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July 2022
1. BEHESHTI M, Taimen P, Kemppainen J, Jambor I, et al
   Value of (68)Ga-labeled bombesin antagonist (RM2) in the detection of primary prostate cancer comparing with [(18)F]fluoromethylcholine PET-CT and multiparametric MRI-a phase I/II study.
   Eur Radiol. 2022 Jul 21. pii: 10.1007/s00330-022-08982.
   PubMed     Abstract available
   
   
   
2. HAMZAOUI D, Montagne S, Granger B, Allera A, et al
   Prostate volume prediction on MRI: tools, accuracy and variability.
   Eur Radiol. 2022;32:4931-4941.
   PubMed     Abstract available
   
   
   June 2022
3. SUSHENTSEV N, McLean MA, Warren AY, Brodie C, et al
   The potential of hyperpolarised (13)C-MRI to target glycolytic tumour core in prostate cancer.
   Eur Radiol. 2022 Jun 22. pii: 10.1007/s00330-022-08929.
   PubMed     Abstract available
   
   
   May 2022
4. TO MNN, Kwak JT
   Biparametric MR signal characteristics can predict histopathological measures of prostate cancer.
   Eur Radiol. 2022 May 4. pii: 10.1007/s00330-022-08808.
   PubMed     Abstract available
   
   
   April 2022
5. MA W, Mao J, Yang J, Wang T, et al
   Comparing the diagnostic performance of radiotracers in prostate cancer biochemical recurrence: a systematic review and meta-analysis.
   Eur Radiol. 2022 Apr 29. pii: 10.1007/s00330-022-08802.
   PubMed     Abstract available
   
   
   
6. BLEKER J, Kwee TC, Rouw D, Roest C, et al
   A deep learning masked segmentation alternative to manual segmentation in biparametric MRI prostate cancer radiomics.
   Eur Radiol. 2022 Apr 14. pii: 10.1007/s00330-022-08712.
   PubMed     Abstract available
   
   
   
7. PADHANI AR, Haider MA, Rouviere O
   Balancing the benefits and harms of MRI-directed biopsy pathways.
   Eur Radiol. 2022;32:2326-2329.
   PubMed     Abstract available
   
   
   
8. MICHALLEK F, Huisman H, Hamm B, Elezkurtaj S, et al
   Accuracy of fractal analysis and PI-RADS assessment of prostate magnetic resonance imaging for prediction of cancer grade groups: a clinical validation study.
   Eur Radiol. 2022;32:2372-2383.
   PubMed     Abstract available
   
   
   March 2022
9. GATTI M, Faletti R, Gentile F, Soncin E, et al
   mEPE-score: a comprehensive grading system for predicting pathologic extraprostatic extension of prostate cancer at multiparametric magnetic resonance imaging.
   Eur Radiol. 2022 Mar 15. pii: 10.1007/s00330-022-08595.
   PubMed     Abstract available
   
   
   
10. ZHENG H, Miao Q, Liu Y, Mirak SA, et al
    Multiparametric MRI-based radiomics model to predict pelvic lymph node invasion for patients with prostate cancer.
    Eur Radiol. 2022 Mar 3. pii: 10.1007/s00330-022-08625.
    PubMed     Abstract available
    
    
    February 2022
11. PENZKOFER T, Padhani AR, Turkbey B, Ahmed HU, et al
    Assessing the clinical performance of artificial intelligence software for prostate cancer detection on MRI.
    Eur Radiol. 2022 Feb 23. pii: 10.1007/s00330-022-08609.
    PubMed    
    
    
    
12. DE ROOIJ M, Barentsz JO
    PI-QUAL v.1: the first step towards good-quality prostate MRI.
    Eur Radiol. 2022;32:876-878.
    PubMed     Abstract available
    
    
    
13. GIGANTI F, Dinneen E, Kasivisvanathan V, Haider A, et al
    Inter-reader agreement of the PI-QUAL score for prostate MRI quality in the NeuroSAFE PROOF trial.
    Eur Radiol. 2022;32:879-889.
    PubMed     Abstract available
    
    
    January 2022
14. BITTENCOURT LK, Guricova K, Zucker I, Durieux JC, et al
    Risk-based MRI-directed diagnostic pathway outperforms non-risk-based pathways in suspected prostate cancer biopsy-naive men: a large cohort validation study.
    Eur Radiol. 2022 Jan 14. pii: 10.1007/s00330-021-08407.
    PubMed     Abstract available
    
