Emerging and anticipated innovations in prostate cancer MRI and their impact on patient care.

التفاصيل البيبلوغرافية
العنوان:	Emerging and anticipated innovations in prostate cancer MRI and their impact on patient care.
المؤلفون:	Correia ETO; Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA., Baydoun A; Department of Radiation Oncology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA., Li Q; Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA., Costa DN; Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA., Bittencourt LK; Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA. Leonardo.KayatBittencourt@Uhhospitals.org.; Department of Radiology, Case Western Reserve University, 11100 Euclid Ave, Cleveland, OH, 44106, USA. Leonardo.KayatBittencourt@Uhhospitals.org.
المصدر:	Abdominal radiology (New York) [Abdom Radiol (NY)] 2024 Jun 14. Date of Electronic Publication: 2024 Jun 14.
Publication Model:	Ahead of Print
نوع المنشور:	Journal Article; Review
اللغة:	English
بيانات الدورية:	Publisher: Springer Country of Publication: United States NLM ID: 101674571 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2366-0058 (Electronic) NLM ISO Abbreviation: Abdom Radiol (NY) Subsets: MEDLINE
أسماء مطبوعة:	Original Publication: [New York] : Springer, [2016]-
مستخلص:	Prostate cancer (PCa) remains the leading malignancy affecting men, with over 3 million men living with the disease in the US, and an estimated 288,000 new cases and almost 35,000 deaths in 2023 in the United States alone. Over the last few decades, imaging has been a cornerstone in PCa care, with a crucial role in the detection, staging, and assessment of PCa recurrence or by guiding diagnostic or therapeutic interventions. To improve diagnostic accuracy and outcomes in PCa care, remarkable advancements have been made to different imaging modalities in recent years. This paper focuses on reviewing the main innovations in the field of PCa magnetic resonance imaging, including MRI protocols, MRI-guided procedural interventions, artificial intelligence algorithms and positron emission tomography, which may impact PCa care in the future. (© 2024. The Author(s).)
References:	Cancer of the Prostate - Cancer Stat Facts. In: SEER. https://seer.cancer.gov/statfacts/html/prost.html . Accessed 7 Jul 2023. Financial Burden of Cancer Care \| Cancer Trends Progress Report. https://progressreport.cancer.gov/after/economic_burden . Accessed 7 Jul 2023. Sadinski M, Karczmar G, Peng Y, et al (2016) Pilot Study of the Use of Hybrid Multidimensional T2-Weighted Imaging-DWI for the Diagnosis of Prostate Cancer and Evaluation of Gleason Score. AJR Am J Roentgenol 207:592–598. https://doi.org/10.2214/AJR.15.15626. (PMID: 10.2214/AJR.15.15626273520265074540) Chatterjee A, Mercado C, Bourne RM, et al (2022) Validation of Prostate Tissue Composition by Using Hybrid Multidimensional MRI: Correlation with Histologic Findings. Radiology 302:368–377. https://doi.org/10.1148/radiol.2021204459. (PMID: 10.1148/radiol.202120445934751615) Lee G, Chatterjee A, Harmath C, et al (2023) Improving reader accuracy and specificity with the addition of hybrid multidimensional-MRI to multiparametric-MRI in diagnosing clinically significant prostate cancers. Abdom Radiol N