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Artykuły w czasopismach na temat "Multiparametric Magnetic Resonance Imaging (mp-MRI)"
Kapur, Savinay, Chandan J. Das i Sanjay Sharma. "Multiparametric Magnetic Resonance Imaging of the Prostate: An Update". Annals of the National Academy of Medical Sciences (India) 55, nr 02 (kwiecień 2019): 074–83. http://dx.doi.org/10.1055/s-0039-1694077.
Pełny tekst źródłaYadav, Kuldeep, Binit Sureka, Poonam Elhence, Gautam Ram Choudhary i Himanshu Pandey. "Pitfalls in Prostate Cancer Magnetic Resonance Imaging". Indian Journal of Medical and Paediatric Oncology 42, nr 01 (marzec 2021): 080–88. http://dx.doi.org/10.1055/s-0041-1730757.
Pełny tekst źródłaSankineni, Sandeep, Murat Osman i Peter L. Choyke. "Functional MRI in Prostate Cancer Detection". BioMed Research International 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/590638.
Pełny tekst źródłaPopita, Cristian, Anca Raluca Popita, Adela Sitar-Taut, Bogdan Petrut, Bogdan Fetica i Ioan Coman. "1.5-Tesla Multiparametric-Magnetic Resonance Imaging for the detection of clinically significant prostate cancer". Medicine and Pharmacy Reports 90, nr 1 (30.01.2017): 40–48. http://dx.doi.org/10.15386/cjmed-690.
Pełny tekst źródłaSardari, Al, John V. Thomas, Jeffrey W. Nix, Jason A. Pietryga, Rupan Sanyal, Jennifer B. Gordetsky i Soroush Rais-Bahrami. "Incidental Bladder Cancer Detected on Multiparametric Magnetic Resonance Imaging of the Prostate Gland". Case Reports in Urology 2015 (2015): 1–4. http://dx.doi.org/10.1155/2015/503154.
Pełny tekst źródłaUllrich, T., C. Arsov, M. Quentin, F. Mones, A. C. Westphalen, D. Mally, A. Hiester, P. Albers, G. Antoch i L. Schimmöller. "Multiparametric magnetic resonance imaging can exclude prostate cancer progression in patients on active surveillance: a retrospective cohort study". European Radiology 30, nr 11 (26.06.2020): 6042–51. http://dx.doi.org/10.1007/s00330-020-06997-1.
Pełny tekst źródłaObino, Mariah Kerubo, Edward Ng’ang’a Chege, Sudhir Vinayak i Samuel Gitau Nguk. "Utility of Multiparametric Magnetic Resonance Imaging as a Predictor of Clinically Significant Prostate Cancer in a Sub-Saharan African Population". Annals of African Surgery 19, nr 2 (30.05.2022): 108–15. http://dx.doi.org/10.4314/aas.v19i2.8.
Pełny tekst źródłaDoykov, Mladen, Lyubomir Chervenkov, Silvia Tsvetkova-Trichkova, Katya Doykova i Aleksandar Georgiev. "Assessment of the Utility of Multiparametric Magnetic Resonance Imaging for Initial Detection of Prostate Cancer". Open Access Macedonian Journal of Medical Sciences 10, B (10.07.2022): 1840–45. http://dx.doi.org/10.3889/oamjms.2022.10401.
Pełny tekst źródłaKowa, Jie-Ying, Neil Soneji, S. Aslam Sohaib, Erik Mayer, Stephen Hazell, Nicholas Butterfield, Joshua Shur i Derfel ap Dafydd. "Detection and staging of radio-recurrent prostate cancer using multiparametric MRI". British Journal of Radiology 94, nr 1120 (1.04.2021): 20201423. http://dx.doi.org/10.1259/bjr.20201423.
Pełny tekst źródłaFaccioli, Niccolò, Elena Santi, Giovanni Foti, Pierpaolo Curti i Mirko D'Onofrio. "Cost-effectiveness analysis of short biparametric magnetic resonance imaging protocol in men at risk of prostate cancer". Archivio Italiano di Urologia e Andrologia 94, nr 2 (29.06.2022): 160–65. http://dx.doi.org/10.4081/aiua.2022.2.160.
