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Perchik JD, Smith AD, Elkassem AA, Park JM, Rothenberg SA, Tanwar M, Yi PH, Sturdivant A, Tridandapani S, Sotoudeh H. Artificial Intelligence Literacy: Developing a Multi-institutional Infrastructure for AI Education. Acad Radiol 2023; 30:1472-1480. [PMID: 36323613 DOI: 10.1016/j.acra.2022.10.002] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Revised: 09/23/2022] [Accepted: 10/01/2022] [Indexed: 11/17/2022]
Abstract
RATIONALE AND OBJECTIVES To evaluate the effectiveness of an artificial intelligence (AI) in radiology literacy course on participants from nine radiology residency programs in the Southeast and Mid-Atlantic United States. MATERIALS AND METHODS A week-long AI in radiology course was developed and included participants from nine radiology residency programs in the Southeast and Mid-Atlantic United States. Ten 30 minutes lectures utilizing a remote learning format covered basic AI terms and methods, clinical applications of AI in radiology by four different subspecialties, and special topics lectures on the economics of AI, ethics of AI, algorithm bias, and medicolegal implications of AI in medicine. A proctored hands-on clinical AI session allowed participants to directly use an FDA cleared AI-assisted viewer and reporting system for advanced cancer. Pre- and post-course electronic surveys were distributed to assess participants' knowledge of AI terminology and applications and interest in AI education. RESULTS There were an average of 75 participants each day of the course (range: 50-120). Nearly all participants reported a lack of sufficient exposure to AI in their radiology training (96.7%, 90/93). Mean participant score on the pre-course AI knowledge evaluation was 8.3/15, with a statistically significant increase to 10.1/15 on the post-course evaluation (p= 0.04). A majority of participants reported an interest in continued AI in radiology education in the future (78.6%, 22/28). CONCLUSION A multi-institutional AI in radiology literacy course successfully improved AI education of participants, with the majority of participants reporting a continued interest in AI in radiology education in the future.
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Affiliation(s)
- J D Perchik
- Department of Diagnostic Radiology, University of Alabama at Birmingham, Birmingham, Alabama.
| | - A D Smith
- Department of Diagnostic Radiology, University of Alabama at Birmingham, Birmingham, Alabama
| | - A A Elkassem
- Department of Diagnostic Radiology, University of Alabama at Birmingham, Birmingham, Alabama
| | - J M Park
- Department of Diagnostic Radiology, University of Alabama at Birmingham, Birmingham, Alabama
| | - S A Rothenberg
- Department of Diagnostic Radiology, University of Alabama at Birmingham, Birmingham, Alabama
| | - M Tanwar
- Department of Diagnostic Radiology, University of Alabama at Birmingham, Birmingham, Alabama
| | - P H Yi
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Intelligent Imaging Center, University of Maryland School of Medicine, Baltimore, Maryland
| | - A Sturdivant
- University of Alabama at Birmingham Heersink School of Medicine
| | - S Tridandapani
- Department of Diagnostic Radiology, University of Alabama at Birmingham, Birmingham, Alabama
| | - H Sotoudeh
- Department of Diagnostic Radiology, University of Alabama at Birmingham, Birmingham, Alabama
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Sorace AG, Elkassem AA, Galgano SJ, Lapi SE, Larimer BM, Partridge SC, Quarles CC, Reeves K, Napier TS, Song PN, Yankeelov TE, Woodard S, Smith AD. Imaging for Response Assessment in Cancer Clinical Trials. Semin Nucl Med 2020; 50:488-504. [PMID: 33059819 PMCID: PMC7573201 DOI: 10.1053/j.semnuclmed.2020.05.001] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
The use of biomarkers is integral to the routine management of cancer patients, including diagnosis of disease, clinical staging and response to therapeutic intervention. Advanced imaging metrics with computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) are used to assess response during new drug development and in cancer research for predictive metrics of response. Key components and challenges to identifying an appropriate imaging biomarker are selection of integral vs integrated biomarkers, choosing an appropriate endpoint and modality, and standardization of the imaging biomarkers for cooperative and multicenter trials. Imaging biomarkers lean on the original proposed quantified metrics derived from imaging such as tumor size or longest dimension, with the most commonly implemented metrics in clinical trials coming from the Response Evaluation Criteria in Solid Tumors (RECIST) criteria, and then adapted versions such as immune-RECIST (iRECIST) and Positron Emission Tomography Response Criteria in Solid Tumors (PERCIST) for immunotherapy response and PET imaging, respectively. There have been many widely adopted biomarkers in clinical trials derived from MRI including metrics that describe cellularity and vascularity from diffusion-weighted (DW)-MRI apparent diffusion coefficient (ADC) and Dynamic Susceptibility Contrast (DSC) or dynamic contrast enhanced (DCE)-MRI (Ktrans, relative cerebral blood volume (rCBV)), respectively. Furthermore, Fluorodexoyglucose (FDG), fluorothymidine (FLT), and fluoromisonidazole (FMISO)-PET imaging, which describe molecular markers of glucose metabolism, proliferation and hypoxia have been implemented into various cancer types to assess therapeutic response to a wide variety of targeted- and chemotherapies. Recently, there have been many functional and molecular novel imaging biomarkers that are being developed that are rapidly being integrated into clinical trials (with anticipation of being implemented into clinical workflow in the future), such as artificial intelligence (AI) and machine learning computational strategies, antibody and peptide specific molecular imaging, and advanced diffusion MRI. These include prostate-specific membrane antigen (PSMA) and trastuzumab-PET, vascular tumor burden extracted from contrast-enhanced CT, diffusion kurtosis imaging, and CD8 or Granzyme B PET imaging. Further excitement surrounds theranostic procedures such as the combination of 68Ga/111In- and 177Lu-DOTATATE to use integral biomarkers to direct care and personalize therapy. However, there are many challenges in the implementation of imaging biomarkers that remains, including understand the accuracy, repeatability and reproducibility of both acquisition and analysis of these imaging biomarkers. Despite the challenges associated with the biological and technical validation of novel imaging biomarkers, a distinct roadmap has been created that is being implemented into many clinical trials to advance the development and implementation to create specific and sensitive novel imaging biomarkers of therapeutic response to continue to transform medical oncology.
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Affiliation(s)
- Anna G Sorace
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL; Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, AL; O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL.
| | - Asser A Elkassem
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL
| | - Samuel J Galgano
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL; O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL
| | - Suzanne E Lapi
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL; O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL; Department of Chemistry, University of Alabama at Birmingham, Birmingham, AL
| | - Benjamin M Larimer
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL; O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL
| | | | - C Chad Quarles
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ
| | - Kirsten Reeves
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL; Cancer Biology, University of Alabama at Birmingham, Birmingham, AL
| | - Tiara S Napier
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL; Cancer Biology, University of Alabama at Birmingham, Birmingham, AL
| | - Patrick N Song
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL
| | - Thomas E Yankeelov
- Department of Biomedical Engineering, University of Texas at Austin, Austin, TX; Department of Diagnostic Medicine, University of Texas at Austin, Austin, TX; Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, TX
| | - Stefanie Woodard
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL
| | - Andrew D Smith
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL; O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL
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