Ces radiol. 2023, 77(4):203-212 | DOI: 10.55095/CesRadiol2023/024

Detection of prostate cancer - comparison of findings evaluated by an experienced radiologist or artificial intelligence using deep learningReview article

Jiří Ferda1, Milan Hora2, Ondřej Hes3
1 Klinika zobrazovacích metod LF UK a FN, Plzeň
2 Urologická klinika LF UK a FN, Plzeň
3 Šiklův ústav patologické anatomie LF UK, Plzeň

Aim: To evaluate the experience with the evaluation of ProStay magneticresonance imaging using a commercially available deep-learning AI tool.

Methods: A group of 100 prostate examinations was evaluated, where thefindings were verified either by surgery or by long-term follow-up, theresults originally described by a radiologist in 2018 were compared, then in2023 the results were evaluated by the artificial intelligence tool ProstateAI (Siemens Healthineers, Erlangen, Germany).

Results: The AI achieved the following results: sensitivity 0.98, specificity0.70, negative predictive value 0.88, positive predictive value 0.93, andaccuracy 0.92 In contrast, the results of a radiologist with 12 years ofexperience in evaluating prostate magnetic resonance imaging were: sensitivity0.99, specificity 0.95, negative predictive value 0.95, positive predictivevalue 0.99, and accuracy 0.98.

Conclusion: The AI tool achieves reliable results, but compared to anexperienced radiolg, its results have lagged behind.

Keywords: prostatic carcinoma, magnetic resonance imaging, artificialintelligence, deep learning

Published: December 1, 2023  Show citation

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Ferda J, Hora M, Hes O. Detection of prostate cancer - comparison of findings evaluated by an experienced radiologist or artificial intelligence using deep learning. Ces radiol. 2023;77(4):203-212. doi: 10.55095/CesRadiol2023/024.
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    References

    1. 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 localizevisible clinically significant prostate cancer in prostate magnetic resonanceimaging: a multicenter external validation study. Quant Imaging Med Surg. 2024 Go to original source...
    2. (1): 43-60.
    3. Hegazi MAAA, Taverna G, Grizzi F. Is Artificial Intelligence the Key toRevolutionizing Benign Prostatic Hyperplasia Diagnosis and Management? Arch EspUrol.
    4. 76(9): 643-656.
    5. van den Berg I, Soeterik TFW, van der Hoeven EJRJ, Claassen B, Brink WM, BaasDJH, Sedelaar JPM, Heine L, Tol J, van der Voort van Zyp JRN, van den Berg CAT,van den Bergh RCN, van Basten JA, van Melick HHE. The Development and ExternalValidation of Artificial Intelligence-Driven MRI-Based Models to ImprovePrediction of Lesion-Specific Extraprostatic Extension in Patients with ProstateCancer. Cancers (Basel) 2023; 15(22): 5452. Go to original source... Go to PubMed...
    6. Kaneko M, Magoulianitis V, Ramacciotti LS, Raman A, Paralkar D, Chen A, ChuTN, Yang Y, Xue J, Yang J, Liu J, Jadvar DS, Gill K, Cacciamani GE, Nikias CL,Duddalwar V, Jay Kuo CC, Gill IS, Abreu AL. The Novel Green Learning ArtificialIntelligence for Prostate Cancer Imaging: A Balanced Alternative to Deep Learning and Radiomics. Urol Clin North Am. 2024; 51(1): 1-13. Go to original source... Go to PubMed...
    7. Belue MJ, Law YM, Marko J, Turkbey E, Malayeri A, Yilmaz EC, Lin Y, JohnsonL, Merriman KM, Lay NS, Wood BJ, Pinto PA, Choyke PL, Harmon SA, Turkbey B. DeepLearning-Based Interpretable AI for Prostate T2W MRI Quality Evaluation. Acad Radiol. 2023; S1076-6332(23)00509-3. doi: 10.1016/j.acra.2023.09.030. [Epub ahead of print]. PMID: 37858505. Go to original source... Go to PubMed...
    8. Lin Y, Belue MJ, Yilmaz EC, Harmon SA, An J, Law YM, Hazen L, Garcia C, Merriman KM, Phelps TE, Lay NS, Toubaji A, Merino MJ, Wood BJ, Gurram S, ChoykePL, Pinto PA, Turkbey B. Deep Learning-Based T2-weighted MR Image QualityAssessment and Its Impact on Prostate Cancer Detection Rates. J Magn ResonImaging 2023. doi: 10.1002/jmri.29031. [Epub ahead of print]. PMID:37811666. Go to original source... Go to PubMed...
    9. Bischoff LM, Peeters JM, Weinhold L, Krausewitz P, Ellinger J, Katemann C, Isaak A, Weber OM, Kuetting D, Attenberger U, Pieper CC, Sprinkart AM, Luetkens JA. Deep Learning Super-Resolution Reconstruction for Fast and Motion-RobustT2-weighted Prostate MRI. Radiology 2023; 308(3): e230427. doi:10.1148/radiol.230427. PMID: 37750774. Go to original source... Go to PubMed...
    10. Thomas M, Murali S, Simpson BSS, Freeman A, Kirkham A, Kelly D, Whitaker HC, Zhao Y, Emberton M, Norris JM. Use of artificial intelligence in the detection of primary prostate cancer in multiparametric MRI with its clinical outcomes: a protocol for a systematic review and meta-analysis. BMJ Open 2023; 13(8): e074009. doi: 10.1136/bmjopen-2023-074009. PMID: 37607794; PMCID:PMC10445392. Go to original source... Go to PubMed...
    11. Netzer N, Eith C, Bethge O, Hielscher T, Schwab C, Stenzinger A, Gnirs R, Schlemmer HP, Maier-Hein KH, Schimmöller L, Bonekamp D. Application of a validated prostate MRI deep learning system to independent same-vendor multi-institutional data: demonstration of transferability. Eur Radiol. 2023; 33(11): 7463-7476. doi: 10.1007/s00330-023-09882-9. [Epub 2023 Jul 28]. PMID:37507610. PMCID: PMC10598076. Go to original source... Go to PubMed...
    12. Kim H, Kang SW, Kim JH, Nagar H, Sabuncu M, Margolis DJA, Kim CK. The role of AI in prostate MRI quality and interpretation: Opportunities and challenges. Eur J Radiol. 2023; 165: 110887. doi: 10.1016/j.ejrad.2023.110887. [Epub 2023 May 23]. PMID: 37245342. Go to original source... Go to PubMed...
    13. Hamm CA, Baumgärtner GL, Biessmann F, Beetz NL, Hartenstein A, Savic LJ, Froböse K, Dräger F, Schallenberg S, Rudolph M, Baur ADJ, Hamm B, Haas M, Hofbauer S, Cash H, Penzkofer T. Interactive Explainable Deep Learning ModelInforms Prostate Cancer Diagnosis at MRI. Radiology 2023; 307(4): e222276. doi: 10.1148/radiol.222276. [Epub 2023 Apr 11]. PMID: 37039688. Go to original source... Go to PubMed...
    14. Oerther B, Engel H, Nedelcu A, Schlett CL, Grimm R, von Busch H, Sigle A, Gratzke C, Bamberg F, Benndorf M. Prediction of upgrade to clinically significant prostate cancer in patients under active surveillance: Performanceof a fully automated AI-algorithm for lesion detection and classification. Prostate 2023; 83(9): 871-878. doi: 10.1002/pros.24528. [Epub 2023 Apr 4].PMID: 36959777. Go to original source... Go to PubMed...

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