Y 48:3216–3228. https://doi.org/10.1007/s00261-023-03969-z. (PMID: 10.1007/s00261-023-03969-z) Lee GH, Chatterjee A, Karademir I, et al (2022) Comparing Radiologist Performance in Diagnosing Clinically Significant Prostate Cancer with Multiparametric versus Hybrid Multidimensional MRI. Radiology 305:399–407. https://doi.org/10.1148/radiol.211895. (PMID: 10.1148/radiol.21189535880981) Chatterjee A, Lee G, Dietz D, Oto A, Karczmar G. Cross vendor validation of Hybrid Multidimensional MRI in the non-invasive measurement of prostate tissue composition. Proc. Intl. Soc. Mag. Reson. Med. 28 (2020). Available from: https://cds.ismrm.org/protected/20MProceedings/PDFfiles/3778.html . Accessed 2024 May 10. Chatterjee A, Engelmann R, Harmath C, Yousuf A, Reynold L, Karczmar G, Oto A. Prospective validation of an automated hybrid multidimensional MRI-based tool to identify areas for prostate cancer biopsy. 2023 ARRS Annual Meeting. Available from: https://apps.arrs.org/AbstractsAM23Open/Main/Abstract/1127 . Accessed 10 May 2024. Sabouri S, Fazli L, Chang SD, et al (2017) MR measurement of luminal water in prostate gland: Quantitative correlation between MRI and histology. J Magn Reson Imaging JMRI 46:861–869. https://doi.org/10.1002/jmri.25624. (PMID: 10.1002/jmri.2562428130866) Gilani N, Rosenkrantz AB, Malcolm P, Johnson G (2015) Minimization of errors in biexponential T2 measurements of the prostate. J Magn Reson Imaging JMRI 42:1072–1077. https://doi.org/10.1002/jmri.24870. (PMID: 10.1002/jmri.2487025704897) Sabouri S, Chang SD, Savdie R, et al (2017) Luminal Water Imaging: A New MR Imaging T2 Mapping Technique for Prostate Cancer Diagnosis. Radiology 284:451–459. https://doi.org/10.1148/radiol.2017161687. (PMID: 10.1148/radiol.201716168728394754) Hectors SJ, Said D, Gnerre J, et al (2020) Luminal Water Imaging: Comparison With Diffusion-Weighted Imaging (DWI) and PI-RADS for Characterization of Prostate Cancer Aggressiveness. J Magn Reson Imaging JMRI 52:271–279. https://doi.org/10.1002/jmri.27050. (PMID: 10.1002/jmri.2705031961049) Carlin D, Orton MR, Collins D, deSouza NM (2019) Probing structure of normal and malignant prostate tissue before and after radiation therapy with luminal water fraction and diffusion-weighted MRI. J Magn Reson Imaging JMRI 50:619–627. https://doi.org/10.1002/jmri.26597. (PMID: 10.1002/jmri.2659730589150) Chan RW, Lau AZ, Detzler G, et al (2019) Evaluating the accuracy of multicomponent T2 parameters for luminal water imaging of the prostate with acceleration using inner-volume 3D GRASE. Magn Reson Med 81:466–476. https://doi.org/10.1002/mrm.27372. (PMID: 10.1002/mrm.2737230058296) Devine W, Giganti F, Johnston EW, et al (2019) Simplified Luminal Water Imaging for the Detection of Prostate Cancer From Multiecho T2 MR Images. J Magn Reson Imaging JMRI 50:910–917. https://doi.org/10.1002/jmri.26608. (PMID: 10.1002/jmri.2660830566264) Ma D, Gulani V, Seiberlich N, et al (2013) Magnetic Resonance Fingerprinting. Nature 495:187–192. https://doi.org/10.1038/nature11971. (PMID: 10.1038/nature11971234860583602925) Yu AC, Badve C, Ponsky LE, et al (2017) Development of a Combined MR Fingerprinting and Diffusion Examination for Prostate Cancer. Radiology 