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Artificial intelligence as diagnostic aiding tool in cases of Prostate Imaging Reporting and Data System category 3: the results of retrospective multi-center cohort study

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Abdominal Radiology Aims and scope

Abstract

Purpose

To study the effect of artificial intelligence (AI) on the diagnostic performance of radiologists in interpreting prostate mpMRI images of the PI-RADS 3 category.

Methods

In this multicenter study, 16 radiologists were invited to interpret prostate mpMRI cases with and without AI. The study included a total of 87 cases initially diagnosed as PI-RADS 3 by radiologists without AI, with 28 cases being clinically significant cancers (csPCa) and 59 cases being non-csPCa. The study compared the diagnostic efficacy between readings without and with AI, the reading time, and confidence levels.

Results

AI changed the diagnosis in 65 out of 87 cases. Among the 59 non-csPCa cases, 41 were correctly downgraded to PI-RADS 1-2, and 9 were incorrectly upgraded to PI-RADS 4-5. For the 28 csPCa cases, 20 were correctly upgraded to PI-RADS 4-5, and 5 were incorrectly downgraded to PI-RADS 1-2. Radiologists assisted by AI achieved higher diagnostic specificity and accuracy than those without AI [0.695 vs 0.000 and 0.736 vs 0.322, both P < 0.001]. Sensitivity with AI was not significantly different from that without AI [0.821 vs 1.000, P = 1.000]. AI reduced reading time significantly compared to without AI (mean: 351 seconds, P < 0.001). The diagnostic confidence score with AI was significantly higher than that without AI (Cohen Kappa: -0.016).

Conclusion

With the help of AI, there was an improvement in the diagnostic accuracy of PI-RADS category 3 cases by radiologists. There is also an increase in diagnostic efficiency and diagnostic confidence.

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References

  1. Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A, Global cancer statistics, 2012, CA Cancer J Clin, 2015, 65(2):87-108. https://doi.org/10.3322/caac.21262.

    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, 2020, 77(1):38-52. https://doi.org/10.1016/j.eururo.2019.08.005

    Article  PubMed  Google Scholar 

  3. Boustani AM, Pucar D, Saperstein L, Molecular imaging of prostate cancer, Br J Radiol, 2018, 91(1084):20170736. https://doi.org/10.1259/bjr.20170736.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Fütterer JJ, Briganti A, De Visschere P, Emberton M, Giannarini G, Kirkham A, et al. Can Clinically Significant Prostate Cancer Be Detected with Multiparametric Magnetic Resonance Imaging? A Systematic Review of the Literature, Eur Urol, 2015, 68(6):1045-53. https://doi.org/10.1016/j.eururo.2015.01.013.

    Article  PubMed  Google Scholar 

  5. Klotz L, Chin J, Black PC, Finelli A, Anidjar M, Bladou F, et al. Comparison of Multiparametric Magnetic Resonance Imaging-Targeted Biopsy With Systematic Transrectal Ultrasonography Biopsy for Biopsy-Naive Men at Risk for Prostate Cancer: A Phase 3 Randomized Clinical Trial, JAMA Oncol, 2021, 1;7(4):534-542. https://doi.org/10.1001/jamaoncol.2020.7589.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Turkbey B, Rosenkrantz AB, Haider MA, Padhani AR, Villeirs G, Macura KJ, et al. Prostate Imaging Reporting and Data System Version 2.1: 2019 Update of Prostate Imaging Reporting and Data System Version 2, Eur Urol, 2019, 76(3):340-351. https://doi.org/10.1016/j.eururo.2019.02.033.

    Article  PubMed  Google Scholar 

  7. Hansen NL, Barrett T, Kesch C, Pepdjonovic L, Bonekamp D, O'Sullivan R, et al. Multicentre evaluation of magnetic resonance imaging supported transperineal prostate biopsy in biopsy-naïve men with suspicion of prostate cancer, BJU Int, 2018, 122(1):40-49. https://doi.org/10.1111/bju.14049.

    Article  PubMed  Google Scholar 

  8. Shin T, Smyth TB, Ukimura O, Ahmadi N, de Castro Abreu AL, Ohe C, et al. Diagnostic accuracy of a five-point Likert scoring system for magnetic resonance imaging (MRI) evaluated according to results of MRI/ultrasonography image-fusion targeted biopsy of the prostate, BJU Int, 2018, 121(1):77-83. https://doi.org/10.1111/bju.13972.

