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Artificial Intelligence in Prostate Cancer Diagnosis

Yıl 2024, Cilt: 19 Sayı: 3, 151 - 156, 30.10.2024
https://doi.org/10.33719/nju1557986

Öz

Prostate cancer (PCa) is a cancer with a broad spectrum of biological behavior and a heterogeneous nature. To prevent overdiagnosis and overtreatment, and to detect clinically significant PCa, standardized scoring and grading systems are used in imaging and pathological examinations. However, reproducibility and agreement between readers in these diagnostic stages, which require experience, are low. Promising results have been achieved by integrating artificial intelligence (AI)-based applications into the diagnosis and management of PCa. In radiological and pathological imaging, computer-aided diagnostic tools have increased clinical efficiency and achieved diagnostic accuracy comparable to that of experienced healthcare professionals. This review provides an overview of AI applications used in radiological imaging, prostate biopsy, and histopathological examination in the diagnosis of PCa.

Kaynakça

  • 1. Culp MB, Soerjomataram I, Efstathiou JA, Bray F, Jemal A. Recent Global Patterns in Prostate Cancer Incidence and Mortality Rates. Eur Urol. 2020;77(1):38-52. https:// doi.org/10.1016/j.eururo.2019.08.005
  • 2. Schoots IG, Padhani AR, Rouvière O, Barentsz JO, Richenberg J. Analysis of Magnetic Resonance Imagingdirected Biopsy Strategies for Changing the Paradigm of Prostate Cancer Diagnosis. Eur Urol Oncol. 2020;3(1):32- 41. https://doi.org/10.1016/j.euo.2019.10.001
  • 3. Epstein JI, Egevad L, Amin MB, Delahunt B, Srigley JR, Humphrey PA; Grading Committee. 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. 2016;40(2):244-52. https://doi.org/10.1097/ PAS.0000000000000530
  • 4. Barbieri CE, Bangma CH, Bjartell A, Catto JW, Culig Z, Grönberg H, Luo J, Visakorpi T, Rubin MA. The mutational landscape of prostate cancer. Eur Urol. 2013;64(4):567-76. https://doi.org/10.1016/j.eururo.2013.05.029
  • 5. Rosenkrantz AB, Ginocchio LA, Cornfeld D, Froemming AT, Gupta RT, Turkbey B, Westphalen AC, Babb JS, Margolis DJ. Interobserver Reproducibility of the PI-RADS Version 2 Lexicon: A Multicenter Study of Six Experienced Prostate Radiologists. Radiology. 2016;280(3):793-804. https://doi.org/10.1148/radiol.2016152542
  • 6. Egevad L, Ahmad AS, Algaba F, Berney DM, BocconGibod L, Compérat E, Evans AJ, Griffiths D, Grobholz R, Kristiansen G, Langner C, Lopez-Beltran A, Montironi R, Moss S, Oliveira P, Vainer B, Varma M, Camparo P. Standardization of Gleason grading among 337 European pathologists. Histopathology. 2013;62(2):247- 56. https://doi.org/10.1111/his.12008
  • 7. Goldenberg SL, Nir G, Salcudean SE. A new era: artificial intelligence and machine learning in prostate cancer. Nat Rev Urol. 2019;16(7):391-403. https://doi.org/10.1038/ s41585-019-0193-3
  • 8. Sherafatmandjoo H, Safaei AA, Ghaderi F, Allameh F. Prostate cancer diagnosis based on multi-parametric MRI, clinical and pathological factors using deep learning. Sci Rep. 2024 Jun 28;14(1):14951. https://doi. org/10.1038/s41598-024-65354-0