283:729–738. https://doi.org/10.1148/radiol.2017161599. (PMID: 10.1148/radiol.201716159928187264) Panda A, Obmann VC, Lo W-C, et al (2019) MR Fingerprinting and ADC Mapping for Characterization of Lesions in the Transition Zone of the Prostate Gland. Radiology 292:685–694. https://doi.org/10.1148/radiol.2019181705. (PMID: 10.1148/radiol.201918170531335285) Lo W-C, Bittencourt LK, Panda A, et al (2022) Multicenter Repeatability and Reproducibility of MR Fingerprinting in Phantoms and in Prostatic Tissue. Magn Reson Med 88:1818–1827. https://doi.org/10.1002/mrm.29264. (PMID: 10.1002/mrm.29264357133799469467) de Oliveira Correia ET, Qiao PL, Griswold MA, et al (2023) Magnetic resonance fingerprinting based comprehensive quantification of T1 and T2 values of the background prostatic peripheral zone: Correlation with clinical and demographic features. Eur J Radiol 164:110883. https://doi.org/10.1016/j.ejrad.2023.110883. (PMID: 10.1016/j.ejrad.2023.11088337209463) Turkbey B, Rosenkrantz AB, Haider MA, 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. https://doi.org/10.1016/j.eururo.2019.02.033. (PMID: 10.1016/j.eururo.2019.02.03330898406) Brunsing RL, Schenker-Ahmed NM, White NS, et al (2017) Restriction spectrum imaging: An evolving imaging biomarker in prostate MRI. J Magn Reson Imaging JMRI 45:323–336. https://doi.org/10.1002/jmri.25419. (PMID: 10.1002/jmri.2541927527500) Conlin CC, Feng CH, Rodriguez-Soto AE, et al (2021) Improved Characterization of Diffusion in Normal and Cancerous Prostate Tissue Through Optimization of Multicompartmental Signal Models. J Magn Reson Imaging JMRI 53:628–639. https://doi.org/10.1002/jmri.27393. (PMID: 10.1002/jmri.2739333131186) Feng CH, Conlin CC, Batra K, et al (2021) Voxel-level Classification of Prostate Cancer on Magnetic Resonance Imaging: Improving Accuracy Using Four-Compartment Restriction Spectrum Imaging. J Magn Reson Imaging JMRI 54:975–984. https://doi.org/10.1002/jmri.27623. (PMID: 10.1002/jmri.2762333786915) Zhong AY, Digma LA, Hussain T, et al (2023) Automated Patient-level Prostate Cancer Detection with Quantitative Diffusion Magnetic Resonance Imaging. Eur Urol Open Sci 47:20–28. https://doi.org/10.1016/j.euros.2022.11.009. (PMID: 10.1016/j.euros.2022.11.00936601040) Panagiotaki E, Walker-Samuel S, Siow B, et al (2014) Noninvasive quantification of solid tumor microstructure using VERDICT MRI. Cancer Res 74:1902–1912. https://doi.org/10.1158/0008-5472.CAN-13-2511. (PMID: 10.1158/0008-5472.CAN-13-251124491802) Panagiotaki E, Chan RW, Dikaios N, et al (2015) Microstructural characterization of normal and malignant human prostate tissue with vascular, extracellular, and restricted diffusion for cytometry in tumours magnetic resonance imaging. Invest Radiol 50:218–227. https://doi.org/10.1097/RLI.0000000000000115. (PMID: 10.1097/RLI.000000000000011525426656) Johnston EW, Bonet-Carne E, Ferizi U, et al (2019) VERDICT MRI for Prostate Cancer: Intracellular Volume Fraction versus Apparent Diffusion Coefficient. Radiology 291:391–397. https://doi.org/10.1148/radiol.2019181749. (PMID: 10.1148/radiol.201918174930938627) Singh S, Rogers H, Kanber B, et al (2022) Avoiding Unnecessary Biopsy after Multiparametric Prostate MRI with VERDICT Analysis: The INNOVATE Study. Radiology 