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Künstliche Intelligenz und Radiomics in der MRT-basierten Prostatadiagnostik

Artificial intelligence and radiomics in MRI-based prostate diagnostics

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Zusammenfassung

Klinisches/methodisches Problem

Angesichts der diagnostischen Komplexität und der großen Untersuchungszahlen ergibt sich die Herausforderung für die moderne Radiologie, klinisch signifikante Prostatakarzinome (PCa) mit hoher Sensitivität und Spezifität zu identifizieren sowie eine Überdiagnostik und Übertherapie von klinisch nicht signifikanten Karzinomen zu vermeiden.

Radiologische Standardverfahren

In den internationalen Leitlinien etabliert sich zunehmend die multiparametrische Magnetresonanztomographie (mpMRT) als Standarduntersuchung in der Diagnostik des PCa.

Methodische Innovationen

Die MRT-Beurteilung nach den PI-RADS-Kriterien durch Radiologen weist eine begrenzte Reproduzierbarkeit auf, sodass die rasanten Entwicklungen der automatisierten Bildanalyse mittels Radiomics oder künstlicher Intelligenz (KI; Machine Learning, Deep Learning) auf eine weitere Verbesserung der Patientenversorgung hoffen lassen.

Leistungsfähigkeit

Die Anwendung von KI konzentriert sich auf die automatisierte Detektion und Klassifikation von PCa-Läsionen und versucht, die Aggressivität anhand des Gleason-Scores zu stratifizieren. Neueste Studien präsentieren gute bis sehr gute Ergebnisse für die Befundung der mpMRT mittels Radiomics oder KI. Eine breite klinische Anwendung bleibt jedoch aktuell noch aus.

Bewertung und Empfehlung für die Praxis

Das wachsende Bewusstsein der Forschungsgesellschaft für eine strukturierte Datenakquise, die Entwicklung robuster AI-Systeme sowie die zunehmende Akzeptanz gegenüber KI-Algorithmen als diagnostisches Hilfsmittel sind Voraussetzungen für die klinische Anwendbarkeit dieser Technologien. Wenn die KI diese Hürden überwindet, kann ihr eine Schlüsselrolle in der quantitativen, reproduzierbaren Befundung der Prostata-MRT bei ständig ansteigenden Untersuchungsvolumina zukommen.

Abstract

Clinical/methodical issue

In view of the diagnostic complexity and the large number of examinations, modern radiology is challenged to identify clinically significant prostate cancer (PCa) with high sensitivity and specificity. Meanwhile overdiagnosis and overtreatment of clinically nonsignificant carcinomas need to be avoided.

Standard radiological methods

Increasingly, international guidelines recommend multiparametric magnetic resonance imaging (mpMRI) as first-line investigation in patients with suspected PCa.

Methodical innovations

Image interpretation according to the PI-RADS criteria is limited by interobserver variability. Thus, rapid developments in the field of automated image analysis tools, including radiomics and artificial intelligence (AI; machine learning, deep learning), give hope for further improvement in patient care.

Performance

AI focuses on the automated detection and classification of PCa, but it also attempts to stratify tumor aggressiveness according to the Gleason score. Recent studies present good to very good results in radiomics or AI-supported mpMRI diagnosis. Nevertheless, these systems are not widely used in clinical practice.

Achievements and practical recommendations

In order to apply these innovative technologies, a growing awareness for the need of structured data acquisition, development of robust systems and an increased acceptance of AI as diagnostic support are needed. If AI overcomes these obstacles, it may play a key role in the quantitative and reproducible image-based diagnosis of ever-increasing prostate MRI examination volumes.

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Literatur

  1. Grönberg H (2003) Prostate cancer epidemiology. Lancet 361:859–864

    Article  PubMed  Google Scholar 

  2. Wolters T et al (2011) A critical analysis of the tumor volume threshold for clinically insignificant prostate cancer using a data set of a randomized screening trial. J Urol 185:121–125

    Article  PubMed  Google Scholar 

  3. Bi WL et al (2019) Artificial intelligence in cancer imaging: clinical challenges and applications. CA Cancer J Clin 69:127–157

    PubMed  PubMed Central  Google Scholar 

  4. Costa DN, Pedrosa I, Donato F Jr, Roehrborn CG, Rofsky NM (2015) MR imaging-transrectal US fusion for targeted prostate biopsies: implications for diagnosis and clinical management. Radiographics 35:696–708

    Article  PubMed  Google Scholar 

  5. Cuocolo R et al (2019) Machine learning applications in prostate cancer magnetic resonance imaging. Eur Radiol Exp 3:35

