Publisher Correction: Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer (npj Digital Medicine, (2019), 2, 1, (48), 10.1038/s41746-019-0112-2);Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer

npj Digital Medicine

Volumn 2, Issue 1, 2019, Pages

  Nagpal, Kunal ^a   Foote, Davis ^a   Liu, Yun ^a   Chen, Po Hsuan Cameron ^a   Wulczyn, Ellery ^a   Tan, Fraser ^a   Olson, Niels ^b   Smith, Jenny L ^b   Mohtashamian, Arash ^b   Wren, James H ^c   Corrado, Greg S ^a   MacDonald, Robert ^a   Peng, Lily H ^a   Amin, Mahul B ^d   Evans, Andrew J ^e   Sangoi, Ankur R ^f   Mermel, Craig H ^a   Hipp, Jason D ^a   Stumpe, Martin C ^g  

^a GOOGLE INC   (United States)

^b NAVAL MEDICAL CENTER SAN DIEGO   (United States)

^c HENRY M JACKSON FOUNDATION FOR THE ADVANCEMENT OF MILITARY MEDICINE   (United States)

^d UNIVERSITY OF TENNESSEE HEALTH SCIENCE CENTER   (United States)

^e UNIVERSITY OF TORONTO   (Canada)

^f EL CAMINO HOSPITAL   (United States)

^g Tempus Labs Inc   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DEEP LEARNING; DIAGNOSIS; DISEASES; MORPHOLOGY; PATIENT TREATMENT; TUMORS; UROLOGY;

CANCER PATIENTS; GLEASON SCORES; IMAGE PATCHES; PROGNOSTIC FACTORS; PROSTATE CANCERS; REFERENCE STANDARD; REPRODUCIBILITIES; TUMOR MORPHOLOGY; VALIDATION SETS; WHOLE SLIDE IMAGES;

LEARNING ALGORITHMS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; BIOCHEMICAL RECURRENCE; CANCER PATIENT; CANCER PROGNOSIS; CANCER RECURRENCE; CANCER SURGERY; CANCER SURVIVAL; COHORT ANALYSIS; CONTROLLED STUDY; DEEP LEARNING; DIAGNOSTIC ACCURACY; DISEASE EXACERBATION; FOLLOW UP; GLEASON SCORE; HUMAN; HUMAN TISSUE; INFORMATION PROCESSING; MAJOR CLINICAL STUDY; MALE; PATHOLOGIST; PATIENT RISK; PRIORITY JOURNAL; PROSTATE CANCER; PROSTATECTOMY; RECEIVER OPERATING CHARACTERISTIC; RETROSPECTIVE STUDY;

EID: 85089606203     PISSN: None     EISSN: 23986352     Source Type: Journal    
DOI: 10.1038/s41746-019-0196-8     Document Type: Erratum

References (58)

