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Larson DB, Bhargavan-Chatfield M, Tilkin M, Coombs L, Wald C. The Road Map for ACR Practice Accreditation for Radiology Artificial Intelligence. J Am Coll Radiol 2025; 22:586-592. [PMID: 40057886 DOI: 10.1016/j.jacr.2025.02.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2025] [Accepted: 02/02/2025] [Indexed: 05/04/2025]
Abstract
As the use of artificial intelligence (AI) continues to grow in radiology, it has become clear that its real-world performance often differs from that demonstrated in premarket testing, underscoring the need for robust quality management (QM) programs at local institutions. For decades, a key mechanism to ensure QM in radiology practices has been ACR accreditation. However, no such program currently exists for AI in radiology. As leaders of the ACR Commissions on Quality and Safety and Informatics, we are dedicated to establishing ACR accreditation for radiology AI. In this article, we outline our plan for this effort. ACR accreditation is a peer-reviewed process that evaluates radiology practices according to ACR Practice Parameters and Technical Standards, which are consensus-based guidelines aimed at improving care quality and reducing variability. ACR Practice Parameters focus on clinical aspects like patient management, and Technical Standards address the performance of imaging and treatment equipment. To support the development of this accreditation program, the ACR Recognized Center for Healthcare-AI (ARCH-AI) program has been established as a precursor to formal accreditation. ARCH-AI participants attest to meeting minimum criteria in areas such as governance, model selection, acceptance testing, monitoring, and management of locally developed models. Insights gained from ARCH-AI will inform the development of the formal accreditation program, which will culminate in ACR Council approval, currently anticipated in spring 2027. The College remains committed to fostering dialogue among members and stakeholders to ensure AI fulfills its promise of enhancing patient care safely and effectively.
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Affiliation(s)
- David B Larson
- Director of the AI Development and Evaluation Lab, Department of Radiology, Stanford University School of Medicine, Stanford, California; Chair, ACR Commission on Quality and Safety; Member of the ACR Board of Chancellors.
| | - Mythreyi Bhargavan-Chatfield
- Executive Vice President for Quality and Safety, American College of Radiology, Reston, Virginia. https://twitter.com/MythreyiC
| | - Michael Tilkin
- Chief Information Officer and Executive Vice President for Technology, American College of Radiology, Reston, Virginia
| | - Laura Coombs
- Vice President, Data Science and Informatics, American College of Radiology, Reston, Virginia
| | - Christoph Wald
- Senior Associate Consultant, Department of Radiology, Mayo Clinic, Rochester, Minnesota; Vice Chair, Board of Chancellors; Chair, Commission on Informatics, American College of Radiology; Adjunct Professor of Radiology, UMass TH Chan School of Medicine, Worcester, Massachusetts. https://twitter.com/waldchristoph
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Gress DA, Samei E, Frush DP, Pelzl CE, Fletcher JG, Mahesh M, Larson DB, Bhargavan-Chatfield M. Ranking the Relative Importance of Image Quality Features in CT by Consensus Survey. J Am Coll Radiol 2025; 22:66-75. [PMID: 39427722 DOI: 10.1016/j.jacr.2024.10.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2024] [Revised: 10/03/2024] [Accepted: 10/11/2024] [Indexed: 10/22/2024]
Abstract
OBJECTIVE This study sought to determine consensus opinions from subspecialty radiologists and imaging physicists on the relative importance of image quality features in CT. METHODS A prospective survey of subspecialty radiologists and medical physicists was conducted to collect consensus opinions on the relative importance of 10 image quality features: axial sharpness, blooming, contrast, longitudinal sharpness, low-contrast axial sharpness, metal artifact, motion, noise magnitude, noise texture, and streaking. The survey was first sent to subspecialty radiologists in volunteer leadership roles in the ACR and RSNA, thereafter relying on snowball sampling. Surveyed subspecialties were abdominal, cardiac, emergency, musculoskeletal, neuroradiology, pediatric, and thoracic radiology and medical physics. Individual respondents' ratings were normalized for calculation of mean normalized ratings and priority rankings for each feature within subspecialties. Also calculated were intraclass correlation coefficients across image quality features within subspecialties and analysis of variance across subspecialties within each feature. RESULTS Most subspecialties had moderate to excellent intraclass agreement. For every radiology subspecialty except musculoskeletal, motion was the most important image quality feature. There was agreement across subspecialties that axial sharpness and contrast are only moderately important. There was disagreement across subspecialties on the relative importance of noise magnitude. Blooming was highly important to cardiac radiologists, and noise texture was highly important to musculoskeletal radiologists. CONCLUSION Image quality preferences differ based on clinical tasks and challenges in each anatomical radiology subspecialty. CT image analysis and development of quantitative measures of quality and protocol optimization-and related policy initiatives-should be specific to radiology subspecialty.