    Article  PubMed  PubMed Central  Google Scholar 

  6. Liu L, Tian Z, Zhang Z, Fei B (2016) Computer-aided detection of prostate cancer with MRI: technology and applications. Acad Radiol 23:1024–1046

    Article  PubMed  PubMed Central  Google Scholar 

  7. Loeb S et al (2014) Overdiagnosis and overtreatment of prostate cancer. Eur Urol 65:1046–1055

    Article  PubMed  PubMed Central  Google Scholar 

  8. Turkbey B et al (2019) Prostate imaging reporting and data system version 2.1: 2019 update of prostate imaging reporting and data system version 2. Eur Urol 76:340–351

    Article  PubMed  Google Scholar 

  9. Rosenkrantz AB et al (2016) Interobserver reproducibility of the PI-RADS version 2 lexicon: a multicenter study of six experienced prostate radiologists. Radiology 280:793–804

    Article  PubMed  Google Scholar 

  10. Venderink W et al (2018) Results of targeted biopsy in men with magnetic resonance imaging lesions classified equivocal, likely or highly likely to be clinically significant prostate cancer. Eur Urol 73:353–360

    Article  PubMed  Google Scholar 

  11. Patel N, Henry A, Scarsbrook A (2018) The value of MR textural analysis in prostate cancer. Clin Radiol 74:876–885

    Article  PubMed  Google Scholar 

  12. Schwier M et al (2019) Repeatability of multiparametric prostate MRI radiomics features. Sci Rep 9:9441

    Article  PubMed  PubMed Central  Google Scholar 

  13. Toivonen J et al (2019) Radiomics and machine learning of multisequence multiparametric prostate MRI: Towards improved non-invasive prostate cancer characterization. PLoS ONE 14:e217702

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Algohary A et al (2018) Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: preliminary findings. J Magn Reson Imaging 48:818–828

    Article  Google Scholar 

  15. Vignati A et al (2015) Texture features on T2-weighted magnetic resonance imaging: new potential biomarkers for prostate cancer aggressiveness. Phys Med Biol 60:2685

    Article  CAS  PubMed  Google Scholar 

  16. Ginsburg SB et al (2017) Radiomic features for prostate cancer detection on MRI differ between the transition and peripheral zones: preliminary findings from a multi-institutional study. J Magn Reson Imaging 46:184–193

    Article  PubMed  Google Scholar 

  17. Wang J et al (2017) Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer. Eur Radiol 27:4082–4090

    Article  PubMed  Google Scholar 

  18. Varghese B et al (2019) Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images. Sci Rep 9:1570

    Article  PubMed  PubMed Central  Google Scholar 

  19. Bonekamp D et al (2018) Radiomic machine learning for characterization of prostate lesions with MRI: comparison to ADC values. Radiology 289:128–137

    Article  PubMed  Google Scholar 

  20. Litjens G, Debats O, Barentsz J, Karssemeijer N, Huisman H (2014) Computer-aided detection of prostate cancer in MRI. IEEE Trans Med Imaging 33:1083–1092

    Article  PubMed  Google Scholar 

  21. Kwak JT et al (2015) Automated prostate cancer detection using T2-weighted and high-b-value diffusion-weighted magnetic resonance imaging. Med Phys 42:2368–2378

    Article  PubMed  PubMed Central  Google Scholar 

  22. Peng Y et al (2013) Quantitative analysis of multiparametric prostate MR images: differentiation between prostate cancer and normal tissue and correlation with Gleason score—a computer-aided diagnosis development study. Radiology 267:787–796

    Article  PubMed  Google Scholar 

  23. Greer MD et al (2018) Computer-aided diagnosis prior to conventional interpretation of prostate mpMRI: an international multi-reader study. Eur Radiol 28:4407–4417

    Article  PubMed  PubMed Central  Google Scholar 

  24. Hamm CA et al (2019) Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI. Eur Radiol 29:3338–3347

    Article  PubMed  PubMed Central  Google Scholar 

  25. Esteva A et al (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542:115–118. https://doi.org/10.1038/nature21056

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Ishioka J et al (2018) Computer-aided diagnosis of prostate cancer on magnetic resonance imaging using a convolutional neural network algorithm. BJU Int 122:411–417

    Article  PubMed  Google Scholar 

  27. Wang X et al (2017) Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning. Sci Rep 7:15415

    Article  PubMed  PubMed Central  Google Scholar 

  28. Schelb P et al (2019) Classification of cancer at prostate MRI: deep learning versus clinical PI-RADS assessment. Radiology. https://doi.org/10.1148/radiol.2019190938