Pełny tekst źródłaRozprawy doktorskie na temat "Multiparametric Magnetic Resonance Imaging (mp-MRI)"
Huang, Bingsheng, i 黄炳升. "Quantitative multiparametric imaging for the evaluation of nasopharyngeal carcinoma using PET and DCE-MRI". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B47869586.
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Diagnostic Radiology
Doctoral
Doctor of Philosophy
Gu, Jing, i 谷静. "Multiparametric imaging using diffusion and dynamic-contrast enhanced MRI, and 18F-FDG PET/CT in the evaluation of primary rectal cancer andmalignant lymphoma". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B47027174.
Pełny tekst źródłaLi, Chao. "Characterising heterogeneity of glioblastoma using multi-parametric magnetic resonance imaging". Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/287475.
Pełny tekst źródłaPaulino, Rodrigo Domingues. "Multiparametric evaluation of the effects of two different doses of butorphanol in dogs undergoing brain MRI". Master's thesis, Universidade de Lisboa, Faculdade de Medicina Veterinária, 2018. http://hdl.handle.net/10400.5/16553.
Pełny tekst źródłaObjective: To investigate the effect of 0.2 and 0.3 mg/kg of intravenous (IV) butorphanol as pre-medication in client owned dogs with suspected intracranial pathology undergoing brain magnetic resonance imaging (MRI). Study design: prospective, randomized, blinded, clinical trial. Animals: Thirty-two client-owned dogs with suspected intracranial pathology, 17 males, 15 females. Methods: Each dog was randomly assigned to receive IV either 0.2 mg/kg (group L) or 0.3 mg/kg (group H) of butorphanol 10 to 15 minutes prior to general anesthesia (GA) induction. Following tracheal intubation, subjects were mechanically ventilated to maintain normocapnia (35-45 mmHg FE’CO2) and GA was maintained with isoflurane in oxygen and air. Scores for mentation, neurological status and sedation were recorded prior to GA; quality of induction, ease of intubation and recovery quality scores were assigned to each subject. Heart rate end expiratory fraction or pressure of expired and inspired gases, arterial blood pressure were recorded and compared between groups. Pulse oximetry, and body temperature were recorded at beginning of the procedure. Once the administration of isoflurane was discontinued, time to tracheal extubation, to first head lift and time to standing were recorded and recovery score was obtained. Results: No differences were observed regarding demographic data and ASA status. Mentation and neurological status were similar between groups. Sedation score was different between groups, with higher sedation in group H (p=0.017). Propofol induction dose was similar between groups. Monitored physiologic variables were not significantly different. Recovery times were similar in both groups. No perioperative complications were observed. Conclusion: The administration of 0.3 mg/kg IV butorphanol as pre-medication promoted higher sedation when compared with 0.2 mg/kg but does not confer any other clinical advantages or sparing effect on propofol induction dose in dogs with suspected intracranial pathology undergoing brain MRI.
RESUMO - AVALIAÇÃO MULTIPARAMÉTRICA DOS EFEITOS DE DUAS DOSES DIFERENTES DE BUTORFANOL EM CÃES SUBMETIDOS A EXAME IMAGIOLÓGICO DE RESSONÂNCIA MAGNÉTICA - Objectivo: Investigar o efeito de duas doses de butorfanol, 0.2 e 0.3 mg/kg, administradas via endovenosa (EV) como pre-medicação para cães com suspeita de patologia intracraniana, submetidos a exame imagiológico através de ressonância magnética (MRI). Desenho do estudo: prospetivo, aleatório, cego, estudo clínico. Animais: trinta e dois cães com cuidadores, 17 machos, 15 fêmeas. Métodos: para cada cão, de forma aleatória, foi designado para receber 0.2 mg/kg (grupo L) ou 0.3 mg/kg (grupo h) de butorfanol EV, 10 a 15 minutos antes de serem induzidos em anestesia geral (GA). Após intubação endotraqueal, os animais foram ventilados mecanicamente, de forma a manterem-se em normocapnia (35-45 mmHg FE’CO2), com uma mistura de isoflurano, oxigénio e ar. Antes de serem induzidos em anestesia geral, os cães foram classificados em relação ao estado de alerta, exame neurológico e sedação; qualidade de indução, facilidade de intubação, qualidade da recuperação da anestesia geral também foram classificados. Foram registados e comparados entre grupos os valores de frequência cardíaca, fração expirada de gases e valores de pressão arterial. Saturação de oxigénio na hemoglobina e temperatura foram medidas ao início da anestesia geral. Quando a administração de isoflurano foi interrompida, tempo até extubação, tempo até erguer a cabeça e tempo até estação foram registados. Resultados: não foram detetadas diferenças entre grupos em relação aos dados demográficos e classificação ASA. Classificação de sedação foi superior no grupo H comparado com o grupo L (p=0.017). Dose de indução de anestesia geral com propofol não diferiu entre grupos. Não foram detetadas diferenças significativas nas constantes fisiológicas medidas durante a anestesia geral. Tempos de recuperação foram similares entre grupos. Não foram registadas complicações peri-anestésicas em nenhum animal. Conclusão: A administração de 0.3 mg/kg de butorfanol EV como pré-medicação promoveu um maior grau de sedação quando comparada com a dose de 0.2 mg/kg em cães com suspeita de patologia intra-craniana submetidos para ressonância magnética.
