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  • Review Article
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A systematic review and meta-analysis of artificial intelligence diagnostic accuracy in prostate cancer histology identification and grading

Prostate Cancer and Prostatic Diseases volume 26, pages 681–692 (2023)Cite this article

Subjects

Abstract

Background

Artificial intelligence (AI) is a promising tool in pathology, including cancer diagnosis, subtyping, grading, and prognostic prediction.

Methods

The aim of the study is to assess AI application in prostate cancer (PCa) histology. We carried out a systematic literature search in 3 databases. Primary outcome was AI accuracy in differentiating between PCa and benign hyperplasia. Secondary outcomes were AI accuracy in determining Gleason grade and agreement among AI and pathologists.

Results

Our final sample consists of 24 studies conducted from 2007 to 2021. They aggregate data from roughly 8000 cases of prostate biopsy and 458 cases of radical prostatectomy (RP). Sensitivity for PCa diagnostic exceeded 90% and ranged from 87% to 100%, and specificity varied from 68% to 99%. Overall accuracy ranged from 83.7% to 98.3% with AUC reaching 0.99. The meta-analysis using the Mantel-Haenszel method showed pooled sensitivity of 0.96 with I2 = 80.7% and pooled specificity of 0.95 with I2 = 86.1%. Pooled positive likehood ratio was 15.3 with I2 = 87.3% and negative – was 0.04 with I2 = 78.6%. SROC (symmetric receiver operating characteristics) curve represents AUC = 0.99. For grading the accuracy of AI was lower: sensitivity for Gleason grading ranged from 77% to 87%, and specificity from 82% to 90%.

Conclusions

The accuracy of AI for PCa identification and grading is comparable to expert pathologists. This is a promising approach which has several possible clinical applications resulting in expedite and optimize pathology reports. AI introduction into common practice may be limited by difficult and time-consuming convolutional neural network training and tuning.

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Fig. 1: PRISMA flow chart.
Fig. 2: SROC curve.

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Data availability

The authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials. Any additional data related to this study are available on request from the corresponding author Dmitry Enikeev.

References

  1. Stamatiou K, Alevizos A, Agapitos E, Sofras F. Incidence of impalpable carcinoma of the prostate and of non-malignant and precarcinomatous lesions in Greek male population: An autopsy study. Prostate [Internet]. 2006;66:1319–28. https://onlinelibrary.wiley.com/doi/10.1002/pros.20339

    Article  PubMed  Google Scholar 

  2. Culp MB, Soerjomataram I, Efstathiou JA, Bray F, Jemal A. Recent global patterns in prostate cancer incidence and mortality rates. Eur Urol [Internet]. 2020;77:38–52. https://linkinghub.elsevier.com/retrieve/pii/S0302283819306190

    Article  PubMed  Google Scholar 

  3. Sun Y, Reynolds HM, Parameswaran B, Wraith D, Finnegan ME, Williams S, et al. Multiparametric MRI and radiomics in prostate cancer: A review. Australas Phys Eng Sci Med [Internet]. 2019;42:3–25. http://link.springer.com/10.1007/s13246-019-00730-z

    Article  PubMed  Google Scholar 

  4. Szentirmai E, Giannico GA. Intraductal carcinoma of the prostate. Pathologica 2020;112:17–24.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Truong M, Frye T, Messing E, Miyamoto H. Historical and contemporary perspectives on cribriform morphology in prostate cancer. Nat Rev Urol [Internet]. 2018;15:475–82. http://www.nature.com/articles/s41585-018-0013-1

    Article  PubMed  Google Scholar 

  6. Hamet P, Tremblay J. Artificial intelligence in medicine. Metab [Internet]. 2017;69:S36–40. https://linkinghub.elsevier.com/retrieve/pii/S002604951730015X

    Article  CAS  Google Scholar 

  7. Ramesh A, Kambhampati C, Monson J, Drew P. Artificial intelligence in medicine. Ann R Coll Surg Engl [Internet]. 2004;86:334–8. http://www.ingentaselect.com/rpsv/cgi-bin/cgi?ini=xref&body=linker&reqdoi=10.1308/147870804290