    Article  PubMed  Google Scholar 

  9. Liddell H, Jyoti R, Haxhimolla HZ, mp-MRI Prostate Characterised PIRADS 3 Lesions are Associated with a Low Risk of Clinically Significant Prostate Cancer - A Retrospective Review of 92 Biopsied PIRADS 3 Lesions, Curr Urol, 2015, 8(2):96-100. https://doi.org/10.1159/000365697.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Hermie I, Van Besien J, De Visschere P, Lumen N, Decaestecker K, Which clinical and radiological characteristics can predict clinically significant prostate cancer in PI-RADS 3 lesions? A retrospective study in a high-volume academic center, Eur J Radiol, 2019, 114:92-98. https://doi.org/10.1016/j.ejrad.2019.02.031.

    Article  PubMed  Google Scholar 

  11. Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL, Artificial intelligence in radiology, Nat Rev Cancer, 2018, 18(8):500-510. https://doi.org/10.1038/s41568-018-0016-5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Moawad AW, Fuentes DT, ElBanan MG, Shalaby AS, Guccione J, Kamel S, et al. Artificial Intelligence in Diagnostic Radiology: Where Do We Stand, Challenges, and Opportunities, J Comput Assist Tomogr, 2022, 01;46(1):78-90. https://doi.org/10.1097/RCT.0000000000001247.

    Article  PubMed  Google Scholar 

  13. 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.

    Article  PubMed  Google Scholar 

  14. Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks, Science, 2006, 28;313(5786):504-7. https://doi.org/10.1126/science.1127647.

    Article  CAS  PubMed  Google Scholar 

  15. Fehr D, Veeraraghavan H, Wibmer A, Gondo T, Matsumoto K, et al. Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images, Proc Natl Acad Sci U S A, 2015, 17;112(46): E6265-73. https://doi.org/10.1073/pnas.1505935112.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Fehr D, Veeraraghavan H, Wibmer A, Gondo T, Matsumoto K, Vargas HA, et al. Deep dense multi-path neural network for prostate segmentation in magnetic resonance imaging, Int J Comput Assist Radiol Surg, 2018, 13(11):1687-1696. https://doi.org/10.1073/pnas.1505935112.

    Article  CAS  Google Scholar 

  17. Bulten W, Kartasalo K, Chen PC, Ström P, Pinckaers H, et al; PANDA challenge consortium. Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge, Nat Med, 2022, 28(1):154-163. https://doi.org/10.1038/s41591-021-01620-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Suarez-Ibarrola R, Sigle A, Eklund M, Eberli D, Miernik A, et al. Artificial Intelligence in Magnetic Resonance Imaging-based Prostate Cancer Diagnosis: Where Do We Stand in 2021? Eur Urol Focus, 2022, 8(2):409-417. https://doi.org/10.1016/j.euf.2021.03.020.

    Article  PubMed  Google Scholar 

  19. Sun Z, Wu P, Cui Y, Liu X, Wang K, Gao G, et al. Deep-Learning Models for Detection and Localization of Visible Clinically Significant Prostate Cancer on Multi-Parametric MRI, J Magn Reson Imaging, 2023, 24. Epub ahead of print. https://doi.org/10.1002/jmri.28608.

  20. Sun Z, Wang K, Kong Z, Xing Z, Chen Y, et al. A multicenter study of artificial intelligence-aided software for detecting visible clinically significant prostate cancer on mpMRI, Insights Imaging, 2023, 30;14(1):72. https://doi.org/10.1186/s13244-023-01421-w.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Zhu L, Gao G, Liu Y, Han C, Liu J, Zhang X, et al. Feasibility of integrating computer-aided diagnosis with structured reports of prostate multiparametric MRI, Clin Imaging, 2020, 60(1):123-130. https://doi.org/10.1016/j.clinimag.2019.12.010.

    Article  PubMed  Google Scholar 

  22. Mottet N, van den Bergh RCN, Briers E, Van den Broeck T, Cumberbatch MG, De Santis M, et al. 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.

    Article  CAS  PubMed  Google Scholar 

  23. Mohler JL, Antonarakis ES, Armstrong AJ, D'Amico AV, Davis BJ, Dorff T, et al. Prostate Cancer, Version 2.2019, NCCN Clinical Practice Guidelines in Oncology, J Natl Compr Canc Netw. 2019, 17(5):479-505. https://doi.org/10.6004/jnccn.2019.0023.

    Article  CAS  PubMed  Google Scholar 

  24. Grivas N, Lardas M, Espinós EL, Lam TB, Rouviere O, Mottet N, et al; Members of the EAU-EANM-ESTRO-ESUR-ISUP-SIOG Prostate Cancer Guidelines Panel. Prostate Cancer Detection Percentages of Repeat Biopsy in Patients with Positive Multiparametric Magnetic Resonance Imaging (Prostate Imaging Reporting and Data System/Likert 3-5) and Negative Initial Biopsy. A Mini Systematic Review, Eur Urol, 2022, 82(5):452-457. https://doi.org/10.1016/j.eururo.2022.07.025.