    
    December 2021
15. MICHALLEK F, Huisman H, Hamm B, Elezkurtaj S, et al
    Prediction of prostate cancer grade using fractal analysis of perfusion MRI: retrospective proof-of-principle study.
    Eur Radiol. 2021 Dec 16. pii: 10.1007/s00330-021-08394.
    PubMed     Abstract available
    
    
    November 2021
16. HOSSEINZADEH M, Saha A, Brand P, Slootweg I, et al
    Deep learning-assisted prostate cancer detection on bi-parametric MRI: minimum training data size requirements and effect of prior knowledge.
    Eur Radiol. 2021 Nov 16. pii: 10.1007/s00330-021-08320.
    PubMed     Abstract available
    
    
    
17. BOSCHHEIDGEN M, Schimmoller L, Arsov C, Ziayee F, et al
    MRI grading for the prediction of prostate cancer aggressiveness.
    Eur Radiol. 2021 Nov 8. pii: 10.1007/s00330-021-08332.
    PubMed     Abstract available
    
    
    October 2021
18. CUOCOLO R, Stanzione A, Faletti R, Gatti M, et al
    MRI index lesion radiomics and machine learning for detection of extraprostatic extension of disease: a multicenter study.
    Eur Radiol. 2021;31:7575-7583.
    PubMed     Abstract available
    
    
    September 2021
19. HINZPETER R, Baumann L, Guggenberger R, Huellner M, et al
    Radiomics for detecting prostate cancer bone metastases invisible in CT: a proof-of-concept study.
    Eur Radiol. 2021 Sep 24. pii: 10.1007/s00330-021-08245.
    PubMed     Abstract available
    
    
    
20. REISCHAUER C, Cancelli T, Malekzadeh S, Froehlich JM, et al
    How to improve image quality of DWI of the prostate-enema or catheter preparation?
    Eur Radiol. 2021;31:6708-6716.
    PubMed     Abstract available
    
    
    August 2021
21. JENTJENS S, Mai C, Ahmadi Bidakhvidi N, De Coster L, et al
    Prospective comparison of simultaneous [(68)Ga]Ga-PSMA-11 PET/MR versus PET/CT in patients with biochemically recurrent prostate cancer.
    Eur Radiol. 2021 Aug 10. pii: 10.1007/s00330-021-08140.
    PubMed     Abstract available
    
    
    July 2021
22. SUSHENTSEV N, Rundo L, Blyuss O, Nazarenko T, et al
    Comparative performance of MRI-derived PRECISE scores and delta-radiomics models for the prediction of prostate cancer progression in patients on active surveillance.
    Eur Radiol. 2021 Jul 13. pii: 10.1007/s00330-021-08151.
    PubMed     Abstract available
    
    
    
23. BURA V, Caglic I, Snoj Z, Sushentsev N, et al
    MRI features of the normal prostatic peripheral zone: the relationship between age and signal heterogeneity on T2WI, DWI, and DCE sequences.
    Eur Radiol. 2021;31:4908-4917.
    PubMed     Abstract available
    
    
    May 2021
24. PENZKOFER T, Padhani AR, Turkbey B, Haider MA, et al
    ESUR/ESUI position paper: developing artificial intelligence for precision diagnosis of prostate cancer using magnetic resonance imaging.
    Eur Radiol. 2021 May 15. pii: 10.1007/s00330-021-08021.
    PubMed     Abstract available
    
    
    April 2021
25. WALLSTROM J, Geterud K, Kohestani K, Maier SE, et al
    Bi- or multiparametric MRI in a sequential screening program for prostate cancer with PSA followed by MRI? Results from the Goteborg prostate cancer screening 2 trial.
    Eur Radiol. 2021 Apr 23. pii: 10.1007/s00330-021-07907.
    PubMed     Abstract available
    
    
    
26. ALBERTS I, Hunermund JN, Sachpekidis C, Mingels C, et al
    The influence of digital PET/CT on diagnostic certainty and interrater reliability in [(68)Ga]Ga-PSMA-11 PET/CT for recurrent prostate cancer.
    Eur Radiol. 2021 Apr 15. pii: 10.1007/s00330-021-07870.
    PubMed     Abstract available
    