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Affiliation(s)
- Dustin A Gress
- ACR, Reston, Virginia, and Department of Health Administration and Policy, George Mason University, College of Public Health, Fairfax, Virginia; Senior Advisor for Medical Physics, ACR Department of Quality and Safety.
| | - Ehsan Samei
- Department of Radiology, Carl E. Ravin Advanced Imaging Laboratories, Medical Physics Graduate Program, Duke University Medical Center, Durham, North Carolina; Chair, Board of Directors, American Association of Physicists in Medicine; Chief Imaging Physicist, Duke University Health System; Director, Center for Virtual Imaging Trials (Duke Radiology). https://twitter.com/EhsanSamei
| | - Donald P Frush
- Department of Radiology, Duke University Medical Center, Durham, North Carolina; Chair, Image Gently Alliance
| | - Casey E Pelzl
- Senior Economics and Health Services Research Analyst, Harvey L. Neiman Health Policy Institute, Reston, Virginia
| | - Joel G Fletcher
- Department of Radiology, Mayo Clinic, Rochester, Minnesota; Member, ACR Commission on Quality and Safety
| | - Mahadevappa Mahesh
- Johns Hopkins University School of Medicine, Baltimore, Maryland; Associate Editor, JACR Editorial Board; Member, ACR Commission on Publications and Lifelong Learning; Fellowship Chair, Maryland Radiological Society; President-Elect, American Association of Physicists in Medicine; Chair, Radiation Control Committee, Johns Hopkins Health Systems. https://twitter.com/mmahesh1
| | - David B Larson
- Executive Vice Chair, Department of Radiology, Stanford University School of Medicine, Stanford, California; Chair, ACR Commission on Quality and Safety; Member, ACR Board of Chancellors; Program Director, ACR Learning Network; Member, Board of Trustees, American Board of Radiology
| | - Mythreyi Bhargavan-Chatfield
- ACR, Reston, Virginia; Executive Vice President, ACR Department of Quality and Safety; Program Director, ACR Learning Network. https://twitter.com/MythreyiC
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Pittman SM, Zacharias-Andrews K, Garcia Tomkins K, Bhargavan-Chatfield M, Larson DB. The ACR Mammography Positioning Improvement Collaborative: A Multicenter Improvement Program Within a Learning Network Framework. J Am Coll Radiol 2024; 21:1755-1764. [PMID: 38950833 DOI: 10.1016/j.jacr.2024.06.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 06/06/2024] [Accepted: 06/12/2024] [Indexed: 07/03/2024]
Abstract
PURPOSE/OBJECTIVE To share the experience and results of the first cohort of the ACR Mammography Positioning Improvement Collaborative, in which participating sites aimed to increase the mean percentage of screening mammograms meeting the established positioning criteria to 85% or greater and show at least modest evidence of improvement at each site by the end of the improvement program. METHODS The sites comprising the first cohort of the collaborative were selected on the basis of strength of local leadership support, intra-organizational relationships, access to data and analytic support, and experience with quality improvement initiatives. During the improvement program, participating sites organized their teams, developed goals, gathered data, evaluated their current state, identified key drivers and root causes of their problems, and developed and tested interventions. A standardized image quality scoring system was also established. The impact of the interventions implemented at each site was assessed by tracking the percentage of screening mammograms meeting overall passing criteria over time. RESULTS Six organizations were selected to participate as the first cohort, beginning with participation in the improvement program. Interventions developed and implemented at each site during the program resulted in improvement in the average percentage of screening mammograms meeting overall passing criteria per week from a collaborative mean of 51% to 86%, with four of six sites meeting or exceeding the target mean performance of 85% by the end of the improvement program. Afterward, all respondents to the postprogram survey indicated that the program was a positive experience. CONCLUSION Using a structured improvement program within a learning network framework, the first cohort of the collaborative demonstrated that improvement in mammography positioning performance can be achieved at multiple sites simultaneously and validated the hypothesis that local sites' shared experiences, insights, and learnings would not only improve performance but would also build a community of improvers collaborating to create the best experience for technologists, staff, and patients.