305:623–630. https://doi.org/10.1148/radiol.212536. (PMID: 10.1148/radiol.21253635916679) Hopstaken JS, Bomers JGR, Sedelaar MJP, et al (2022) An Updated Systematic Review on Focal Therapy in Localized Prostate Cancer: What Has Changed over the Past 5 Years? Eur Urol 81:5–33. https://doi.org/10.1016/j.eururo.2021.08.005. (PMID: 10.1016/j.eururo.2021.08.00534489140) Raz O, Haider MA, Davidson SRH, et al (2010) Real-time magnetic resonance imaging-guided focal laser therapy in patients with low-risk prostate cancer. Eur Urol 58:173–177. https://doi.org/10.1016/j.eururo.2010.03.006. (PMID: 10.1016/j.eururo.2010.03.00620334965) Lindner U, Lawrentschuk N, Weersink RA, et al (2010) Focal laser ablation for prostate cancer followed by radical prostatectomy: validation of focal therapy and imaging accuracy. Eur Urol 57:1111–1114. https://doi.org/10.1016/j.eururo.2010.03.008. (PMID: 10.1016/j.eururo.2010.03.00820346578) Mehralivand S, George AK, Hoang AN, et al (2021) MRI-guided focal laser ablation of prostate cancer: a prospective single-arm, single-center trial with 3 years of follow-up. Diagn Interv Radiol 27:394–400. https://doi.org/10.5152/dir.2021.20095. (PMID: 10.5152/dir.2021.20095340031278136525) Westin C, Chatterjee A, Ku E, et al (2018) MRI Findings After MRI-Guided Focal Laser Ablation of Prostate Cancer. AJR Am J Roentgenol 211:595–604. https://doi.org/10.2214/AJR.17.19201. (PMID: 10.2214/AJR.17.1920129995499) Oto A, Sethi I, Karczmar G, et al (2013) MR imaging-guided focal laser ablation for prostate cancer: phase I trial. Radiology 267:932–940. https://doi.org/10.1148/radiol.13121652. (PMID: 10.1148/radiol.1312165223440319) Eggener SE, Yousuf A, Watson S, et al (2016) Phase II Evaluation of Magnetic Resonance Imaging Guided Focal Laser Ablation of Prostate Cancer. J Urol 196:1670–1675. https://doi.org/10.1016/j.juro.2016.07.074. (PMID: 10.1016/j.juro.2016.07.07427449263) Al-Hakeem Y, Raz O, Gacs Z, et al (2019) Magnetic resonance image-guided focal laser ablation in clinically localized prostate cancer: safety and efficacy. ANZ J Surg 89:1610–1614. https://doi.org/10.1111/ans.15526. (PMID: 10.1111/ans.1552631679182) Chopra R, Colquhoun A, Burtnyk M, et al (2012) MR imaging-controlled transurethral ultrasound therapy for conformal treatment of prostate tissue: initial feasibility in humans. Radiology 265:303–313. https://doi.org/10.1148/radiol.12112263. (PMID: 10.1148/radiol.1211226322929332) Klotz L, Pavlovich CP, Chin J, et al (2021) Magnetic Resonance Imaging-Guided Transurethral Ultrasound Ablation of Prostate Cancer. J Urol 205:769–779. https://doi.org/10.1097/JU.0000000000001362. (PMID: 10.1097/JU.000000000000136233021440) Eggener S, Pavlovich C, Koch M, Gardner T, Zagaja G, Penson D, et al. Pivotal study of MRI-guided transurethral ultrasound ablation (TULSA) of localized prostate cancer: 5-year follow up. 24th Annual Meeting Society of Urologic Oncology. Available from: https://suo-abstracts.secure-platform.com/a/gallery/rounds/18/details/3269 . Accessed 15 May 2024. Profound Medical Inc. A comparison of TULSA procedure vs. radical prostatectomy in participants with localized prostate cancer (CAPTAIN). ClinicalTrials.gov [Internet]. 