  • 9. Porpiglia F, Manfredi M, Mele F, Cossu M, Bollito E, Veltri A, Cirillo S, Regge D, Faletti R, Passera R, Fiori C, De Luca S. Diagnostic Pathway with Multiparametric Magnetic Resonance Imaging Versus Standard Pathway: Results from a Randomized Prospective Study in Biopsy-naïve Patients with Suspected Prostate Cancer. Eur Urol. 2017;72(2):282-288. https://doi.org/10.1016/j. eururo.2016.08.041
  • 10. Cacciamani GE, Sanford DI, Chu TN, Kaneko M, De Castro Abreu AL, Duddalwar V, Gill IS. Is Artificial Intelligence Replacing Our Radiology Stars? Not Yet! Eur Urol Open Sci. 2022 Dec 19;48:14-16. https://doi.org/10.1016/j.euros.2022.09.024
  • 11. Rosenkrantz AB, Ayoola A, Hoffman D, Khasgiwala A, Prabhu V, Smereka P, Somberg M, Taneja SS. The Learning Curve in Prostate MRI Interpretation: SelfDirected Learning Versus Continual Reader Feedback. AJR Am J Roentgenol. 2017;208(3):W92-W100. https:// doi.org/10.2214/AJR.16.16876
  • 12. Richenberg J, Løgager V, Panebianco V, Rouviere O, Villeirs G, Schoots IG. The primacy of multiparametric MRI in men with suspected prostate cancer. Eur Radiol. 2019;29(12):6940-6952. https://doi.org/10.1007/s00330-019-06166-z
  • 13. Smith CP, Harmon SA, Barrett T, Bittencourt LK, Law YM, Shebel H, An JY, Czarniecki M, Mehralivand S, Coskun M, Wood BJ, Pinto PA, Shih JH, Choyke PL, Turkbey B. Intra- and interreader reproducibility of PIRADSv2: A multireader study. J Magn Reson Imaging. 2019;49(6):1694-1703. https://doi.org/10.1002/jmri.26555
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  • 15. Mata LA, Retamero JA, Gupta RT, García Figueras R, Luna A. Artificial Intelligence-assisted Prostate Cancer Diagnosis: Radiologic-Pathologic Correlation. Radiographics. 2021;41(6):1676-1697. https://doi.org/10.1148/rg.2021210020
  • 16. Wang B, Lei Y, Tian S, Wang T, Liu Y, Patel P, Jani AB, Mao H, Curran WJ, Liu T, Yang X. Deeply supervised 3D fully convolutional networks with group dilated convolution for automatic MRI prostate segmentation. Med Phys. 2019;46(4):1707-1718. https://doi. org/10.1002/mp.13416
  • 17. Sanford TH, Zhang L, Harmon SA, Sackett J, Yang D, Roth H, Xu Z, Kesani D, Mehralivand S, Baroni RH, Barrett T, Girometti R, Oto A, Purysko AS, Xu S, Pinto PA, Xu D, Wood BJ, Choyke PL, Turkbey B. Data Augmentation and Transfer Learning to Improve Generalizability of an Automated Prostate Segmentation Model. AJR Am J Roentgenol. 2020;215(6):1403-1410. https://doi.org/10.2214/AJR.19.22347
  • 18. Gaur S, Lay N, Harmon SA, Doddakashi S, Mehralivand S, Argun B, Barrett T, Bednarova S, Girometti R, Karaarslan E, Kural AR, Oto A, Purysko AS, Antic T, Magi-Galluzzi C, Saglican Y, Sioletic S, Warren AY, Bittencourt L, Fütterer JJ, Gupta RT, Kabakus I, Law YM, Margolis DJ, Shebel H, Westphalen AC, Wood BJ, Pinto PA, Shih JH, Choyke PL, Summers RM, Turkbey B. Can computeraided diagnosis assist in the identification of prostate cancer on prostate MRI? a multi-center, multi-reader investigation. Oncotarget. 2018 Sep 18;9(73):33804- 33817. https://doi.org/10.18632/oncotarget.26100
  • 19. Mehrtash A, Sedghi A, Ghafoorian M, Taghipour M, Tempany CM, Wells WM 3rd, Kapur T, Mousavi P, Abolmaesumi P, Fedorov A. Classification of Clinical Significance of MRI Prostate Findings Using 3D Convolutional Neural Networks. Proc SPIE Int Soc Opt Eng. 2017 Feb 11;10134:101342A. https://doi.org/10.1117/12.2277123