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Affiliation(s)
- Sarah M Pittman
- Breast Imaging Fellowship Program Director, Department of Radiology, Stanford University School of Medicine, Stanford, California; Physician Leader, Mammography Positioning Improvement Collaborative, American College of Radiology Learning Network.
| | | | - Kandice Garcia Tomkins
- Improvement Consultant, Department of Radiology, Stanford University School of Medicine, Stanford, California
| | | | - David B Larson
- Executive Vice Chair, Department of Radiology, Stanford University School of Medicine, Stanford, California; Chair, ACR Commission on Quality and Safety; Member of the ACR Board of Chancellors. https://twitter.com/larson_david_b
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Davenport MS. Attempting to Improve Prostate MR Image Quality at Scale Through the ACR Learning Network. J Am Coll Radiol 2024; 21:1475-1476. [PMID: 38704058 DOI: 10.1016/j.jacr.2024.03.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Accepted: 03/22/2024] [Indexed: 05/06/2024]
Affiliation(s)
- Matthew S Davenport
- Departments of Radiology and Urology, Michigan Medicine, Ann Arbor, Michigan.
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Purysko AS, Zacharias-Andrews K, Tomkins KG, Turkbey IB, Giganti F, Bhargavan-Chatfield M, Larson DB. Improving Prostate MR Image Quality in Practice-Initial Results From the ACR Prostate MR Image Quality Improvement Collaborative. J Am Coll Radiol 2024; 21:1464-1474. [PMID: 38729590 DOI: 10.1016/j.jacr.2024.04.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 04/06/2024] [Accepted: 04/13/2024] [Indexed: 05/12/2024]
Abstract
OBJECTIVE Variability in prostate MRI quality is an increasingly recognized problem that negatively affects patient care. This report aims to describe the results and key learnings of the first cohort of the ACR Learning Network Prostate MR Image Quality Improvement Collaborative. METHODS Teams from five organizations in the United States were trained on a structured improvement method. After reaching a consensus on image quality and auditing their images using the Prostate Imaging Quality (PI-QUAL) system, teams conducted a current state analysis to identify barriers to obtaining high-quality images. Through plan-do-study-act cycles involving frontline staff, each site designed and tested interventions targeting image quality key drivers. The percentage of examinations meeting quality criteria (ie, PI-QUAL score ≥4) was plotted on a run chart, and project progress was reviewed in weekly meetings. At the collaborative level, the goal was to increase the percentage of examinations with PI-QUAL ≥4 to at least 85%. RESULTS Across 2,380 examinations audited, the mean weekly rates of prostate MR examinations meeting image quality criteria increased from 67% (range: 60%-74%) at baseline to 87% (range: 80%-97%) upon program completion. The most commonly employed interventions were MR protocol adjustments, development and implementation of patient preparation instructions, personnel training, and development of an auditing process mechanism. CONCLUSION A learning network model, in which organizations share knowledge and work together toward a common goal, can improve prostate MR image quality at multiple sites simultaneously. The inaugural cohort's key learnings provide a road map for improvement on a broader scale.
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Affiliation(s)
- Andrei S Purysko
- Head, Section of Abdominal Imaging, Imaging Institute, Cleveland Clinic, Cleveland, Ohio; Physician Leader, Prostate MR Image Quality Improvement Collaborative, American College of Radiology Learning Network.