  25. Santomartino SM, Yi PH, Systematic Review of Radiologist and Medical Student Attitudes on the Role and Impact of AI in Radiology, Acad Radiol, 2022, 29: S1076-6332(21)00624-3. https://doi.org/10.1016/j.acra.2021.12.032.

    Article  Google Scholar 

  26. Giannini V, Mazzetti S, Cappello G, Doronzio VM, Vassallo L, Russo F, et al. Computer-Aided Diagnosis Improves the Detection of Clinically Significant Prostate Cancer on Multiparametric-MRI: A Multi-Observer Performance Study Involving Inexperienced Readers, Diagnostics (Basel), 2021, 28;11(6): 973. https://doi.org/10.3390/diagnostics11060973.

    Article  PubMed  Google Scholar 

  27. Giannini V, Mazzetti S, Armando E, Carabalona S, Russo F, Giacobbe A, et al. Multiparametric magnetic resonance imaging of the prostate with computer-aided detection: experienced observer performance study, Eur Radiol, 2017, 27(10):4200-4208. https://doi.org/10.1007/s00330-017-4805-0.

    Article  PubMed  Google Scholar 

  28. Mehralivand S, Harmon SA, Shih JH, Smith CP, Lay N, Argun B, et al. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Hiremath A, Shiradkar R, Fu P, Mahran A, Rastinehad AR, Tewari A, et al. An integrated nomogram combining deep learning, Prostate Imaging-Reporting and Data System (PI-RADS) scoring, and clinical variables for identification of clinically significant prostate cancer on biparametric MRI: a retrospective multicentre study, Lancet Digit Health, 2021, 3(7):e445-e454. https://doi.org/10.1016/S2589-7500(21)00082-0.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Wadera A, Alabousi M, Pozdnyakov A, Kashif Al-Ghita M, Jafri A, McInnes MD, et al. Impact of PI-RADS Category 3 lesions on the diagnostic accuracy of MRI for detecting prostate cancer and the prevalence of prostate cancer within each PI-RADS category: A systematic review and meta-analysis, Br J Radiol, 2021, 1; 94(1118): 20191050. https://doi.org/10.1259/bjr.20191050.

    Article  PubMed  Google Scholar 

  31. Curran-Everett D, Milgrom H, Post-hoc data analysis: benefits and limitations, Curr Opin Allergy Clin Immunol, 2013 Jun;13(3):223-4. https://doi.org/10.1097/ACI.0b013e3283609831.

    Article  PubMed  Google Scholar 

  32. Yang S, Zhao W, Tan S, Zhang Y, Wei C, Chen T, et al. Combining clinical and MRI data to manage PI-RADS 3 lesions and reduce excessive biopsy, Transl Androl Urol, 2020, 9(3):1252-1261. https://doi.org/10.21037/tau-19-755.

    Article  PubMed  PubMed Central  Google Scholar 

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Author notes

  1. Kexin Wang and Zhangli Xing have contributed equally to this work.

Authors and Affiliations

  1. School of Basic Medical Sciences, Capital Medical University, Beijing, 100069, China

    Kexin Wang

  2. Department of Radiology, Fujian Medical University Union Hospital, No. 29, Xin Quan Road, Gulou District, Fuzhou, 350001, Fujian Province, China

    Zhangli Xing, Yang Yu & Yunjing Xue

  3. Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, No. 467, Zhongshan Road, Shahekou District, Dalian, 116023, Liaoning Province, China

    Zixuan Kong & Xiangpeng Zhao

  4. Department of Radiology, West China Hospital, Sichuan University, No. 37, Guoxue Lane, Wuhou District, Chengdu, 610044, Sichuan Province, China

    Yuntian Chen & Bin Song

  5. Beijing Smart Tree Medical Technology Co. Ltd., No. 97, Changping Road, Shahe Town, Changping District, Beijing, 102200, China

    Xiangpeng Wang & Pengsheng Wu

  6. Peking University First Hospital, No. 8, Xishku Road, Xicheng District, Beijing, 100034, China

    Xiaoying Wang

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  1. Kexin Wang
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Wang, K., Xing, Z., Kong, Z. et al. Artificial intelligence as diagnostic aiding tool in cases of Prostate Imaging Reporting and Data System category 3: the results of retrospective multi-center cohort study. Abdom Radiol 48, 3757–3765 (2023). https://doi.org/10.1007/s00261-023-03989-9

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