2024 Apr 10 [cited 2024 May 10]. Available from: https://www.clinicaltrials.gov/study/NCT05027477 . Establishment Registration & Device Listing. https://www.accessdata.fda.gov/scrIpts/cdrh/cfdocs/cfRL/rl.cfm?rid=295066 . Accessed 11 Jul 2023. Satya P, Adams J, Venkataraman SS, et al (2022) Office-Based, Single-Sided, Low-Field MRI-Guided Prostate Biopsy. Cureus 14:e25021. https://doi.org/10.7759/cureus.25021. (PMID: 10.7759/cureus.2502135719765) Eastham JA, Auffenberg GB, Barocas DA, et al (2022) Clinically Localized Prostate Cancer: AUA/ASTRO Guideline. Part III: Principles of Radiation and Future Directions. J Urol 208:26–33. https://doi.org/10.1097/JU.0000000000002759. (PMID: 10.1097/JU.000000000000275935536141) Pathmanathan AU, McNair HA, Schmidt MA, et al (2019) Comparison of prostate delineation on multimodality imaging for MR-guided radiotherapy. Br J Radiol 92:20180948. https://doi.org/10.1259/bjr.20180948. (PMID: 10.1259/bjr.20180948306767726540870) Lee JY, Spratt DE, Liss AL, McLaughlin PW (2016) Vessel-sparing radiation and functional anatomy-based preservation for erectile function after prostate radiotherapy. Lancet Oncol 17:e198–208. https://doi.org/10.1016/S1470-2045(16)00063-2. (PMID: 10.1016/S1470-2045(16)00063-227301047) Kerkmeijer LGW, Groen VH, Pos FJ, et al (2021) Focal Boost to the Intraprostatic Tumor in External Beam Radiotherapy for Patients With Localized Prostate Cancer: Results From the FLAME Randomized Phase III Trial. J Clin Oncol Off J Am Soc Clin Oncol 39:787–796. https://doi.org/10.1200/JCO.20.02873. (PMID: 10.1200/JCO.20.02873) Reijnen C, Brunenberg EJL, Kerkmeijer LGW (2023) Advancing the treatment of localized prostate cancer with MR-guided radiotherapy. Prostate Cancer Prostatic Dis 26:50–52. https://doi.org/10.1038/s41391-022-00632-4. (PMID: 10.1038/s41391-022-00632-436550217) Teunissen FR, Willigenburg T, Tree AC, et al (2023) Magnetic Resonance-Guided Adaptive Radiation Therapy for Prostate Cancer: The First Results from the MOMENTUM study-An International Registry for the Evidence-Based Introduction of Magnetic Resonance-Guided Adaptive Radiation Therapy. Pract Radiat Oncol 13:e261–e269. https://doi.org/10.1016/j.prro.2022.09.007. (PMID: 10.1016/j.prro.2022.09.00736462619) Kishan AU, Ma TM, Lamb JM, et al (2023) Magnetic Resonance Imaging-Guided vs Computed Tomography-Guided Stereotactic Body Radiotherapy for Prostate Cancer: The MIRAGE Randomized Clinical Trial. JAMA Oncol 9:365–373. https://doi.org/10.1001/jamaoncol.2022.6558. (PMID: 10.1001/jamaoncol.2022.6558366338779857817) Gassenmaier S, Afat S, Nickel D, et al (2021) Deep learning-accelerated T2-weighted imaging of the prostate: Reduction of acquisition time and improvement of image quality. Eur J Radiol 137:109600. https://doi.org/10.1016/j.ejrad.2021.109600. (PMID: 10.1016/j.ejrad.2021.10960033610853) Ueda T, Ohno Y, Yamamoto K, et al (2022) Deep Learning Reconstruction of Diffusion-weighted MRI Improves Image Quality for Prostatic Imaging. Radiology 303:373–381. https://doi.org/10.1148/radiol.204097. (PMID: 10.1148/radiol.20409735103536) van Lohuizen Q, Roest C, Simonis FFJ, et al (2024) Assessing deep learning reconstruction for faster prostate MRI: visual vs. diagnostic performance metrics. Eur Radiol. https://doi.org/10.1007/s00330-024-10771-y. (PMID: 