| | | | | | - Ismail Baris Turkbey
- Head, Magnetic Resonance Imaging Section and the Artificial Intelligence Resource, Molecular Imaging Branch, Molecular Imaging Program, National Cancer Institute, Bethesda, Maryland. https://twitter.com/radiolobt
| | - Francesco Giganti
- Department of Radiology, University College London Hospital NHS Foundation Trust, London, UK; Division of Surgery & Interventional Science, University College London, London, UK. https://twitter.com/giga_fra
| | - Mythreyi Bhargavan-Chatfield
- Executive Vice President for Quality and Safety, American College of Radiology, Reston, Virginia. https://twitter.com/MythreyiC
| | - David B Larson
- Senior Vice Chair for Strategy and Clinical Operations, Department of Radiology, Stanford University School of Medicine, Stanford, California; Chair, Commission on Quality and Safety, American College of Radiology. https://twitter.com/larson_david_b
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Larson DB. A Vision for Global CT Radiation Dose Optimization. J Am Coll Radiol 2024; 21:1311-1317. [PMID: 38302037 DOI: 10.1016/j.jacr.2024.01.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 01/11/2024] [Accepted: 01/17/2024] [Indexed: 02/03/2024]
Abstract
The topic of CT radiation dose management is receiving renewed attention since the recent approval by CMS for new CT dose measures. Widespread variation in CT dose persists in practices across the world, suggesting that current dose optimization techniques are lacking. The author outlines a proposed strategy for facilitating global CT radiation dose optimization. CT radiation dose optimization can be defined as the routine use of CT scan parameters that consistently produce images just above the minimum threshold of acceptable image quality for a given clinical indication, accounting for relevant patient characteristics, using the most dose-efficient techniques available on the scanner. To accomplish this, an image quality-based target dose must be established for every protocol; for nonhead CT applications, these target dose values must be expressed as a function of patient size. As variation in outcomes is reduced, the dose targets can be decreased to more closely approximate the minimum image quality threshold. Maintaining CT radiation dose optimization requires a process control program, including measurement, evaluation, feedback, and control. This is best accomplished by local teams made up of radiologists, medical physicists, and technologists, supported with protected time and needed tools, including analytics and protocol management applications. Other stakeholders critical to facilitating CT radiation dose management include researchers, funding agencies, industry, regulators, accreditors, payers, and the ACR. Analogous coordinated approaches have transformed quality in other industries and can be the mechanism for achieving the universal goal of CT radiation dose optimization.
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Affiliation(s)
- David B Larson
- Executive Vice Chair, Department of Radiology, Stanford University School of Medicine, Stanford, California; and Chair, ACR Commission on Quality and Safety.
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Carbonell C, Adegbulugbe A, Cheung W, Ruff P. Barriers and Challenges to Implementing a Quality Improvement Program: Political and Administrative Challenges. JCO Glob Oncol 2024; 10:e2300455. [PMID: 38935883 DOI: 10.1200/go.23.00455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 03/17/2024] [Accepted: 04/30/2024] [Indexed: 06/29/2024] Open
Abstract
Quality improvement (QI) programs have rapidly grown in health care over recent years. Despite increasing evidence of successful QI initiatives resulting in improved outcomes, the adoption and implementation of QI programs remain a challenge worldwide. This paper briefly describes political and administrative barriers that impede the implementation of QI programs, including political and ideological factors, socioeconomic and educational barriers, and barriers related to data collection, privacy, and security. Key political and administrative barriers identified include resource limitations due to inadequate public funding, stringent laws, and change resistance. Potential solutions include support and commitment from regional and national authorities, consultation of all involved parties during QI program development, and financial incentives. The barrier of limited resources is starker among low- and middle-income countries (LMICs) compared with high-income countries (HICs) due to the absence of adequate infrastructure, personnel equipped with QI-oriented skills, and analytical technology. Solutions that have facilitated QI programs in some LMICs include outreach and collaboration with other health centers and established QI programs in HICs. The lack of QI-specific training and education in medical curricula challenges QI implementation but can be mitigated through the provision of QI promotion webinars, QI-specific project opportunities, and formalized QI training modules. Finally, barriers related to data collection, privacy, and security include laws hindering the availability of quality data, inefficient data collection and processes, and outdated clinical information systems. Access to high-quality data, organized record-keeping, and alignment of data collection processes will help alleviate these barriers to QI program implementation. The multidimensional nature of these barriers means that proposed solutions will require coordination from multiple stakeholders, government support, and leaders across multiple fields.