    
    March 2021
27. LIM CS, Abreu-Gomez J, Flood TA, Carrion I, et al
    Prevalence of prostate cancer in PI-RADS version 2.1 T2-weighted transition zone 'nodule in nodule' and 'homogeneous mildly hypointense area between nodules' criteria: MRI-radical prostatectomy histopathological evaluation.
    Eur Radiol. 2021 Mar 29. pii: 10.1007/s00330-021-07855.
    PubMed     Abstract available
    
    
    January 2021
28. ALONGI P, Stefano A, Comelli A, Laudicella R, et al
    Radiomics analysis of 18F-Choline PET/CT in the prediction of disease outcome in high-risk prostate cancer: an explorative study on machine learning feature classification in 94 patients.
    Eur Radiol. 2021 Jan 14. pii: 10.1007/s00330-020-07617.
    PubMed     Abstract available
    
    
    
29. MEHRALIVAND S
    Editorial comment on "Natural history of prostate cancer on active surveillance: stratification by MRI using the PRECISE recommendations in a UK cohort".
    Eur Radiol. 2021 Jan 8. pii: 10.1007/s00330-020-07589.
    PubMed    
    
    
    
30. PARK MY, Park KJ, Kim MH, Kim JK, et al
    Preoperative MRI-based estimation of risk for positive resection margin after radical prostatectomy in patients with prostate cancer: development and validation of a simple scoring system.
    Eur Radiol. 2021 Jan 2. pii: 10.1007/s00330-020-07569.
    PubMed     Abstract available
    
    
    
31. STEINBERG RL, Rasmussen RG, Johnson BA, Ghandour R, et al
    Prospective performance of clear cell likelihood scores (ccLS) in renal masses evaluated with multiparametric magnetic resonance imaging.
    Eur Radiol. 2021;31:314-324.
    PubMed     Abstract available
    
    
    
32. SCHELB P, Wang X, Radtke JP, Wiesenfarth M, et al
    Simulated clinical deployment of fully automatic deep learning for clinical prostate MRI assessment.
    Eur Radiol. 2021;31:302-313.
    PubMed     Abstract available
    
    
    December 2020
33. HUEBNER NA, Korn S, Resch I, Grubmuller B, et al
    Visibility of significant prostate cancer on multiparametric magnetic resonance imaging (MRI)-do we still need contrast media?
    Eur Radiol. 2020 Dec 2. pii: 10.1007/s00330-020-07494.
    PubMed     Abstract available
    
    
    November 2020
34. PADHANI AR, Schoots IG, Turkbey B, Giannarini G, et al
    A multifaceted approach to quality in the MRI-directed biopsy pathway for prostate cancer diagnosis.
    Eur Radiol. 2020 Nov 25. pii: 10.1007/s00330-020-07527.
    PubMed     Abstract available
    
    
    
35. DELLI PIZZI A, Mastrodicasa D, Marchioni M, Primiceri G, et al
    Bladder cancer: do we need contrast injection for MRI assessment of muscle invasion? A prospective multi-reader VI-RADS approach.
    Eur Radiol. 2020 Nov 19. pii: 10.1007/s00330-020-07473.
    PubMed     Abstract available
    
    
    
36. CAGLIC I, Sushentsev N, Gnanapragasam VJ, Sala E, et al
    MRI-derived PRECISE scores for predicting pathologically-confirmed radiological progression in prostate cancer patients on active surveillance.
    Eur Radiol. 2020 Nov 16. pii: 10.1007/s00330-020-07336.
    PubMed     Abstract available
    
    
    
37. OTTOSSON F, Baco E, Lauritzen PM, Rud E, et al
    The prevalence and locations of bone metastases using whole-body MRI in treatment-naive intermediate- and high-risk prostate cancer.
    Eur Radiol. 2020 Nov 3. pii: 10.1007/s00330-020-07363.
    PubMed     Abstract available
    
    
    
38. KAN Y, Zhang Q, Hao J, Wang W, et al
    Clinico-radiological characteristic-based machine learning in reducing unnecessary prostate biopsies of PI-RADS 3 lesions with dual validation.
    Eur Radiol. 2020;30:6274-6284.
    PubMed     Abstract available
    