10.1007/s00330-024-10771-y38724765) Giganti F, Allen C, Emberton M, et al (2020) Prostate Imaging Quality (PI-QUAL): A New Quality Control Scoring System for Multiparametric Magnetic Resonance Imaging of the Prostate from the PRECISION trial. Eur Urol Oncol 3:615–619. https://doi.org/10.1016/j.euo.2020.06.007. (PMID: 10.1016/j.euo.2020.06.00732646850) Lin Y, Belue MJ, Yilmaz EC, et al (2024) Deep Learning-Based T2-Weighted MR Image Quality Assessment and Its Impact on Prostate Cancer Detection Rates. J Magn Reson Imaging JMRI 59:2215–2223. https://doi.org/10.1002/jmri.29031. (PMID: 10.1002/jmri.2903137811666) Belue MJ, Law YM, Marko J, et al (2024) Deep Learning-Based Interpretable AI for Prostate T2W MRI Quality Evaluation. Acad Radiol 31:1429–1437. https://doi.org/10.1016/j.acra.2023.09.030. (PMID: 10.1016/j.acra.2023.09.03037858505) Alis D, Kartal MS, Seker ME, et al (2023) Deep learning for assessing image quality in bi-parametric prostate MRI: A feasibility study. Eur J Radiol 165:110924. https://doi.org/10.1016/j.ejrad.2023.110924. (PMID: 10.1016/j.ejrad.2023.11092437354768) Wang B, Lei Y, Tian S, et al (2019) Deeply supervised 3D fully convolutional networks with group dilated convolution for automatic MRI prostate segmentation. Med Phys 46:1707–1718. https://doi.org/10.1002/mp.13416. (PMID: 10.1002/mp.13416307027596453726) Ushinsky A, Bardis M, Glavis-Bloom J, et al (2021) A 3D-2D Hybrid U-Net Convolutional Neural Network Approach to Prostate Organ Segmentation of MpMRI. AJR Am J Roentgenol 216:111–116. https://doi.org/10.2214/AJR.19.22168. (PMID: 10.2214/AJR.19.2216832812797) Sanford TH, Zhang L, Harmon SA, et al (2020) Data Augmentation and Transfer Learning to Improve Generalizability of an Automated Prostate Segmentation Model. AJR Am J Roentgenol 215:1403–1410. https://doi.org/10.2214/AJR.19.22347. (PMID: 10.2214/AJR.19.22347330527378974988) Cuocolo R, Cipullo MB, Stanzione A, et al (2020) Machine learning for the identification of clinically significant prostate cancer on MRI: a meta-analysis. Eur Radiol 30:6877–6887. https://doi.org/10.1007/s00330-020-07027-w. (PMID: 10.1007/s00330-020-07027-w32607629) Hamm CA, Baumgärtner GL, Biessmann F, et al (2023) Interactive Explainable Deep Learning Model Informs Prostate Cancer Diagnosis at MRI. Radiology 307:e222276. https://doi.org/10.1148/radiol.222276. (PMID: 10.1148/radiol.22227637039688) Youn SY, Choi MH, Kim DH, et al (2021) Detection and PI-RADS classification of focal lesions in prostate MRI: Performance comparison between a deep learning-based algorithm (DLA) and radiologists with various levels of experience. Eur J Radiol 142:109894. https://doi.org/10.1016/j.ejrad.2021.109894. (PMID: 10.1016/j.ejrad.2021.10989434388625) Li H, Moon JT, Iyer D, et al (2023) Decoding radiology reports: Potential application of OpenAI ChatGPT to enhance patient understanding of diagnostic reports. Clin Imaging 101:137–141. https://doi.org/10.1016/j.clinimag.2023.06.008. (PMID: 10.1016/j.clinimag.2023.06.00837336169) Hu Y, Modat M, Gibson E, et al (2018) Weakly-supervised convolutional neural networks for multimodal image registration. Med Image Anal 49:1–13. https://doi.org/10.1016/j.media.2018.07.002. (PMID: 10.1016/j.media.2018.07.002300072536742510) Anas