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Akkus Z, Cai J, Boonrod A, Zeinoddini A, Weston AD, Philbrick KA, et al. A Survey of Deep-Learning Applications in Ultrasound: Artificial Intelligence–Powered Ultrasound for Improving Clinical Workflow. J Am Coll Radio [Internet]. 2019;16:1318–28. https://linkinghub.elsevier.com/retrieve/pii/S1546144019307112

    Article  Google Scholar 

  9. Mintz Y, Brodie R. Introduction to artificial intelligence in medicine. Minim Invasive Ther Allied Technol [Internet]. 2019;28:73–81. https://www.tandfonline.com/doi/full/10.1080/13645706.2019.1575882

    Article  PubMed  Google Scholar 

  10. Niel O, Bastard P. Artificial Intelligence in Nephrology: Core Concepts, Clinical Applications, and Perspectives. Am J Kidney Dis [Internet]. 2019;74:803–10. https://linkinghub.elsevier.com/retrieve/pii/S027263861930842X

    Article  PubMed  Google Scholar 

  11. Jiang Y, Yang M, Wang S, Li X, Sun Y. Emerging role of deep learning‐based artificial intelligence in tumor pathology. Cancer Commun [Internet]. 2020;40:154–66. https://onlinelibrary.wiley.com/doi/10.1002/cac2.12012

    Article  Google Scholar 

  12. Niazi MKK, Parwani AV, Gurcan MN. Digital pathology and artificial intelligence. Lancet Oncol [Internet]. 2019;20:e253–61. https://linkinghub.elsevier.com/retrieve/pii/S1470204519301548

    Article  PubMed  Google Scholar 

  13. Acs B, Rantalainen M, Hartman J. Artificial intelligence as the next step towards precision pathology. J Intern Med [Internet]. 2020;288:62–81. https://onlinelibrary.wiley.com/doi/10.1111/joim.13030

    Article  CAS  PubMed  Google Scholar 

  14. Phillips B, Ball C, Sackett D, Badenoch D, Straus S, Haynes B, et al. Levels of Evidence [Internet]. Oxford Centre for Evidence-based Medicine. [cited 2020 Aug 20]. Available from: https://www.cebm.net/2009/06/oxford-centre-evidence-based-medicine-levels-evidence-march-2009/

  15. Wittke C, Mayer J, Schweiggert F. On the classification of prostate carcinoma with methods from spatial statistics. IEEE Trans Inf Technol Biomed [Internet]. 2007;11:406–14. http://ieeexplore.ieee.org/document/4267694/

    Article  PubMed  Google Scholar 

  16. Gorelick L, Veksler O, Gaed M, Gomez JA, Moussa M, Bauman G, et al. Prostate Histopathology: Learning Tissue Component Histograms for Cancer Detection and Classification. IEEE Trans Med Imaging [Internet]. 2013;32:1804–18. http://ieeexplore.ieee.org/document/6522505/

    Article  PubMed  Google Scholar 

  17. Chen C, Huang Y, Fang P, Liang C, Chang R. A computer‐aided diagnosis system for differentiation and delineation of malignant regions on whole‐slide prostate histopathology image using spatial statistics and multidimensional DenseNet. Med Phys [Internet]. 2020;47:1021–33. https://onlinelibrary.wiley.com/doi/10.1002/mp.13964

    Article  PubMed  Google Scholar 

  18. Lucas M, Jansen I, Savci-Heijink CD, Meijer SL, de Boer OJ, van Leeuwen TG, et al. Deep learning for automatic Gleason pattern classification for grade group determination of prostate biopsies. Virchows Arch [Internet]. 2019;475:77–83. http://link.springer.com/10.1007/s00428-019-02577-x