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LUZZAGO, STEFANO. "REPEATED MRI SCANS DURING ACTIVE SURVEILLANCE FOR PROSTATE CANCER: NATURAL HISTORY OF PROSTATIC LESIONS AND UPGRADING RATES OVER TIME". Doctoral thesis, Università degli Studi di Milano, 2021. http://hdl.handle.net/2434/881234.
Pełny tekst źródłaBorkowetz, Angelika, Ivan Platzek, Marieta Toma, Theresa Renner, Roman Herout, Martin Baunacke, Michael Laniado i in. "Evaluation of Prostate Imaging Reporting and Data System Classification in the Prediction of Tumor Aggressiveness in Targeted Magnetic Resonance Imaging/Ultrasound-Fusion Biopsy". Karger, 2017. https://tud.qucosa.de/id/qucosa%3A70625.
Pełny tekst źródłaBesson, Florent. "Integrating PET-MR data for a multiparametric approach of tumour heterogeneity in non-small-cell lung cancer (NSCLC)". Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASS081.
Pełny tekst źródłaTumor heterogeneity is an important factor of progression and resistance to treatment. Multiparametric PET-MRI imaging offers unique opportunities to characterize biological cellular processes, but has never been evaluated at the regional level in Non-Small Cell Lung Cancer (NSCLC), the leading cause of oncological death. A simultaneous dynamic multiparametric 18F-FDG PET-MRI approach has been developed to this end. This approach required the “in-house” implementation of the reference absolute PET quantitative method of glucose metabolism (Sokoloff's tri-compartmental model); the development of a method for correcting geometric distortions in diffusion weighted imaging, validated on phantom and clinically tested; the phantom validation of quantitative MRI methods (T1/T2 relaxometry), also clinically tested; and the "in-house" implementation of the Tofts compartmental model (extended version) for the evaluation of tumor vascularization by dynamic perfusion MRI. The results of our work, performed at the regional intra-tumor level, illustrate the heterogeneity of the regional interlinks between glucose metabolism and vascularization in NSCLC, two fundamental biological hallmarks of tumor progression, and show that an unsupervised tumor partitioning by Gaussian mixture model, integrating all the PET-MRI biomarkers of this project, individualizes 3 types of supervoxels, whose biological signature can be predicted with 97% accuracy by 4 dominant PET-MRI biomarkers, revealed by metaheuristic machine learning methods
Jaouen, Tristan. "Caractérisation du cancer de la prostate de haut grade à l’IRM multiparamétrique à l’aide d’un système de diagnostic assisté par ordinateur basé sur la radiomique et utilisé comme lecteur autonome ou comme second lecteur". Electronic Thesis or Diss., Lyon, 2022. http://www.theses.fr/2022LYSE1140.