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Affiliation(s)
- Chantelle Carbonell
- Department of Oncology, Cumming School of Medicine, University of Calgary, Calgary, Canada
- Department of Community Health Sciences, University of Calgary, Calgary, Canada
| | - Abisola Adegbulugbe
- Department of Oncology, Cumming School of Medicine, University of Calgary, Calgary, Canada
- Department of Community Health Sciences, University of Calgary, Calgary, Canada
| | - Winson Cheung
- Department of Oncology, Cumming School of Medicine, University of Calgary, Calgary, Canada
- Department of Community Health Sciences, University of Calgary, Calgary, Canada
| | - Paul Ruff
- Emeritus Professor, Division of Medical Oncology, University of Witwatersrand Faculty of Health Sciences, Johannesburg, South Africa
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Robertson SH, Owenby E, Beasley C, Wall L, Gray B, Boru I, Kalisz KR, Kruse DE, Marin D, Thomas SP, Macdonald EB, Purysko AS, Gupta RT. Optimization of non-endorectal prostate MR image quality using PI-QUAL: A multidisciplinary team approach. Eur J Radiol 2023; 166:110998. [PMID: 37506475 DOI: 10.1016/j.ejrad.2023.110998] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 07/06/2023] [Accepted: 07/19/2023] [Indexed: 07/30/2023]
Abstract
PURPOSE To evaluate the utility of the PI-QUAL score in assessing protocol changes aimed to improve image quality from a non-endorectal coil prostate MR imaging protocol during a 9-month quality improvement (QI) project and to quantify the inter-reader agreement of PI-QUAL scores between radiologists, technologists, and physicists. METHODS This retrospective study audited 1,012 multiparametric prostate MRI examinations as part of a national QI project according to the PI-QUAL standard. PI-QUAL scores were used to inform MR protocol changes. Following the project, 4 radiologists, 2 technologists, and 1 medical physicist collectively audited an additional set of 150 examinations to identify statistical improvements in image quality using the two-tailed Wilcoxon rank sum test. The improvements due to individual protocol changes were assessed among subsets of the 1,012 examinations which compared examinations occurring before and after the isolated protocol change. Inter-reader variability was assessed using the percent majority agreement and the average standard deviation of PI-QUAL scores between evaluators. RESULTS During this QI project, PI-QUAL scores improved from 3.67 ± 0.75 to 4.16 ± 0.59 (p < 0.01) after implementing a series of protocol changes. Among a subset of 451 cases, we found that adopting R/L rather than A/P phase encoding reduced distortion in diffusion-weighted imaging (DW) from 21.6% (41/190 A/P phase encoded cases) to 11.5% (30/261 R/L phase encoded cases) (p < 0.01). Similarly, in the same 451 cases, adopting R/L phase encoding in T2WI reduced breathing motion artifacts from 34.6% (94/272 A/P phase encoding cases) to 12.8% (23/179 R/L phase encoding cases) (p < 0.01). DWI wraparound artifact was mitigated by employing a full-pelvis shim and enabling the abdomen shim option. The occurrence of low signal-to-noise ratio was reduced from 19.4% (19/98 cases without a weight-based threshold) to 6.3% (10/160) by instituting a weight-based threshold for using an endorectal coil (p < 0.01). The percent majority agreement was similar between radiologists, technologists and physicists, and all evaluators combined (72%, 77%, and 67%, respectively). CONCLUSIONS PI-QUAL can evaluate image quality changes resulting from protocol optimizations at both the exam- and series-levels. With training, radiologists, technologists, and physicists can perform PI-QUAL scoring with similar performance. Broadening the scope of the quality improvement team can result in meaningful and lasting change.
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Affiliation(s)
- Scott H Robertson
- Department of Radiology, Duke University Medical Center, Durham, NC, United States.
| | - Erica Owenby
- Department of Radiology, Duke University Medical Center, Durham, NC, United States.
| | - Christopher Beasley
- Department of Radiology, Duke University Medical Center, Durham, NC, United States.
| | - Lisa Wall
- Department of Radiology, Duke University Medical Center, Durham, NC, United States
| | - Bradley Gray
- Department of Radiology, Duke University Medical Center, Durham, NC, United States.
| | - Issack Boru
- Department of Radiology, Duke University Medical Center, Durham, NC, United States.
| | - Kevin R Kalisz
- Department of Radiology, Duke University Medical Center, Durham, NC, United States.
| | - Danielle E Kruse
- Department of Radiology, Duke University Medical Center, Durham, NC, United States.
| | - Daniele Marin
- Department of Radiology, Duke University Medical Center, Durham, NC, United States.
| | - Sarah P Thomas
- Department of Radiology, Duke University Medical Center, Durham, NC, United States.
| | - Erin B Macdonald
- Department of Radiology, Duke University Medical Center, Durham, NC, United States.
| | - Andrei S Purysko
- Section of Abdominal Imaging, Imaging Institute, Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, OH, United States.
| | - Rajan T Gupta
- Department of Radiology, Duke University Medical Center, Durham, NC, United States; Department of Surgery, Division of Urology, Duke University Medical Center, Durham, NC, United States; Duke Cancer Institute Center for Prostate and Urologic Cancers, Duke University Medical Center, Durham, NC, United States.