EMA, Mousavi P, Abolmaesumi P (2018) A deep learning approach for real time prostate segmentation in freehand ultrasound guided biopsy. Med Image Anal 48:107–116. https://doi.org/10.1016/j.media.2018.05.010. (PMID: 10.1016/j.media.2018.05.01029886268) Heston WD (1997) Characterization and glutamyl preferring carboxypeptidase function of prostate specific membrane antigen: a novel folate hydrolase. Urology 49:104–112. https://doi.org/10.1016/s0090-4295(97)00177-5. (PMID: 10.1016/s0090-4295(97)00177-59123729) Research C for DE and (2021) FDA approves second PSMA-targeted PET imaging drug for men with prostate cancer. FDA. Hofman MS, Lawrentschuk N, Francis RJ, et al (2020) Prostate-specific membrane antigen PET-CT in patients with high-risk prostate cancer before curative-intent surgery or radiotherapy (proPSMA): a prospective, randomised, multicentre study. Lancet Lond Engl 395:1208–1216. https://doi.org/10.1016/S0140-6736(20)30314-7. (PMID: 10.1016/S0140-6736(20)30314-7) Surasi DS, Eiber M, Maurer T, et al (2023) Diagnostic Performance and Safety of Positron Emission Tomography with 18F-rhPSMA-7.3 in Patients with Newly Diagnosed Unfavourable Intermediate- to Very-high-risk Prostate Cancer: Results from a Phase 3, Prospective, Multicentre Study (LIGHTHOUSE). Eur Urol S0302–2838(23)02949–4. https://doi.org/10.1016/j.eururo.2023.06.018. Jani AB, Ravizzini GC, Gartrell BA, et al (2023) Diagnostic Performance and Safety of 18F-rhPSMA-7.3 Positron Emission Tomography in Men With Suspected Prostate Cancer Recurrence: Results From a Phase 3, Prospective, Multicenter Study (SPOTLIGHT). J Urol 210:299–311. https://doi.org/10.1097/JU.0000000000003493. (PMID: 10.1097/JU.000000000000349337126069) Evangelista L, Maurer T, van der Poel H, et al (2022) [68Ga]Ga-PSMA Versus [18F]PSMA Positron Emission Tomography/Computed Tomography in the Staging of Primary and Recurrent Prostate Cancer. A Systematic Review of the Literature. Eur Urol Oncol 5:273–282. https://doi.org/10.1016/j.euo.2022.03.004. (PMID: 10.1016/j.euo.2022.03.00435367165) Trabulsi EJ, Rumble RB, Jadvar H, et al (2020) Optimum Imaging Strategies for Advanced Prostate Cancer: ASCO Guideline. J Clin Oncol Off J Am Soc Clin Oncol 38:1963–1996. https://doi.org/10.1200/JCO.19.02757. (PMID: 10.1200/JCO.19.02757) Evangelista L, Zattoni F, Cassarino G, et al (2021) PET/MRI in prostate cancer: a systematic review and meta-analysis. Eur J Nucl Med Mol Imaging 48:859–873. https://doi.org/10.1007/s00259-020-05025-0. (PMID: 10.1007/s00259-020-05025-032901351) Huang R, Li Y, Wu H, et al (2023) 68Ga-PSMA-11 PET/CT versus 68Ga-PSMA-11 PET/MRI for the detection of biochemically recurrent prostate cancer: a systematic review and meta-analysis. Front Oncol 13:1216894. https://doi.org/10.3389/fonc.2023.1216894. (PMID: 10.3389/fonc.2023.12168943764543310461474) Giganti F, Dinneen E, Kasivisvanathan V, et al (2022) Inter-reader agreement of the PI-QUAL score for prostate MRI quality in the NeuroSAFE PROOF trial. Eur Radiol 32:879–889. https://doi.org/10.1007/s00330-021-08169-1. (PMID: 10.1007/s00330-021-08169-134327583) Karanasios E, Caglic I, Zawaideh JP, Barrett T (2022) Prostate MRI quality: clinical impact of the PI-QUAL score in prostate cancer diagnostic