Pełny tekst źródłaWe developed a region of interest-based (ROIs) computer-aided diagnosis system (CAD) to characterize International Society of Urological Pathology grade (ISUP) ≥2 prostate cancers at multiparametric MRI (mp-MRI). Image parameters from two multi-vendor datasets of 265 pre-prostatectomy and 112 pre-biopsy MRIs were combined using logistic regression. The best models used the ADC 2nd percentile (ADC2) and normalized wash-in rate (WI) in the peripheral zone (PZ) and the ADC 25th percentile (ADC25) in the transition zone (TZ). They were combined in the CAD system. The CAD was retrospectively assessed on two multi-vendor datasets containing respectively 158 and 105 pre-biopsy MRIs from our institution (internal test dataset) and another institution (external test dataset). Two radiologists independently outlined lesions targeted at biopsy. The Prostate Imaging-Reporting and Data System version 2 (PI-RADSv2) score prospectively assigned at biopsy and the CAD score were compared to biopsy findings. At patient level, the areas under the Receiver Operating Characteristic curve (AUC) of the PI-RADSv2 score were 82% (95% CI: 74-87) and 85% (95% CI: 79-91) in the internal and external test datasets respectively. For both radiologists, the CAD score had similar AUC results in the internal (82%, 95% CI: 76-89, p=1; 84%, 95% CI: 78-91, p=1) and external (82%, 95% CI: 76-89, p=0.82; 86%, 95% CI: 79-93, p=1) test datasets. Combining PI-RADSv2 and CAD findings could have avoided 41-52% of biopsies while missing 6-10% of ISUP≥2 cancers. The CAD system confirmed its robustness showing good discrimination of ISUP ≥2 cancers in a multicentric study involving 22 different scanners with highly heterogeneous image protocols. In per patient analysis, the CAD and the PI-RADSv2 had similar AUC values (76%, 95% CI: 70-82 vs 79%, 95% CI: 73-86; p=0.34) and sensitivities (86%, 95% CI: 76-96 vs 89%, 95% CI: 79-98 for PI-RADSv2 ≥4). The specificity of the CAD (62%, 95% CI: 53-70 vs 49%, 95% CI: 39-59 for PI-RADSv2 ≥4) could be used to complement the PI-RADSv2 score and potentially avoid 50% of biopsies, while missing 13% of ISUP ≥2 cancers. These findings were very similar to those reported in the single center test cohorts. Given its robustness, the CAD could then be exploited in more specific applications. The CAD first provided good discrimination of ISUP ≥2 cancers in patients under Active Surveillance. Its AUC (80%, 95% CI: 74-86) was similar to that of the PI-RADS score prospectively assigned by specialized uro-radiologists at the time of biopsy (81%, 95% CI: 74-87; p=0.96). After dichotomization, the CAD was more specific than the PI-RADS ≥3 (p<0.001) and the PI-RADS ≥4 scores (p<0.001). It could offer a solution to select patients who could safely avoid confirmatory or follow-up biopsy during Active Surveillance (25%), while missing 5% of ISUP≥2 cancers. Finally, the CAD was tested with the pre-prostatectomy mp-MRIs of 56 Japanese patients, from a population which is geographically distant from its training population and which is of interest because of its low prostate cancer incidence and mortality. The CAD obtained an AUC similar to the PI-RADSv2 score assigned by an experience radiologist in the PZ (80%, 95% CI: 71-90 vs 80%, 95% CI: 71-89; p=0.886) and in the TZ (79%, 95% CI: 66-90 vs 93%, 95%CI: 82-96; p=0.051). These promising and robust results across heterogeneous datasets suggest that the CAD could be used in clinical routine as a second opinion reader to help select the patients who could safely avoid biopsy. This CAD may assist less experience readers in the characterization of prostate lesions
Duran, Audrey. "Intelligence artificielle pour la caractérisation du cancer de la prostate par agressivité en IRM multiparamétrique". Thesis, Lyon, 2022. http://theses.insa-lyon.fr/publication/2022LYSEI008/these.pdf.