    Article  PubMed  Google Scholar 

  29. Stamey TA, McNeal JE, Yemoto CM, Sigal BM, Johnstone IM (1999) Biological determinants of cancer progression in men with prostate cancer. JAMA 281:1395–1400

    Article  CAS  PubMed  Google Scholar 

  30. Aldoj N, Lukas S, Dewey M, Penzkofer T (2019) Semi-automatic classification of prostate cancer on multi-parametric MR imaging using a multi-channel 3D convolutional neural network. Eur Radiol. https://doi.org/10.1007/s00330-019-06417-z

    Article  PubMed  Google Scholar 

  31. Antonelli M et al (2019) Machine learning classifiers can predict Gleason pattern 4 prostate cancer with greater accuracy than experienced radiologists. Eur Radiol 29:4754–4764. https://doi.org/10.1007/s00330-019-06244-2

    Article  PubMed  PubMed Central  Google Scholar 

  32. Min X et al (2019) Multi-parametric MRI-based radiomics signature for discriminating between clinically significant and insignificant prostate cancer: Cross-validation of a machine learning method. Eur J Radiol 115:16–21

    Article  PubMed  Google Scholar 

  33. Karimi D et al (2019) Accurate and robust deep learning-based segmentation of the prostate clinical target volume in ultrasound images. Med Image Anal 57:186–196. https://doi.org/10.1016/j.media.2019.07.005

    Article  PubMed  Google Scholar 

  34. Nguyen D et al (2019) A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning. Sci Rep 9:1076

    Article  PubMed  PubMed Central  Google Scholar 

  35. Wang B et al (2019) Deeply supervised 3D fully convolutional networks with group dilated convolution for automatic MRI prostate segmentation. Med Phys 46:1707–1718

    Article  PubMed  Google Scholar 

  36. Lee SL et al (2019) Changes in ADC and T2-weighted MRI-derived radiomic features in patients treated with focal salvage HDR prostate brachytherapy for local recurrence after previous external-beam radiotherapy. Brachytherapy 18(5):567. https://doi.org/10.1016/j.brachy.2019.04.006

    Article  PubMed  Google Scholar 

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Authors and Affiliations

  1. Institute of Radiology, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität, and Berlin Institute of Health, Augustenburger Platz 1, 13353, Berlin, Deutschland

    Charlie Alexander Hamm,  Nick Lasse Beetz,  Lynn Jeanette Savic &  Tobias Penzkofer

  2. Berlin Institute of Health, 10178, Berlin, Deutschland

    Tobias Penzkofer

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  1. Charlie Alexander Hamm
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  2. Nick Lasse Beetz
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  4. Tobias Penzkofer
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Corresponding author

Correspondence to Tobias Penzkofer.

Ethics declarations

Interessenkonflikt

C.A. Hamm und N.L. Beetz geben an, dass kein Interessenkonflikt besteht. L.J. Savic erhielt Research Grants der Leopoldina, der Rolf W. Günther Stiftung für radiologische Wissenschaften, des Berlin Institute of Health (Clinician Scientist Program) und der Society of Interventional Oncology. T. Penzkofer erhielt Research Grants des Berlin Institute of Health (Clinician Scientist Program) und nimmt an Forschungsvorhaben mit den folgenden Firmen (ohne persönliche Zuwendungen) teil: AGO, Aprea AB, ARCAGY-GINECO, Astellas Pharma Global Inc (APGD), Astra Zeneca, Clovis Oncology, Inc., Dohme Corp, Holaira, Incyte Corporation, Karyopharm, Lion Biotechnologies, Inc., MedImmune, Merck Sharp, Millennium Pharmaceuticals, Inc., Morphotec Inc., NovoCure Ltd., PharmaMar S.A. and PharmaMar USA, Inc, Roche, Siemens Healthineers und TESARO Inc.

Für diesen Beitrag wurden von den Autoren keine Studien an Menschen oder Tieren durchgeführt. Für die aufgeführten Studien gelten die jeweils dort angegebenen ethischen Richtlinien.

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Hamm, C.A., Beetz, N.L., Savic, L.J. et al. Künstliche Intelligenz und Radiomics in der MRT-basierten Prostatadiagnostik. Radiologe 60, 48–55 (2020). https://doi.org/10.1007/s00117-019-00613-0

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  • DOI: https://doi.org/10.1007/s00117-019-00613-0

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