work-up. Br J Radiol 95:20211372. https://doi.org/10.1259/bjr.20211372. (PMID: 10.1259/bjr.202113723517997110993954) Pötsch N, Rainer E, Clauser P, et al (2022) Impact of PI-QUAL on PI-RADS and cancer yield in an MRI-TRUS fusion biopsy population. Eur J Radiol 154:110431. https://doi.org/10.1016/j.ejrad.2022.110431. (PMID: 10.1016/j.ejrad.2022.11043135803101) Brembilla G, Lavalle S, Parry T, et al (2023) Impact of prostate imaging quality (PI-QUAL) score on the detection of clinically significant prostate cancer at biopsy. Eur J Radiol 164:110849. https://doi.org/10.1016/j.ejrad.2023.110849. (PMID: 10.1016/j.ejrad.2023.11084937141845) Windisch O, Benamran D, Dariane C, et al (2023) Role of the Prostate Imaging Quality PI-QUAL Score for Prostate Magnetic Resonance Image Quality in Pathological Upstaging After Radical Prostatectomy: A Multicentre European Study. Eur Urol Open Sci 47:94–101. https://doi.org/10.1016/j.euros.2022.11.013. (PMID: 10.1016/j.euros.2022.11.01336601048) Lee CH, Vellayappan B, Tan CH (2022) Comparison of diagnostic performance and inter-reader agreement between PI-RADS v2.1 and PI-RADS v2: systematic review and meta-analysis. Br J Radiol 95:20210509. https://doi.org/10.1259/bjr.20210509. (PMID: 10.1259/bjr.2021050934520694) Bhayana R, O’Shea A, Anderson MA, et al (2021) PI-RADS Versions 2 and 2.1: Interobserver Agreement and Diagnostic Performance in Peripheral and Transition Zone Lesions Among Six Radiologists. AJR Am J Roentgenol 217:141–151. https://doi.org/10.2214/AJR.20.24199. (PMID: 10.2214/AJR.20.2419932903060) Labus S, Altmann MM, Huisman H, et al (2023) A concurrent, deep learning-based computer-aided detection system for prostate mpMRI: a performance study involving experienced and less-experienced radiologists. Eur Radiol 33:64–76. https://doi.org/10.1007/s00330-022-08978-y. (PMID: 10.1007/s00330-022-08978-y35900376) Wenske S, Quarrier S, Katz AE (2013) Salvage cryosurgery of the prostate for failure after primary radiotherapy or cryosurgery: long-term clinical, functional, and oncologic outcomes in a large cohort at a tertiary referral centre. Eur Urol 64:1–7. https://doi.org/10.1016/j.eururo.2012.07.008. (PMID: 10.1016/j.eururo.2012.07.00822840351) Heard JR, Naser-Tavakolian A, Nazmifar M, Ahdoot M (2023) Focal prostate cancer therapy in the era of multiparametric MRI: a review of options and outcomes. Prostate Cancer Prostatic Dis 26:218–227. https://doi.org/10.1038/s41391-022-00501-0. (PMID: 10.1038/s41391-022-00501-035246609) Blazevski A, Scheltema MJ, Yuen B, et al (2020) Oncological and Quality-of-life Outcomes Following Focal Irreversible Electroporation as Primary Treatment for Localised Prostate Cancer: A Biopsy-monitored Prospective Cohort. Eur Urol Oncol 3:283–290. https://doi.org/10.1016/j.euo.2019.04.008. (PMID: 10.1016/j.euo.2019.04.00831103721)
فهرسة مساهمة:	Keywords: Artificial intelligence; Magnetic resonance imaging; Positron emission tomography; Prostatic neoplasms
تواريخ الأحداث:	Date Created: 20240614 Latest Revision: 20240614
رمز التحديث:	20240615
DOI:	10.1007/s00261-024-04423-4
PMID:	38877356
قاعدة البيانات:	MEDLINE

Find this article in full text from Springer Verlag.

Full Text Finder

الوصف
تدمد:	2366-0058
DOI:	10.1007/s00261-024-04423-4