    
    September 2020
39. GIGANTI F, Stabile A, Stavrinides V, Osinibi E, et al
    Natural history of prostate cancer on active surveillance: stratification by MRI using the PRECISE recommendations in a UK cohort.
    Eur Radiol. 2020 Sep 30. pii: 10.1007/s00330-020-07256.
    PubMed     Abstract available
    
    
    
40. SHIRADKAR R, Panda A, Leo P, Janowczyk A, et al
    Correction to: T1 and T2 MR fingerprinting measurements of prostate cancer and prostatitis correlate with deep learning-derived estimates of epithelium, lumen, and stromal composition on corresponding whole mount histopathology.
    Eur Radiol. 2020 Sep 18. pii: 10.1007/s00330-020-07285.
    PubMed     Abstract available
    
    
    
41. HU L, Zhou DW, Fu CX, Benkert T, et al
    Advanced zoomed diffusion-weighted imaging vs. full-field-of-view diffusion-weighted imaging in prostate cancer detection: a radiomic features study.
    Eur Radiol. 2020 Sep 16. pii: 10.1007/s00330-020-07227.
    PubMed     Abstract available
    
    
    
42. KIM CH, Kim CK, Park JJ, Park SY, et al
    Yield of concurrent systemic biopsy during MRI-targeted biopsy according to Prostate Imaging Reporting and Data System version 2 in patients with suspected prostate cancer.
    Eur Radiol. 2020 Sep 10. pii: 10.1007/s00330-020-07167.
    PubMed     Abstract available
    
    
    
43. SHIRADKAR R, Panda A, Leo P, Janowczyk A, et al
    T1 and T2 MR fingerprinting measurements of prostate cancer and prostatitis correlate with deep learning-derived estimates of epithelium, lumen, and stromal composition on corresponding whole mount histopathology.
    Eur Radiol. 2020 Sep 2. pii: 10.1007/s00330-020-07214.
    PubMed     Abstract available
    
    
    
44. CORNUD F, Lefevre A, Flam T, Dumonceau O, et al
    MRI-directed high-frequency (29MhZ) TRUS-guided biopsies: initial results of a single-center study.
    Eur Radiol. 2020;30:4838-4846.
    PubMed     Abstract available
    
    
    August 2020
45. GUGLIANDOLO SG, Pepa M, Isaksson LJ, Marvaso G, et al
    MRI-based radiomics signature for localized prostate cancer: a new clinical tool for cancer aggressiveness prediction? Sub-study of prospective phase II trial on ultra-hypofractionated radiotherapy (AIRC IG-13218).
    Eur Radiol. 2020 Aug 27. pii: 10.1007/s00330-020-07105.
    PubMed     Abstract available
    
    
    
46. SUN C, Wang S, Chatterjee A, Medved M, et al
    T2*-weighted MRI as a non-contrast-enhanced method for assessment of focal laser ablation zone extent in prostate cancer thermotherapy.
    Eur Radiol. 2020 Aug 12. pii: 10.1007/s00330-020-07127.
    PubMed     Abstract available
    
    
    
47. ABREU-GOMEZ J, Walker D, Alotaibi T, McInnes MDF, et al
    Effect of observation size and apparent diffusion coefficient (ADC) value in PI-RADS v2.1 assessment category 4 and 5 observations compared to adverse pathological outcomes.
    Eur Radiol. 2020;30:4251-4261.
    PubMed     Abstract available
    
    
    July 2020
48. HIREMATH A, Shiradkar R, Merisaari H, Prasanna P, et al
    Test-retest repeatability of a deep learning architecture in detecting and segmenting clinically significant prostate cancer on apparent diffusion coefficient (ADC) maps.
    Eur Radiol. 2020 Jul 23. pii: 10.1007/s00330-020-07065.
    PubMed     Abstract available
    
    
    
49. BERNATZ S, Ackermann J, Mandel P, Kaltenbach B, et al
    Comparison of machine learning algorithms to predict clinically significant prostate cancer of the peripheral zone with multiparametric MRI using clinical assessment categories and radiomic features.
    Eur Radiol. 2020 Jul 16. pii: 10.1007/s00330-020-07064.
    PubMed     Abstract available
    
    
    
50. ZAWAIDEH JP, Sala E, Shaida N, Koo B, et al
    Diagnostic accuracy of biparametric versus multiparametric prostate MRI: assessment of contrast benefit in clinical practice.
    Eur Radiol. 2020;30:4039-4049.
    PubMed     Abstract available
    