Pełny tekst źródłaProstate cancer (PCa) is the most frequently diagnosed cancer in men in more than half the countries in the world and the fifth leading cause of cancer death among men in 2020. Diagnosis of PCa includes multiparametric magnetic resonance imaging acquisition (mp-MRI) - which combines T2 weighted (T2-w), diffusion weighted imaging (DWI) and dynamic contrast enhanced (DCE) sequences - prior to any biopsy. The joint analysis of these multimodal images is time demanding and challenging, especially when individual MR sequences yield conflicting findings. In addition, the sensitivity of MRI is low for less aggressive cancers and inter-reader reproducibility remains moderate at best. Moreover, visual analysis does not currently allow to determine the cancer aggressiveness, characterized by the Gleason score (GS). This is why computer-aided diagnosis (CAD) systems based on statistical learning models have been proposed in recent years, to assist radiologists in their diagnostic task, but the vast majority of these models focus on the binary detection of clinically significant (CS) lesions. The objective of this thesis is to develop a CAD system to detect and segment PCa on mp-MRI images but also to characterize their aggressiveness, by predicting the associated GS. In a first part, we present a supervised CAD system to segment PCa by aggressiveness from T2-w and ADC maps. This end-to-end multi-class neural network jointly segments the prostate gland and cancer lesions with GS group grading. The model was trained and validated with a 5-fold cross-validation on a heterogeneous series of 219 MRI exams acquired on three different scanners prior prostatectomy. Regarding the automatic GS group grading, Cohen’s quadratic weighted kappa coefficient (κ) is 0.418 ± 0.138, which is the best reported lesion-wise kappa for GS segmentation to our knowledge. The model has also encouraging generalization capacities on the PROSTATEx-2 public dataset. In a second part, we focus on a weakly supervised model that allows the inclusion of partly annotated data, where the lesions are identified by points only, for a consequent saving of time and the inclusion of biopsy-based databases. Regarding the automatic GS group grading on our private dataset, we show that we can approach performance achieved with the baseline fully supervised model while considering 6% of annotated voxels only for training. In the last part, we study the contribution of DCE MRI, a sequence often omitted as input to deep models, for the detection and characterization of PCa. We evaluate several ways to encode the perfusion from the DCE MRI information in a U-Net like architecture. Parametric maps derived from DCE MR exams are shown to positively impact segmentation and grading performance of PCa lesions
Gholizadeh, Neda. "Improved prostate tumour identification and delineation using multiparametric magnetic resonance imaging". Thesis, 2019. http://hdl.handle.net/1959.13/1411984.
Pełny tekst źródłaIn the last few decades, new imaging techniques based on magnetic resonance imaging (MRI) have been developed to improve early-stage detection and diagnosis of prostate cancer. MRI plays an important role in improving the current strategies for detection, delineation and risk stratification for prostate cancer. T2-weighted imaging (T2WI) is the primary imaging technique to evaluate anatomy and pathology in the prostate. However MRI images are expressed in arbitrary units and the absolute values contained within those images can vary due to differences between: MRI scanners; scanning techniques or other technical influences resulting in variations between patients or for the same patient when rescanned at different time points. In this thesis, statistically-based scale standardisation methods for two different centres have been used to address these problems. The results demonstrated a robust and reliable standardisation method for quantitative image assessment. The combination of conventional anatomical and advanced MRI is known as multiparametric MRI (mp-MRI). Mp-MRI is considered to have great potential in accurate prostate cancer diagnosis and characterization. Advanced MRI techniques provide physiological information about tissue to improve prostate cancer diagnosis and characterization. A recent advanced MRI technique is diffusion tensor imaging (DTI). Quantitative analysis of DTI could improve the detection and characterization of prostate cancer by providing additional information. However, difficulties in the processing and interpretation of DTI has limited the role of this imaging in clinical mp-MRI procedures. To evaluate the diagnostic performance of DTI, quantitative DTI and DTI tractography parameters with a focus on their impact in diagnostic and managing prostate cancer