  • 20. Cao R, Mohammadian Bajgiran A, Afshari Mirak S, Shakeri S, Zhong X, Enzmann D, Raman S, Sung K. Joint Prostate Cancer Detection and Gleason Score Prediction in mp-MRI via FocalNet. IEEE Trans Med Imaging. 2019;38(11):2496-2506. https://doi.org/10.1109/ TMI.2019.2901928
  • 21. Le MH, Chen J, Wang L, Wang Z, Liu W, Cheng KT, Yang X. Automated diagnosis of prostate cancer in multiparametric MRI based on multimodal convolutional neural networks. Phys Med Biol. 2017 Jul 24;62(16):6497- 6514. https://doi.org/10.1088/1361-6560/aa7731
  • 22. Giannini V, Mazzetti S, Defeudis A, Stranieri G, Calandri M, Bollito E, Bosco M, Porpiglia F, Manfredi M, De Pascale A, Veltri A, Russo F, Regge D. A Fully Automatic Artificial Intelligence System Able to Detect and Characterize Prostate Cancer Using Multiparametric MRI: Multicenter and Multi-Scanner Validation. Front Oncol. 2021 Oct 1;11:718155. https://doi.org/10.3389/ fonc.2021.718155
  • 23. Mehralivand S, Harmon SA, Shih JH, Smith CP, Lay N, Argun B, Bednarova S, Baroni RH, Canda AE, Ercan K, Girometti R, Karaarslan E, Kural AR, Purysko AS, RaisBahrami S, Tonso VM, Magi-Galluzzi C, Gordetsky JB, Macarenco RSES, Merino MJ, Gumuskaya B, Saglican Y, Sioletic S, Warren AY, Barrett T, Bittencourt L, Coskun M, Knauss C, Law YM, Malayeri AA, Margolis DJ, Marko J, Yakar D, Wood BJ, Pinto PA, Choyke PL, Summers RM, Turkbey B. Multicenter Multireader Evaluation of an Artificial Intelligence-Based Attention Mapping System for the Detection of Prostate Cancer With Multiparametric MRI. AJR Am J Roentgenol. 2020;215(4):903-912. https://doi.org/10.2214/ AJR.19.22573
  • 24. Sun Z, Wang K, Wu C, Chen Y, Kong Z, She L, Song B, Luo N, Wu P, Wang X, Zhang X, Wang X. Using an artificial intelligence model to detect and localize visible clinically significant prostate cancer in prostate magnetic resonance imaging: a multicenter external validation study. Quant Imaging Med Surg. 2024 Jan 3;14(1):43-60. https://doi.org/10.21037/qims-23-791
  • 25. Mottet N, van den Bergh RCN, Briers E, Van den BroeckT, Cumberbatch MG, De Santis M, Fanti S, Fossati N, Gandaglia G, Gillessen S, Grivas N, Grummet J, Henry AM, van der Kwast TH, Lam TB, Lardas M, Liew M, Mason MD, Moris L, Oprea-Lager DE, van der Poel HG, Rouvière O, Schoots IG, Tilki D, Wiegel T, Willemse PM, Cornford P. EAU-EANM-ESTRO-ESUR-SIOG Guidelines on Prostate Cancer-2020 Update. Part 1: Screening, Diagnosis, and Local Treatment with Curative Intent. Eur Urol. 2021;79(2):243-262. https://doi.org/10.1016/j.eururo.2020.09.042
  • 26. van Sloun RJG, Wildeboer RR, Mannaerts CK, Postema AW, Gayet M, Beerlage HP, Salomon G, Wijkstra H, Mischi M. Deep Learning for Real-time, Automatic, and Scanner-adapted Prostate (Zone) Segmentation of Transrectal Ultrasound, for Example, Magnetic Resonance Imaging-transrectal Ultrasound Fusion Prostate Biopsy. Eur Urol Focus. 2021;7(1):78-85. https:// doi.org/10.1016/j.euf.2019.04.009
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Prostat Kanseri Tanısında Yapay Zeka