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Fütterer JJ, Tempany C. Prostate MRI and image quality: The radiologist's perspective. Eur J Radiol 2023; 165:110930. [PMID: 37364484 PMCID: PMC10466385 DOI: 10.1016/j.ejrad.2023.110930] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 06/12/2023] [Indexed: 06/28/2023]
Abstract
Multiparametric MRI (mpMRI) of the prostate plays an important role in the healthcare pathway of prostate cancer. The implementation of the guidelines resulted in an almost vertical increase in the number of prostate MRI examinations. High image quality is important in the diagnostic pathway of prostate cancer. Standardization of prostate MRI quality using objective and pre-defined criteria is of utmost importance.
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Affiliation(s)
| | - Clare Tempany
- Department of Radiology, Harvard Medical School, United States
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Purysko AS, Tempany C, Macura KJ, Turkbey B, Rosenkrantz AB, Gupta RT, Attridge L, Hernandez D, Garcia-Tomkins K, Bhargavan-Chatfield M, Weinreb J, Larson DB. American College of Radiology initiatives on prostate magnetic resonance imaging quality. Eur J Radiol 2023; 165:110937. [PMID: 37352683 PMCID: PMC10461171 DOI: 10.1016/j.ejrad.2023.110937] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 06/14/2023] [Accepted: 06/16/2023] [Indexed: 06/25/2023]
Abstract
Magnetic resonance imaging (MRI) has become integral to diagnosing and managing patients with suspected or confirmed prostate cancer. However, the benefits of utilizing MRI can be hindered by quality issues during imaging acquisition, interpretation, and reporting. As the utilization of prostate MRI continues to increase in clinical practice, the variability in MRI quality and how it can negatively impact patient care have become apparent. The American College of Radiology (ACR) has recognized this challenge and developed several initiatives to address the issue of inconsistent MRI quality and ensure that imaging centers deliver high-quality patient care. These initiatives include the Prostate Imaging Reporting and Data System (PI-RADS), developed in collaboration with an international panel of experts and members of the European Society of Urogenital Radiology (ESUR), the Prostate MR Image Quality Improvement Collaborative, which is part of the ACR Learning Network, the ACR Prostate Cancer MRI Center Designation, and the ACR Appropriateness Criteria. In this article, we will discuss the importance of these initiatives in establishing quality assurance and quality control programs for prostate MRI and how they can improve patient outcomes.
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Affiliation(s)
- Andrei S Purysko
- Section of Abdominal Imaging and Nuclear Radiology Department, Imaging Institute, Cleveland Clinic, Cleveland, OH, USA.
| | - Clare Tempany
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Katarzyna J Macura
- The Russel H. Morgan Department of Radiology and Radiological Science, The James Buchanan Brady Urological Institute, Johns Hopkins University, Baltimore, MD, USA
| | - Baris Turkbey
- Molecular Imaging Branch, NCI, NIH, Bethesda, MD, USA
| | | | - Rajan T Gupta
- Departments of Radiology and Surgery and Duke Cancer Institute Center for Prostate and Urologic Cancers, Duke University Medical Center, Durham, NC, USA
| | | | | | | | | | - Jeffrey Weinreb
- Department of Radiology, Yale School of Medicine, New Haven, CT, USA
| | - David B Larson
- Department of Radiology, Stanford University, Stanford, CA, USA
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11
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Carlos RC. Risk and System Change. J Am Coll Radiol 2023; 20:289. [PMID: 36922101 DOI: 10.1016/j.jacr.2023.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Indexed: 03/16/2023]
Affiliation(s)
- Ruth C Carlos
- Department of Radiology, University of Michigan, Ann Arbor, Michigan; and is the Editor-in-Chief of JACR.
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