    Article  PubMed  Google Scholar 

  19. Nir G, Karimi D, Goldenberg SL, Fazli L, Skinnider BF, Tavassoli P, et al. Comparison of Artificial Intelligence Techniques to Evaluate Performance of a Classifier for Automatic Grading of Prostate Cancer From Digitized Histopathologic Images. JAMA Netw Open [Internet]. 2019;2:e190442 http://jamanetworkopen.jamanetwork.com/article.aspx?doi=10.1001/jamanetworkopen.2019.0442

    Article  PubMed  Google Scholar 

  20. Arvaniti E, Fricker KS, Moret M, Rupp N, Hermanns T, Fankhauser C, et al. Automated Gleason grading of prostate cancer tissue microarrays via deep learning. Sci Rep. [Internet]. 2018;8:12054 http://www.nature.com/articles/s41598-018-30535-1

    Article  PubMed  Google Scholar 

  21. Bulten W, Bándi P, Hoven J, Loo R, van de, Lotz J, Weiss N, et al. Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standard. Sci Rep. [Internet]. 2019;9:864 http://www.nature.com/articles/s41598-018-37257-4

    Article  PubMed  Google Scholar 

  22. Ambrosini P, Hollemans E, Kweldam CF, van Leenders GJLH, Stallinga S, Vos F. Automated detection of cribriform growth patterns in prostate histology images. Sci Rep. [Internet]. 2020;10:1–13. https://doi.org/10.1038/s41598-020-71942-7

    Article  CAS  Google Scholar 

  23. Bulten W, Pinckaers H, van Boven H, Vink R, de Bel T, van Ginneken B, et al. Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study. Lancet Oncol [Internet]. 2020;21:233–41. https://doi.org/10.1016/S1470-2045(19)30739-9

    Article  PubMed  Google Scholar 

  24. Egevad L, Swanberg D, Delahunt B, Ström P, Kartasalo K, Olsson H, et al. Identification of areas of grading difficulties in prostate cancer and comparison with artificial intelligence assisted grading. Virchows Arch [Internet]. 2020;477:777–86. https://link.springer.com/10.1007/s00428-020-02858-w

    Article  CAS  PubMed  Google Scholar 

  25. Lokhande A, Bonthu S, Singhal N Carcino-Net: A Deep Learning Framework for Automated Gleason Grading of Prostate Biopsies. In: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) [Internet]. IEEE; 2020. p. 1380–3. Available from: https://ieeexplore.ieee.org/document/9176235/

  26. Marginean F, Arvidsson I, Simoulis A, Christian Overgaard N, Åström K, Heyden A, et al. An Artificial Intelligence–based Support Tool for Automation and Standardisation of Gleason Grading in Prostate Biopsies. Eur Urol. Focus [Internet]. 2020;46:5–11. https://linkinghub.elsevier.com/retrieve/pii/S2405456920302960

    Google Scholar 

  27. Nagpal K, Foote D, Tan F, Liu Y, Chen P-HC, Steiner DF, et al. Development and Validation of a Deep Learning Algorithm for Gleason Grading of Prostate Cancer From Biopsy Specimens. JAMA Oncol [Internet]. 2020;6:1372 https://jamanetwork.com/journals/jamaoncology/fullarticle/2768225

    Article  PubMed  Google Scholar 

  28. Pantanowitz L, Quiroga-Garza GM, Bien L, Heled R, Laifenfeld D, Linhart C, et al. An artificial intelligence algorithm for prostate cancer diagnosis in whole slide images of core needle biopsies: a blinded clinical validation and deployment study. Lancet Digit Heal [Internet] 2020;2:e407–16. https://doi.org/10.1016/S2589-7500(20)30159-X

    Article  Google Scholar 

  29. Raciti P, Sue J, Ceballos R, Godrich R, Kunz JD, Kapur S, et al. Novel artificial intelligence system increases the detection of prostate cancer in whole slide images of core needle biopsies. Mod Pathol [Internet]. 2020;33:2058–66. https://doi.org/10.1038/s41379-020-0551-y