Yıl 2024, Cilt: 19 Sayı: 3, 151 - 156, 30.10.2024
https://doi.org/10.33719/nju1557986

Öz

Prostat kanseri (PK), geniş bir biyolojik davranış spektrumuna ve heterojen bir yapıya sahip bir kanserdir. Aşırı tanı ve tedaviden kaçınmak ve klinik olarak anlamlı PK'ni tespit etmek amacıyla görüntüleme ve patolojik incelemelerde standardize edilmiş puanlama ve derecelendirme sistemleri kullanılmaktadır. Ancak, deneyim gerektiren bu tanısal aşamalarda okuyucular arasındaki tekrarlanabilirlik ve uyum düşüktür. Yapay zeka (YZ) tabanlı uygulamaların PK'nin tanı ve yönetimine entegre edilmesiyle umut verici sonuçlar elde edilmiştir. Radyolojik ve patolojik görüntülemede, bilgisayar destekli tanı araçları klinik verimliliği artırmış ve deneyimli sağlık profesyonellerine benzer tanısal doğruluk sağlamıştır. Bu inceleme, PK'nin tanısında kullanılan radyolojik görüntüleme, prostat biyopsisi ve histopatolojik incelemede YZ uygulamalarına genel bir bakış sunmaktadır.

Kaynakça

  • 1. Culp MB, Soerjomataram I, Efstathiou JA, Bray F, Jemal A. Recent Global Patterns in Prostate Cancer Incidence and Mortality Rates. Eur Urol. 2020;77(1):38-52. https:// doi.org/10.1016/j.eururo.2019.08.005
  • 2. Schoots IG, Padhani AR, Rouvière O, Barentsz JO, Richenberg J. Analysis of Magnetic Resonance Imagingdirected Biopsy Strategies for Changing the Paradigm of Prostate Cancer Diagnosis. Eur Urol Oncol. 2020;3(1):32- 41. https://doi.org/10.1016/j.euo.2019.10.001
  • 3. Epstein JI, Egevad L, Amin MB, Delahunt B, Srigley JR, Humphrey PA; Grading Committee. 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. 2016;40(2):244-52. https://doi.org/10.1097/ PAS.0000000000000530
  • 4. Barbieri CE, Bangma CH, Bjartell A, Catto JW, Culig Z, Grönberg H, Luo J, Visakorpi T, Rubin MA. The mutational landscape of prostate cancer. Eur Urol. 2013;64(4):567-76. https://doi.org/10.1016/j.eururo.2013.05.029
  • 5. Rosenkrantz AB, Ginocchio LA, Cornfeld D, Froemming AT, Gupta RT, Turkbey B, Westphalen AC, Babb JS, Margolis DJ. Interobserver Reproducibility of the PI-RADS Version 2 Lexicon: A Multicenter Study of Six Experienced Prostate Radiologists. Radiology. 2016;280(3):793-804. https://doi.org/10.1148/radiol.2016152542
  • 6. Egevad L, Ahmad AS, Algaba F, Berney DM, BocconGibod L, Compérat E, Evans AJ, Griffiths D, Grobholz R, Kristiansen G, Langner C, Lopez-Beltran A, Montironi R, Moss S, Oliveira P, Vainer B, Varma M, Camparo P. Standardization of Gleason grading among 337 European pathologists. Histopathology. 2013;62(2):247- 56. https://doi.org/10.1111/his.12008
  • 7. Goldenberg SL, Nir G, Salcudean SE. A new era: artificial intelligence and machine learning in prostate cancer. Nat Rev Urol. 2019;16(7):391-403. https://doi.org/10.1038/ s41585-019-0193-3
  • 8. Sherafatmandjoo H, Safaei AA, Ghaderi F, Allameh F. Prostate cancer diagnosis based on multi-parametric MRI, clinical and pathological factors using deep learning. Sci Rep. 2024 Jun 28;14(1):14951. https://doi. org/10.1038/s41598-024-65354-0
  • 9. Porpiglia F, Manfredi M, Mele F, Cossu M, Bollito E, Veltri A, Cirillo S, Regge D, Faletti R, Passera R, Fiori C, De Luca S. Diagnostic Pathway with Multiparametric Magnetic Resonance Imaging Versus Standard Pathway: Results from a Randomized Prospective Study in Biopsy-naïve Patients with Suspected Prostate Cancer. Eur Urol. 2017;72(2):282-288. https://doi.org/10.1016/j. eururo.2016.08.041