    
    June 2020
51. CUOCOLO R, Cipullo MB, Stanzione A, Romeo V, et al
    Machine learning for the identification of clinically significant prostate cancer on MRI: a meta-analysis.
    Eur Radiol. 2020 Jun 30. pii: 10.1007/s00330-020-07027.
    PubMed     Abstract available
    
    
    
52. ARIF M, Schoots IG, Castillo Tovar J, Bangma CH, et al
    Clinically significant prostate cancer detection and segmentation in low-risk patients using a convolutional neural network on multi-parametric MRI.
    Eur Radiol. 2020 Jun 27. pii: 10.1007/s00330-020-07008.
    PubMed     Abstract available
    
    
    
53. DENIFFEL D, Abraham N, Namdar K, Dong X, et al
    Using decision curve analysis to benchmark performance of a magnetic resonance imaging-based deep learning model for prostate cancer risk assessment.
    Eur Radiol. 2020 Jun 26. pii: 10.1007/s00330-020-07030.
    PubMed     Abstract available
    
    
    
54. ULLRICH T, Arsov C, Quentin M, Mones F, et al
    Multiparametric magnetic resonance imaging can exclude prostate cancer progression in patients on active surveillance: a retrospective cohort study.
    Eur Radiol. 2020 Jun 26. pii: 10.1007/s00330-020-06997.
    PubMed     Abstract available
    
    
    
55. SVIRYDENKA H, Muehlematter UJ, Nagel HW, Delso G, et al
    (68)Ga-PSMA-11 dose reduction for dedicated pelvic imaging with simultaneous PET/MR using TOF BSREM reconstructions.
    Eur Radiol. 2020;30:3188-3197.
    PubMed     Abstract available
    
    
    
56. BREMBILLA G, Dell'Oglio P, Stabile A, Damascelli A, et al
    Interreader variability in prostate MRI reporting using Prostate Imaging Reporting and Data System version 2.1.
    Eur Radiol. 2020;30:3383-3392.
    PubMed     Abstract available
    
    
    May 2020
57. DE ROOIJ M, Israel B, Tummers M, Ahmed HU, et al
    ESUR/ESUI consensus statements on multi-parametric MRI for the detection of clinically significant prostate cancer: quality requirements for image acquisition, interpretation and radiologists' training.
    Eur Radiol. 2020 May 19. pii: 10.1007/s00330-020-06929.
    PubMed     Abstract available
    
    
    April 2020
58. WINKEL DJ, Breit HC, Block TK, Boll DT, et al
    High spatiotemporal resolution dynamic contrast-enhanced MRI improves the image-based discrimination of histopathology risk groups of peripheral zone prostate cancer: a supervised machine learning approach.
    Eur Radiol. 2020 Apr 23. pii: 10.1007/s00330-020-06849.
    PubMed     Abstract available
    
    
    
59. LIECHTI MR, Muehlematter UJ, Schneider AF, Eberli D, et al
    Manual prostate cancer segmentation in MRI: interreader agreement and volumetric correlation with transperineal template core needle biopsy.
    Eur Radiol. 2020 Apr 19. pii: 10.1007/s00330-020-06786.
    PubMed     Abstract available
    
    
    
60. ALVES JR, Muglia VF, Lucchesi FR, Faria RAOG, et al
    Independent external validation of nomogram to predict extracapsular extension in patients with prostate cancer.
    Eur Radiol. 2020 Apr 19. pii: 10.1007/s00330-020-06839.
    PubMed     Abstract available
    
    
    
61. GIGANTI F, Pecoraro M, Stavrinides V, Stabile A, et al
    Interobserver reproducibility of the PRECISE scoring system for prostate MRI on active surveillance: results from a two-centre pilot study.
    Eur Radiol. 2020;30:2082-2090.
    PubMed     Abstract available
    
    
    March 2020
62. RUDOLPH MM, Baur ADJ, Haas M, Cash H, et al
    Validation of the PI-RADS language: predictive values of PI-RADS lexicon descriptors for detection of prostate cancer.
    Eur Radiol. 2020 Mar 26. pii: 10.1007/s00330-020-06773.
    PubMed     Abstract available
    
    

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