1. Prostate Cancer—Cancer Stat Facts. https://seer.cancer.gov/statfacts/html/prost.html. Accessed 22 August 2018.
2. Epstein, J. I. et al. A contemporary prostate cancer grading system: a validated alternative to the Gleason score. Eur. Urol. 69, 428–435 (2016).
3. Epstein, J. I., Allsbrook, W. C., Amin, M. B. & Egevad, L. L. The 2005 International Society of Urological Pathology (ISUP) consensus conference on gleason grading of prostatic carcinoma. Am. J. Surg. Pathol. 29, 1228–1242 (2005).
4. Epstein, J. I. et al. The 2014 International Society of Urological Pathology (ISUP) Consensus Conference on Gleason Grading of Prostatic Carcinoma: Definition of Grading Patterns and Proposal for a New Grading System. Am. J. Surg. Pathol. 40, 244–252 (2016).
5. NCCN Clinical Practice Guidelines in Oncology. https://www.nccn.org/professionals/physician_gls/default.aspx#prostate. Accessed 14 August 2018.
6. Persson, J. et al. Interobserver variability in the pathological assessment of radical prostatectomy specimens: findings of the Laparoscopic Prostatectomy Robot Open (LAPPRO) study. Scand. J. Urol. 48, 160–167 (2014).
7. Veloso, S. G. et al. Interobserver agreement of Gleason score and modified Gleason score in needle biopsy and in surgical specimen of prostate cancer. Int. Braz. J. Urol. 33, 639–646 (2007). Discussion 647–51.
8. Montironi, R., Lopez-Beltran, A., Cheng, L., Montorsi, F. & Scarpelli, M. Central prostate pathology review: should it be mandatory? Eur. Urol. 64, 199–201 (2013). Discussion 202–203.
9. Bottke, D. et al. Phase 3 study of adjuvant radiotherapy versus wait and see in pT3 prostate cancer: impact of pathology review on analysis. Eur. Urol. 64, 193–198 (2013).
10. Egevad, L. et al. Standardization of Gleason grading among 337 European pathologists. Histopathology 62, 247–256 (2013).
11. Netto, G. J., Eisenberger, M., Epstein, J. I. & TAX 3501 Trial Investigators. Interobserver variability in histologic evaluation of radical prostatectomy between central and local pathologists: findings of TAX 3501 multinational clinical trial. Urology 77, 1155–1160 (2011).
12. Allsbrook, W. C. Jr et al. Interobserver reproducibility of Gleason grading of prostatic carcinoma: urologic pathologists. Hum. Pathol. 32, 74–80 (2001).
13. Allsbrook, W. C. Jr et al. Interobserver reproducibility of Gleason grading of prostatic carcinoma: general pathologist. Hum. Pathol. 32, 81–88 (2001).
14. Mikami, Y. et al. Accuracy of gleason grading by practicing pathologists and the impact of education on improving agreement. Hum. Pathol. 34, 658–665 (2003).
15. Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017).
16. Haenssle, H. A. et al. Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann. Oncol. 29, 1836–1842 (2018).
17. Gulshan, V. et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316, 2402–2410 (2016).
18. Ting, D. S. W. et al. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA 318, 2211–2223 (2017).
19. Burlina, P. M. et al. Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks. JAMA Ophthalmol. 135, 1170–1176 (2017).
20. De Fauw, J. et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat. Med. 24, 1342–1350 (2018).
21. Kermany, D. S. et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172, 1122–1131.e9 (2018).
22. Rajpurkar, P. et al. CheXNet: radiologist-level pneumonia detection on chest X-rays with deep learning. Preprint at arXiv [cs.CV]. https://arxiv.org/abs/1711.05225 (2017).
23. Chilamkurthy, S. et al. Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. Lancet, 10.1016/S0140-6736(18)31645-3 (2018).
24. Ehteshami Bejnordi, B. et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 318, 2199–2210 (2017).
25. Liu, Y. et al. Detecting cancer metastases on gigapixel pathology images. Preprint at arXiv [cs.CV]. https://arxiv.org/abs/1703.02442 (2017).
26. Campanella, G., Silva, V. W. K. & Fuchs, T. J. Terabyte-scale deep multiple instance learning for classification and localization in pathology. Preprint at arXiv [cs.CV]. https://arxiv.org/abs/1805.06983 (2018).
27. Arvaniti, E. et al. Automated Gleason grading of prostate cancer tissue microarrays via deep learning. Sci. Rep. 8, 12054 (2018).
28. Steiner, D. F. et al. Impact of deep learning assistance on the histopathologic review of lymph nodes for metastatic breast cancer. Am. J. Surg. Pathol., 10.1097/PAS.0000000000001151 (2018).
29. Liu, Y. et al. Artificial intelligence–based breast cancer nodal metastasis detection. Arch. Pathol. Lab. Med. 10.5858/arpa.2018-0147-oa (2018).
30. Leo, P. et al. Stable and discriminating features are predictive of cancer presence and Gleason grade in radical prostatectomy specimens: a multi-site study. Sci. Rep. 8, 14918 (2018).
31. Sparks, R. & Madabhushi, A. Statistical shape model for manifold regularization: Gleason grading of prostate histology. Comput. Vis. Image Under. 117, 1138–1146 (2013).
