget 9, 10284–10293 (2018)
41. Schüffler. TMARKER: A free software toolkit for histopathological cell counting and staining estimation. J. Pathol. Inform. 4, 2 (2013)
42. Kingma, D. & Ba, J. Adam: A Method for Stochastic Optimization. arXiv preprint arXiv:1412.6980 (2014)