    Article  PubMed  Google Scholar 

  30. Silva-Rodríguez J, Colomer A, Sales MA, Molina R, Naranjo V. Going deeper through the Gleason scoring scale: An automatic end-to-end system for histology prostate grading and cribriform pattern detection. Comput Methods Prog Biomed [Internet]. 2020;195:105637. https://linkinghub.elsevier.com/retrieve/pii/S016926072031470X

    Article  Google Scholar 

  31. Steiner DF, Nagpal K, Sayres R, Foote DJ, Wedin BD, Pearce A, et al. Evaluation of the Use of Combined Artificial Intelligence and Pathologist Assessment to Review and Grade Prostate Biopsies. JAMA Netw Open [Internet]. 2020;3:e2023267. https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2772831

    Article  PubMed  Google Scholar 

  32. Ström P, Kartasalo K, Olsson H, Solorzano L, Delahunt B, Berney DM, et al. Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study. Lancet Oncol [Internet]. 2020;21:222–32. https://linkinghub.elsevier.com/retrieve/pii/S1470204519307387

    Article  PubMed  Google Scholar 

  33. Yang Q, Xu Z, Liao C, Cai J, Huang Y, Chen H, et al. Epithelium segmentation and automated Gleason grading of prostate cancer via deep learning in label‐free multiphoton microscopic images. J Biophotonics [Internet]. 2020;13:1–11. https://onlinelibrary.wiley.com/doi/10.1002/jbio.201900203

    CAS  Google Scholar 

  34. Bulten W, Balkenhol M, Belinga J-JA, Brilhante A, Çakır A, Egevad L, et al. Artificial intelligence assistance significantly improves Gleason grading of prostate biopsies by pathologists. Mod Pathol [Internet]. 2021;34:660–71. https://www.nature.com/articles/s41379-020-0640-y

    Article  CAS  PubMed  Google Scholar 

  35. Kudo MS, de Souza VMG, de Souza Amaral G, de Souza Melo PA, Estivallet CLN, Santos ER, et al. The potential of convolutional neural network diagnosing prostate cancer. Res Biomed Eng [Internet]. 2021;37:25–31. http://link.springer.com/10.1007/s42600-020-00095-3

    Article  Google Scholar 

  36. Mun Y, Paik I, Shin S-J, Kwak T-Y, Chang H. Yet Another Automated Gleason Grading System (YAAGGS) by weakly supervised deep learning. npj Digit Med [Internet] 2021;4:99. https://doi.org/10.1038/s41746-021-00469-6

    Article  PubMed  Google Scholar 

  37. Perincheri S, Levi AW, Celli R, Gershkovich P, Rimm D, Morrow JS, et al. An independent assessment of an artificial intelligence system for prostate cancer detection shows strong diagnostic accuracy. Mod Pathol [Internet]. 2021;34:1588–95. https://doi.org/10.1038/s41379-021-00794-x

    Article  Google Scholar 

  38. Silva LM, Pereira EM, Salles PG, Godrich R, Ceballos R, Kunz JD, et al. Independent real‐world application of a clinical‐grade automated prostate cancer detection system. J Pathol [Internet]. 2021;254:147–58. https://onlinelibrary.wiley.com/doi/10.1002/path.5662

    Article  PubMed  Google Scholar 

  39. Rigby AS. Statistical methods in epidemiology. v. Towards an understanding of the kappa coefficient. Disabil Rehabil [Internet]. 2000;22:339–44. http://www.tandfonline.com/doi/full/10.1080/096382800296575

    Article  CAS  PubMed  Google Scholar 

  40. Swets J. Measuring the accuracy of diagnostic systems. Science [Internet] 1988;240:1285–93. https://www.sciencemag.org/lookup/doi/10.1126/science.3287615

    Article  CAS  PubMed  Google Scholar 

  41. Rivero Belenchón I, Checcucci E, Gómez Rivas J, Puliatti S, Taratkin M, Kowalewski KF, et al. Comment on “Artificial intelligence to predict oncological outcome directly from hematoxylin and eosin-stained slides in urology: a systematic review.”. Minerva Urol Nephrol. 2022;74:810–2.