  • 10. Cacciamani GE, Sanford DI, Chu TN, Kaneko M, De Castro Abreu AL, Duddalwar V, Gill IS. Is Artificial Intelligence Replacing Our Radiology Stars? Not Yet! Eur Urol Open Sci. 2022 Dec 19;48:14-16. https://doi.org/10.1016/j.euros.2022.09.024
  • 11. Rosenkrantz AB, Ayoola A, Hoffman D, Khasgiwala A, Prabhu V, Smereka P, Somberg M, Taneja SS. The Learning Curve in Prostate MRI Interpretation: SelfDirected Learning Versus Continual Reader Feedback. AJR Am J Roentgenol. 2017;208(3):W92-W100. https:// doi.org/10.2214/AJR.16.16876
  • 12. Richenberg J, Løgager V, Panebianco V, Rouviere O, Villeirs G, Schoots IG. The primacy of multiparametric MRI in men with suspected prostate cancer. Eur Radiol. 2019;29(12):6940-6952. https://doi.org/10.1007/s00330-019-06166-z
  • 13. Smith CP, Harmon SA, Barrett T, Bittencourt LK, Law YM, Shebel H, An JY, Czarniecki M, Mehralivand S, Coskun M, Wood BJ, Pinto PA, Shih JH, Choyke PL, Turkbey B. Intra- and interreader reproducibility of PIRADSv2: A multireader study. J Magn Reson Imaging. 2019;49(6):1694-1703. https://doi.org/10.1002/jmri.26555
  • 14. Sugano D, Sanford D, Abreu A, Duddalwar V, Gill I, Cacciamani GE. Impact of radiomics on prostate cancer detection: a systematic review of clinical applications. Curr Opin Urol. 2020;30(6):754-781. https://doi.org/10.1097/MOU.0000000000000822
  • 15. Mata LA, Retamero JA, Gupta RT, García Figueras R, Luna A. Artificial Intelligence-assisted Prostate Cancer Diagnosis: Radiologic-Pathologic Correlation. Radiographics. 2021;41(6):1676-1697. https://doi.org/10.1148/rg.2021210020
  • 16. Wang B, Lei Y, Tian S, Wang T, Liu Y, Patel P, Jani AB, Mao H, Curran WJ, Liu T, Yang X. Deeply supervised 3D fully convolutional networks with group dilated convolution for automatic MRI prostate segmentation. Med Phys. 2019;46(4):1707-1718. https://doi. org/10.1002/mp.13416
  • 17. Sanford TH, Zhang L, Harmon SA, Sackett J, Yang D, Roth H, Xu Z, Kesani D, Mehralivand S, Baroni RH, Barrett T, Girometti R, Oto A, Purysko AS, Xu S, Pinto PA, Xu D, Wood BJ, Choyke PL, Turkbey B. Data Augmentation and Transfer Learning to Improve Generalizability of an Automated Prostate Segmentation Model. AJR Am J Roentgenol. 2020;215(6):1403-1410. https://doi.org/10.2214/AJR.19.22347
  • 18. Gaur S, Lay N, Harmon SA, Doddakashi S, Mehralivand S, Argun B, Barrett T, Bednarova S, Girometti R, Karaarslan E, Kural AR, Oto A, Purysko AS, Antic T, Magi-Galluzzi C, Saglican Y, Sioletic S, Warren AY, Bittencourt L, Fütterer JJ, Gupta RT, Kabakus I, Law YM, Margolis DJ, Shebel H, Westphalen AC, Wood BJ, Pinto PA, Shih JH, Choyke PL, Summers RM, Turkbey B. Can computeraided diagnosis assist in the identification of prostate cancer on prostate MRI? a multi-center, multi-reader investigation. Oncotarget. 2018 Sep 18;9(73):33804- 33817. https://doi.org/10.18632/oncotarget.26100
  • 19. Mehrtash A, Sedghi A, Ghafoorian M, Taghipour M, Tempany CM, Wells WM 3rd, Kapur T, Mousavi P, Abolmaesumi P, Fedorov A. Classification of Clinical Significance of MRI Prostate Findings Using 3D Convolutional Neural Networks. Proc SPIE Int Soc Opt Eng. 2017 Feb 11;10134:101342A. https://doi.org/10.1117/12.2277123