32. Nguyen, K., Jain, A. K. & Allen, R. L. Automated gland segmentation and classification for gleason grading of prostate tissue images. In: 2010 20th International Conference on Pattern Recognition, 23–26 August 2010, Istanbul (2010).
33. Jiménez del Toro, O. et al. Convolutional neural networks for an automatic classification of prostate tissue slides with high-grade Gleason score. In Medical Imaging 2017: Digital Pathology (Orlando, Florida, USA, 2017).
34. Ma, Z. et al. Semantic segmentation for prostate cancer grading by convolutional neural networks. In Medical Imaging 2018: Digital Pathology (Houston, Texas, USA, 2018).
35. Kallen, H., Molin, J., Heyden, A., Lundstrom, C. & Astrom, K. Towards grading gleason score using generically trained deep convolutional neural networks. 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI) (Prague, Czech Republic, 2016).
36. Zhong, Q. et al. A curated collection of tissue microarray images and clinical outcome data of prostate cancer patients. Sci. Data 4, 170014 (2017).
37. van der Kwast, T. H. et al. Impact of pathology review of stage and margin status of radical prostatectomy specimens (EORTC trial 22911). Virchows Arch. 449, 428–434 (2006).
38. Srigley, J. R. et al. Protocol for the examination of specimens from patients with carcinoma of the prostate gland. Arch. Pathol. Lab. Med. 133, 1568–1576 (2009).
39. Humphrey, P. A., Moch, H., Cubilla, A. L., Ulbright, T. M. & Reuter, V. E. The 2016 WHO classification of tumours of the urinary system and male genital organs—Part B: prostate and bladder tumours. Eur. Urol. 70, 106–119 (2016).
40. Epstein, J. I., Amin, M. B., Reuter, V. E. & Humphrey, P. A. Contemporary Gleason grading of prostatic carcinoma: an update with discussion on practical issues to implement the 2014 International Society of Urological Pathology (ISUP) Consensus Conference on Gleason Grading of Prostatic Carcinoma. Am. J. Surg. Pathol. 41, e1–e7 (2017).
41. Sauter, G. et al. Clinical utility of quantitative gleason grading in prostate biopsies and prostatectomy specimens. Eur. Urol. 69, 592–598 (2016).
42. Cox, D. R. Regression models and life-tables. in Springer Series in Statistics (eds Kotz, S., & Johnson, N. L.) 527–541 (Springer, New York, NY, 1992).
43. Brimo, F., Schultz, L. & Epstein, J. I. The value of mandatory second opinion pathology review of prostate needle biopsy interpretation before radical prostatectomy. J. Urol. 184, 126–130 (2010).
44. Zhou, M. et al. Diagnosis of ‘poorly formed glands’ gleason pattern 4 prostatic adenocarcinoma on needle biopsy: an interobserver reproducibility study among urologic pathologists with recommendations. Am. J. Surg. Pathol. 39, 1331–1339 (2015).
45. Shah, R. B. et al. Diagnosis of Gleason Pattern 5 prostate adenocarcinoma on core needle biopsy. Am. J. Surg. Pathol. 39, 1242–1249 (2015).
46. Gordetsky, J. & Epstein, J. Grading of prostatic adenocarcinoma: current state and prognostic implications. Diagn. Pathol. 11, 25 (2016).
47. Aeffner, F. et al. The gold standard paradox in digital image analysis: manual versus automated scoring as ground truth. Arch. Pathol. Lab. Med. 141, 1267–1275 (2017).
48. Wang, D., Khosla, A., Gargeya, R., Irshad, H. & Beck, A. H. Deep learning for identifying metastatic breast cancer. Preprint at arXiv [q-bio.QM]. https://arxiv.org/abs/1606.05718 (2016).
49. Ehteshami Bejnordi, B. et al. Deep learning-based assessment of tumor-associated stroma for diagnosing breast cancer in histopathology images. In 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) (Melbourne, Australia, 2017).
50. Weinstein, J. N. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat. Genet. 45, 1113–1120 (2013).
51. Liu, J. et al. An integrated TCGA Pan-cancer clinical data resource to drive high-quality survival outcome analytics. Cell 173, 400–416.e11 (2018).
52. Stephenson, A. J. et al. Defining biochemical recurrence of prostate cancer after radical prostatectomy: a proposal for a standardized definition. J. Clin. Oncol. 24, 3973–3978 (2006).
53. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J. & Wojna, Z. Rethinking the Inception architecture for computer vision. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (Las Vegas, NV, USA, 2016).
54. Chen, P.-H. C. et al. Microscope 2.0: an augmented reality microscope with real-time artificial intelligence integration. Preprint at arXiv [cs.CV]. https://arxiv.org/abs/1812.00825 (2018).
55. Bejnordi, B. E. et al. Stain specific standardization of whole-slide histopathological images. IEEE Trans. Med. Imaging 35, 404–415 (2016).
56. Zoph, B., Vasudevan, V., Shlens, J. & Le, Q. V. Learning transferable architectures for scalable image recognition. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (Salt Lake City, UT, USA, 2018).
57. Chollet, F. Xception: deep learning with depthwise separable convolutions. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (Honolulu, HI, USA, 2017).
58. Cohen, J. A coefficient of agreement for nominal Ssales. Educ. Psychol. Meas. 20, 37–46 (1960).