    PubMed  Google Scholar 

  42. Checcucci E, Rosati S, De Cillis S, Vagni M, Giordano N, Piana A, et al. Artificial intelligence for target prostate biopsy outcomes prediction the potential application of fuzzy logic. Prostate Cancer Prostatic Dis. 2022;25:359–62.

    Article  PubMed  Google Scholar 

  43. Checcucci E, De Cillis S, Granato S, Chang P, Afyouni AS, Okhunov Z. Applications of neural networks in urology: a systematic review. Curr Opin Urol. 2020;30:788–807.

    Article  PubMed  Google Scholar 

  44. Kumar N, Gupta R, Gupta S. Whole Slide Imaging (WSI) in Pathology: Current Perspectives and Future Directions. J Digit Imaging. 2020;33:1034–40.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Spratt DE, Sun Y, Van der Wal D, Huang S-C, Mohamad O, Armstrong AJ, et al. An AI-derived digital pathology-based biomarker to predict the benefit of androgen deprivation therapy in localized prostate cancer with validation in NRG/RTOG 9408. J Clin Oncol [Internet]. 2022;40:223–223. https://doi.org/10.1200/JCO.2022.40.6_suppl.223. 6_suppl

    Article  Google Scholar 

  46. Beck AH, Sangoi AR, Leung S, Marinelli RJ, Nielsen TO, van de Vijver MJ, et al. Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Sci Transl Med. 2011;3:108ra113.

    Article  PubMed  Google Scholar 

  47. Doyle S, Feldman MD, Shih N, Tomaszewski J, Madabhushi A. Cascaded discrimination of normal, abnormal, and confounder classes in histopathology: Gleason grading of prostate cancer. BMC Bioinforma. 2012;13:282.

    Article  Google Scholar 

  48. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521:436–44.

    Article  CAS  PubMed  Google Scholar 

  49. Angermueller C, Pärnamaa T, Parts L, Stegle O. Deep learning for computational biology. Mol Syst Biol. 2016;12:878.

    Article  PubMed  PubMed Central  Google Scholar 

  50. El Naqa I, Boone JM, Benedict SH, Goodsitt MM, Chan H-P, Drukker K, et al. AI in medical physics: guidelines for publication. Vol. 48, Medical physics. United States; 2021. p. 4711–4.

  51. Ruamviboonsuk P, Chantra S, Seresirikachorn K, Ruamviboonsuk V, Sangroongruangsri S. Economic Evaluations of Artificial Intelligence in Ophthalmology. Asia-Pac J Ophthalmol [Internet]. 2021;10:307–16. https://journals.lww.com/10.1097/APO.0000000000000403

    Article  Google Scholar 

  52. Mori Y, Kudo S, East JE, Rastogi A, Bretthauer M, Misawa M, et al. Cost savings in colonoscopy with artificial intelligence-aided polyp diagnosis: An add-on analysis of a clinical trial (with video). Gastrointest Endosc [Internet]. 2020;92:905–911.e1. https://linkinghub.elsevier.com/retrieve/pii/S0016510720340347

    Article  PubMed  Google Scholar 

  53. Mayo RC, Leung JWT. Impact of Artificial Intelligence on Women’s Imaging: Cost-Benefit Analysis. Am J Roentgenol [Internet]. 2019;212:1172–3. https://www.ajronline.org/doi/10.2214/AJR.18.20419

    Article  Google Scholar 

  54. Salcedo J, Rosales M, Kim JS, Nuno D, Suen S-C, Chang AH. Cost-effectiveness of artificial intelligence monitoring for active tuberculosis treatment: A modeling study. Durand-Zaleski I, editor. PLoS One [Internet]. 2021 Jul;16:e0254950. Available from: https://dx.plos.org/10.1371/journal.pone.0254950

  55. Schwendicke F, Rossi JG, Göstemeyer G, Elhennawy K, Cantu AG, Gaudin R, et al. Cost-effectiveness of Artificial Intelligence for Proximal Caries Detection. J Dent Res [Internet]. 2021;100:369–76. http://journals.sagepub.com/doi/10.1177/0022034520972335