  • 20. Cao R, Mohammadian Bajgiran A, Afshari Mirak S, Shakeri S, Zhong X, Enzmann D, Raman S, Sung K. Joint Prostate Cancer Detection and Gleason Score Prediction in mp-MRI via FocalNet. IEEE Trans Med Imaging. 2019;38(11):2496-2506. https://doi.org/10.1109/ TMI.2019.2901928
  • 21. Le MH, Chen J, Wang L, Wang Z, Liu W, Cheng KT, Yang X. Automated diagnosis of prostate cancer in multiparametric MRI based on multimodal convolutional neural networks. Phys Med Biol. 2017 Jul 24;62(16):6497- 6514. https://doi.org/10.1088/1361-6560/aa7731
  • 22. Giannini V, Mazzetti S, Defeudis A, Stranieri G, Calandri M, Bollito E, Bosco M, Porpiglia F, Manfredi M, De Pascale A, Veltri A, Russo F, Regge D. A Fully Automatic Artificial Intelligence System Able to Detect and Characterize Prostate Cancer Using Multiparametric MRI: Multicenter and Multi-Scanner Validation. Front Oncol. 2021 Oct 1;11:718155. https://doi.org/10.3389/ fonc.2021.718155
  • 23. Mehralivand S, Harmon SA, Shih JH, Smith CP, Lay N, Argun B, Bednarova S, Baroni RH, Canda AE, Ercan K, Girometti R, Karaarslan E, Kural AR, Purysko AS, RaisBahrami S, Tonso VM, Magi-Galluzzi C, Gordetsky JB, Macarenco RSES, Merino MJ, Gumuskaya B, Saglican Y, Sioletic S, Warren AY, Barrett T, Bittencourt L, Coskun M, Knauss C, Law YM, Malayeri AA, Margolis DJ, Marko J, Yakar D, Wood BJ, Pinto PA, Choyke PL, Summers RM, Turkbey B. Multicenter Multireader Evaluation of an Artificial Intelligence-Based Attention Mapping System for the Detection of Prostate Cancer With Multiparametric MRI. AJR Am J Roentgenol. 2020;215(4):903-912. https://doi.org/10.2214/ AJR.19.22573
  • 24. Sun Z, Wang K, Wu C, Chen Y, Kong Z, She L, Song B, Luo N, Wu P, Wang X, Zhang X, Wang X. Using an artificial intelligence model to detect and localize visible clinically significant prostate cancer in prostate magnetic resonance imaging: a multicenter external validation study. Quant Imaging Med Surg. 2024 Jan 3;14(1):43-60. https://doi.org/10.21037/qims-23-791
  • 25. Mottet N, van den Bergh RCN, Briers E, Van den BroeckT, Cumberbatch MG, De Santis M, Fanti S, Fossati N, Gandaglia G, Gillessen S, Grivas N, Grummet J, Henry AM, van der Kwast TH, Lam TB, Lardas M, Liew M, Mason MD, Moris L, Oprea-Lager DE, van der Poel HG, Rouvière O, Schoots IG, Tilki D, Wiegel T, Willemse PM, Cornford P. EAU-EANM-ESTRO-ESUR-SIOG Guidelines on Prostate Cancer-2020 Update. Part 1: Screening, Diagnosis, and Local Treatment with Curative Intent. Eur Urol. 2021;79(2):243-262. https://doi.org/10.1016/j.eururo.2020.09.042
  • 26. van Sloun RJG, Wildeboer RR, Mannaerts CK, Postema AW, Gayet M, Beerlage HP, Salomon G, Wijkstra H, Mischi M. Deep Learning for Real-time, Automatic, and Scanner-adapted Prostate (Zone) Segmentation of Transrectal Ultrasound, for Example, Magnetic Resonance Imaging-transrectal Ultrasound Fusion Prostate Biopsy. Eur Urol Focus. 2021;7(1):78-85. https:// doi.org/10.1016/j.euf.2019.04.009
  • 27. Mehrtash A, Ghafoorian M, Pernelle G, Ziaei A, Heslinga FG, Tuncali K, Fedorov A, Kikinis R, Tempany CM, Wells WM, Abolmaesumi P, Kapur T. Automatic Needle Segmentation and Localization in MRI With 3-D Convolutional Neural Networks: Application to MRITargeted Prostate Biopsy. IEEE Trans Med Imaging. 2019;38(4):1026-1036. https://doi.org/10.1109/ TMI.2018.2876796