patients were extracted and evaluated. A fast imaging technique was utilised to decrease motion artefacts throughout the acquisition of prostate cancer patient data in an ethics approved MRI imaging study. The results demonstrated that DTI and DTI tractography have the potential to provide imaging biomarkers in the detection and characterization of prostate cancer in the peripheral zone. In addition, the utility of DTI in addition to T2-weighted imaging (T2WI) and diffusion weighted imaging (DWI) was assessed for the voxel based detection and prediction of peripheral zone dominant prostate tumours using supervised machine learning technique. Machine learning is a method of data analysis that automates analytical model building. These results demonstrated that DTI in combination with T2WI and DWI can improve the accuracy of prostate cancer detection and delineation. However, T2WI, DWI and DTI have limitations for central gland prostate cancer detection. It is well established that magnetic resonance spectroscopic imaging (MRSI) can provide valuable metabolic information for the non-invasive assessment of central gland prostate cancer. However, MRSI has been excluded from routine clinical mp-MRI in the most recently updated Prostate Imaging Reporting and Data System PI-RADS V2 guideline, probably due to moderate metabolite signal-to-noise ratio (SNR), relatively long acquisition times, the need for a high level of operator expertise, low spectral resolution (especially at 1.5T) and non-standardised acquisition and postprocessing techniques. In the most recent years, MRSI have undergone several technical improvements and show renewed promise for prostate tumour detection and localization. The most recently developed MRSI acquisition technique is known as a gradient-modulated offset-independent adiabatic (GOIA) semi-localized adiabatic selective refocusing (sLASER) (GOIA-sLASER) pulse sequence, which enables acquisition of metabolic data with a high spectral and spatial resolution without an endorectal coil at 3T in a clinically feasible scanning time (8-10 minutes). This thesis investigated the efficacy of in vivo MRSI using the GOIA-sLASER pulse sequence without an endorectal coil for detection and characterization of central gland prostate cancer. This results showed that the GOIA-sLASER sequence with an external phased-array coil allows for an accurate assessment of central gland prostate cancer. In addition, the diagnostic performance of mp-MRI, including T2WI, DWI and dynamic contrast with and without MRSI was assessed. These results demonstrated that MRSI using GOIA-sLASER can be a promising technique for non-invasive and accurate diagnosis of prostate cancer in the central zone. The performance of DTI and MRSI shown in this thesis illustrates potential advantages of non-invasive mp-MRI imaging in the course of prostate cancer detection and diagnosis.
Książki na temat "Multiparametric Magnetic Resonance Imaging (mp-MRI)"
Vilanova, Joan C., Violeta Catalá, Ferran Algaba i Oscar Laucirica. Atlas of Multiparametric Prostate MRI: With PI-RADS Approach and Anatomic-MRI-Pathological Correlation. Springer, 2017.
Znajdź pełny tekst źródłaVilanova, Joan C., Violeta Catalá, Ferran Algaba i Oscar Laucirica. Atlas of Multiparametric Prostate MRI: With PI-RADS Approach and Anatomic-MRI-Pathological Correlation. Springer, 2018.
Znajdź pełny tekst źródłaCzęści książek na temat "Multiparametric Magnetic Resonance Imaging (mp-MRI)"
Marino, Maria Adele, i Katja Pinker-Domenig. "Multiparametric Imaging: Cutting-Edge Sequences and Techniques Including Diffusion-Weighted Imaging, Magnetic Resonance Spectroscopy, and PET/CT or PET/MRI". W Breast Oncology: Techniques, Indications, and Interpretation, 283–320. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-42563-4_15.
Pełny tekst źródłaDahm, Philipp. "MRI-Targeted or Standard Biopsy for Prostate Cancer Diagnosis". W 50 Studies Every Urologist Should Know, 13–18. Oxford University Press, 2021. http://dx.doi.org/10.1093/med/9780190655341.003.0003.
Pełny tekst źródłaBogaert, Jan, Rolf Symons i Jeremy Wright. "CMR—basic principles". W The ESC Textbook of Cardiovascular Imaging, redaktorzy José Luis Zamorano, Jeroen J. Bax, Juhani Knuuti, Patrizio Lancellotti, Fausto J. Pinto, Bogdan A. Popescu i Udo Sechtem, 67–78. Oxford University Press, 2021. http://dx.doi.org/10.1093/med/9780198849353.003.0004.
Pełny tekst źródłaRaporty organizacyjne na temat "Multiparametric Magnetic Resonance Imaging (mp-MRI)"
Thomas Austin, Evan, Paul Kang, Chinedu Mmeje, Joseph Mashni, Mark Brenner, Phillip Koo i John C Chang. Validation of PI-RADS v2 Scores at Various Non-University Radiology Practices. Science Repository, grudzień 2021. http://dx.doi.org/10.31487/j.aco.2021.02.02.
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