    Article  CAS  PubMed  Google Scholar 

  56. Loeb S, Bjurlin MA, Nicholson J, Tammela TL, Penson DF, Carter HB, et al. Overdiagnosis and Overtreatment of Prostate Cancer. Eur Urol [Internet]. 2014;65:1046–55. https://linkinghub.elsevier.com/retrieve/pii/S0302283813014905

    Article  PubMed  Google Scholar 

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Author information

Authors and Affiliations

  1. Institute for Urology and Reproductive Health, Sechenov University, Moscow, Russia

    Andrey Morozov, Mark Taratkin, Anastasia Shpikina & Dmitry Enikeev

  2. Institute for Clinical Medicine, Sechenov University, Moscow, Russia

    Andrey Bazarkin

  3. Department of Urology, Clinico San Carlos University Hospital, Madrid, Spain

    Juan Gomez Rivas

  4. Urology Department, University of Modena and Reggio Emilia, Modena, Italy

    Stefano Puliatti

  5. Department of Surgery, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Turin, Italy

    Enrico Checcucci

  6. Department of Uro-Nephrology. Virgen del Rocío University Hospital. Seville, “Seville Biomedicine Institute, IBiS/ Virgen del Rocío University Hospital /CSIC/Seville University. Seville”, Seville, Spain

    Ines Rivero Belenchon

  7. Department of Urology, University Medical Center Mannheim, Heidelberg University, Heidelberg, Germany

    Karl-Friedrich Kowalewski

  8. Department of Urology, James Buchanan Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, USA

    Nirmish Singla

  9. Department of Surgery, S.H. Ho Urology Centre, The Chinese University of Hong Kong, Hong Kong, China

    Jeremy Y. C. Teoh

  10. Department of Public Health and Healthcare, Sechenov University, Moscow, Russia

    Vasiliy Kozlov

  11. Department of Urology, Ludwig-Maximilian-University Munich, Munich, Germany

    Severin Rodler

  12. Division of Urology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy

    Pietro Piazza

  13. Karl Landsteiner Institute of Urology and Andrology, Vienna, Austria

    Harun Fajkovic & Dmitry Enikeev

  14. Department of Urology, Medical University of Vienna, Vienna, Austria

    Harun Fajkovic & Dmitry Enikeev

  15. Pathology department, Rabin Medical Center, Petach Tikwa, Israel

    Maxim Yakimov

  16. USC Institute of Urology and Catherine & Joseph Aresty Department of Urology, Keck School of Medicine, Los Angeles, CA, USA

    Andre Luis Abreu & Giovanni E. Cacciamani

  17. Artificial Intelligence Center at USC Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA

    Andre Luis Abreu & Giovanni E. Cacciamani

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  1. Andrey Morozov
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Consortia

Young Academic Urologists (YAU) Working Group in Uro-technology of the European Association of Urology

  • Mark Taratkin
  • , Juan Gomez Rivas
  • , Stefano Puliatti
  • , Enrico Checcucci
  • , Ines Rivero Belenchon
  • , Karl-Friedrich Kowalewski
  • , Severin Rodler
  • , Pietro Piazza
  •  & Giovanni E. Cacciamani

Contributions

AM: conception; interpretation; writing. MT: conception; writing. AB: interpretation; writing. JGR: conception; editing. SP: conception; editing. EC: conception; editing. IRB: conception; editing. K-FK: conception; editing. AS: conception; writing. NS: conception; editing. JYCT: conception; editing. VK: interpretation; statistical analysis. ALA: conception; editing. GEC: conception; editing. DE: conception; interpretation; writing.

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Correspondence to Dmitry Enikeev.

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Morozov, A., Taratkin, M., Bazarkin, A. et al. A systematic review and meta-analysis of artificial intelligence diagnostic accuracy in prostate cancer histology identification and grading. Prostate Cancer Prostatic Dis 26, 681–692 (2023). https://doi.org/10.1038/s41391-023-00673-3

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