  • 28. Wang X, Xie Y, Zheng X, Liu B, Chen H, Li J, Ma X, Xiang J, Weng G, Zhu W, Wang G, Fang Y, Cheng H, Xie L. A prospective multi-center randomized comparative trial evaluating outcomes of transrectal ultrasound (TRUS)- guided 12-core systematic biopsy, mpMRI-targeted 12-core biopsy, and artificial intelligence ultrasound of prostate (AIUSP) 6-core targeted biopsy for prostate cancer diagnosis. World J Urol. 2023;41(3):653-662. https://doi.org/10.1007/s00345-022-04086-0
  • 29. Ling JQ, Mao J. [State of the art and perspective of pulp regeneration]. Zhonghua Kou Qiang Yi Xue Za Zhi. 2018 Jun 9;53(6):361-366. Chinese. https://doi.org/10.3760/ cma.j.issn.1002-0098.2018.06.001
  • 30. Checcucci E, Piana A, Volpi G, Piazzolla P, Amparore D, De Cillis S, Piramide F, Gatti C, Stura I, Bollito E, Massa F, Di Dio M, Fiori C, Porpiglia F. Three-dimensional automatic artificial intelligence driven augmentedreality selective biopsy during nerve-sparing robotassisted radical prostatectomy: A feasibility and accuracy study. Asian J Urol. 2023;10(4):407-415. https://doi. org/10.1016/j.ajur.2023.08.001
  • 31. Allsbrook WC Jr, Mangold KA, Johnson MH, Lane RB, Lane CG, Epstein JI. Interobserver reproducibility of Gleason grading of prostatic carcinoma: general pathologist. Hum Pathol. 2001;32(1):81-8. https://doi. org/10.1053/hupa.2001.21135
  • 32. Retamero JA, Aneiros-Fernandez J, Del Moral RG. Complete Digital Pathology for Routine Histopathology Diagnosis in a Multicenter Hospital Network. Arch Pathol Lab Med. 2020;144(2):221-228. https://doi. org/10.5858/arpa.2018-0541-OA
  • 33. Arvaniti E, Fricker KS, Moret M, Rupp N, Hermanns T, Fankhauser C, Wey N, Wild PJ, Rüschoff JH, Claassen M. Automated Gleason grading of prostate cancer tissue microarrays via deep learning. Sci Rep. 2018 Aug 13;8(1):12054. https://doi.org/10.1038/s41598-018- 30535-1
  • 34. Nagpal K, Foote D, Liu Y, Chen PC, Wulczyn E, Tan F, Olson N, Smith JL, Mohtashamian A, Wren JH, Corrado GS, MacDonald R, Peng LH, Amin MB, Evans AJ, Sangoi AR, Mermel CH, Hipp JD, Stumpe MC. Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer. NPJ Digit Med. 2019 Jun 7;2:48. https://doi.org/10.1038/s41746-019-0112-2
  • 35. Bulten W, Pinckaers H, van Boven H, Vink R, de Bel T, van Ginneken B, van der Laak J, Hulsbergen-van de Kaa C, Litjens G. Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study. Lancet Oncol. 2020;21(2):233-241. https://doi.org/10.1016/S1470-2045(19)30739-9
  • 36. Shao Y, Bazargani R, Karimi D, Wang J, Fazli L, Goldenberg SL, Gleave ME, Black PC, Bashashati A, Salcudean S. Prostate Cancer Risk Stratification by Digital Histopathology and Deep Learning. JCO Clin Cancer Inform. 2024;8:e2300184. https://doi. org/10.1200/CCI.23.00184
  • 37. Santa-Rosario JC, Gustafson EA, Sanabria Bellassai DE, Gustafson PE, de Socarraz M. Validation and three years of clinical experience in using an artificial intelligence algorithm as a second read system for prostate cancer diagnosis-real-world experience. J Pathol Inform. 2024 Apr 30;15:100378. https://doi.org/10.1016/j.jpi.2024.100378
Toplam 37 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Üroloji
Bölüm Derlemeler
Yazarlar

Adem Alçın 0000-0002-5026-5168

Asıf Yıldırım 0000-0002-3386-971X

Yayımlanma Tarihi 30 Ekim 2024
Gönderilme Tarihi 29 Eylül 2024
Kabul Tarihi 9 Ekim 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 19 Sayı: 3

Kaynak Göster

Vancouver Alçın A, Yıldırım A. Artificial Intelligence in Prostate Cancer Diagnosis. New J Urol. 2024;19(3):151-6.