Pub Date : 2024-08-06DOI: 10.1016/j.jacr.2024.07.019
Kenneth C Wang, Justin R Ryan, Leonid Chepelev, Nicole Wake, Edward P Quigley, Lumarie Santiago, Adam Wentworth, Amy Alexander, Jonathan M Morris, Dominik Fleischmann, David H Ballard, Prashanth Ravi, Jeffrey D Hirsch, Gregory M Sturgeon, Yu-Hui Huang, Summer J Decker, Natalia von Windheim, Robert S Pugliese, Ronald V Hidalgo, Pushpak Patel, Joseb Colon, Florian M Thieringer, Frank J Rybicki
Purpose: The aim of this study was to report data from the first 3 years of operation of the RSNA-ACR 3D Printing Registry.
Methods: Data from June 2020 to June 2023 were extracted, including demographics, indications, workflow, and user assessments. Clinical indications were stratified by 12 organ systems. Imaging modalities, printing technologies, and numbers of parts per case were assessed. Effort data were analyzed, dividing staff members into provider and nonprovider categories. The opinions of clinical users were evaluated using a Likert scale questionnaire, and estimates of procedure time saved were collected.
Results: A total of 20 sites and 2,637 cases were included, consisting of 1,863 anatomic models and 774 anatomic guides. Mean patient ages for models and guides were 42.4 ± 24.5 years and 56.3 ± 18.5 years, respectively. Cardiac models were the most common type of model (27.2%), and neurologic guides were the most common type of guide (42.4%). Material jetting, vat photopolymerization, and material extrusion were the most common printing technologies used overall (85.6% of all cases). On average, providers spent 92.4 min and nonproviders spent 335.0 min per case. Providers spent most time on consultation (33.6 min), while nonproviders focused most on segmentation (148.0 min). Confidence in treatment plans increased after using 3-D printing (P < .001). Estimated procedure time savings for 155 cases was 40.5 ± 26.1 min.
Conclusions: Three-dimensional printing is performed at health care facilities for many clinical indications. The registry provides insight into the technologies and workflows used to create anatomic models and guides, and the data show clinical benefits from 3-D printing.
目的：报告 RSNA-ACR 3D 打印注册中心运行头三年的数据：报告 RSNA-ACR 3D 打印注册中心运行头三年的数据：方法：提取 2020 年 6 月至 2023 年 6 月的数据，包括人口统计学、适应症、工作流程和用户评估。临床适应症按 12 个器官系统进行了分层。对成像方式、打印技术和每个病例的部件数量进行了评估。对工作量数据进行了分析，将工作人员分为提供者和非提供者两类。通过李克特量表问卷评估了临床用户的意见，并收集了节省的手术时间估计数：结果：共纳入了 20 个地点和 2,637 个病例，包括 1,863 个解剖模型和 774 个解剖指南。模型和导板的患者平均年龄分别为（42.4 ± 24.5）岁和（56.3 ± 18.5）岁。心脏模型是最常见的模型类型（27.2%），神经导板是最常见的导板类型（42.4%）。材料喷射、大桶光聚合和材料挤压是最常用的打印技术（占所有病例的 85.6%）。每个病例，医疗服务提供者平均花费 92.4 分钟，非医疗服务提供者平均花费 335.0 分钟。医疗服务提供者花费最多的时间是咨询（33.6 分钟），而非医疗服务提供者花费最多的时间是分割（148.0 分钟）。使用 3D 打印技术后，对治疗计划的信心增加了（p 结论：医疗机构在许多临床适应症中都使用了 3D 打印技术。登记册提供了对用于创建解剖模型和指南的技术和工作流程的深入了解，数据显示了 3D 打印带来的临床益处。
{"title":"Demographics, Utilization, Workflow, and Outcomes Based on Observational Data From the RSNA-ACR 3D Printing Registry.","authors":"Kenneth C Wang, Justin R Ryan, Leonid Chepelev, Nicole Wake, Edward P Quigley, Lumarie Santiago, Adam Wentworth, Amy Alexander, Jonathan M Morris, Dominik Fleischmann, David H Ballard, Prashanth Ravi, Jeffrey D Hirsch, Gregory M Sturgeon, Yu-Hui Huang, Summer J Decker, Natalia von Windheim, Robert S Pugliese, Ronald V Hidalgo, Pushpak Patel, Joseb Colon, Florian M Thieringer, Frank J Rybicki","doi":"10.1016/j.jacr.2024.07.019","DOIUrl":"10.1016/j.jacr.2024.07.019","url":null,"abstract":"<p><strong>Purpose: </strong>The aim of this study was to report data from the first 3 years of operation of the RSNA-ACR 3D Printing Registry.</p><p><strong>Methods: </strong>Data from June 2020 to June 2023 were extracted, including demographics, indications, workflow, and user assessments. Clinical indications were stratified by 12 organ systems. Imaging modalities, printing technologies, and numbers of parts per case were assessed. Effort data were analyzed, dividing staff members into provider and nonprovider categories. The opinions of clinical users were evaluated using a Likert scale questionnaire, and estimates of procedure time saved were collected.</p><p><strong>Results: </strong>A total of 20 sites and 2,637 cases were included, consisting of 1,863 anatomic models and 774 anatomic guides. Mean patient ages for models and guides were 42.4 ± 24.5 years and 56.3 ± 18.5 years, respectively. Cardiac models were the most common type of model (27.2%), and neurologic guides were the most common type of guide (42.4%). Material jetting, vat photopolymerization, and material extrusion were the most common printing technologies used overall (85.6% of all cases). On average, providers spent 92.4 min and nonproviders spent 335.0 min per case. Providers spent most time on consultation (33.6 min), while nonproviders focused most on segmentation (148.0 min). Confidence in treatment plans increased after using 3-D printing (P < .001). Estimated procedure time savings for 155 cases was 40.5 ± 26.1 min.</p><p><strong>Conclusions: </strong>Three-dimensional printing is performed at health care facilities for many clinical indications. The registry provides insight into the technologies and workflows used to create anatomic models and guides, and the data show clinical benefits from 3-D printing.</p>","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141908574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-06DOI: 10.1016/j.jacr.2024.07.015
Caitlin Coughlin, Orit Ariel Glenn
{"title":"Patient-Friendly Summary of the ACR Appropriateness Criteria®: Cerebrovascular Diseases-Stroke and Stroke-Related Conditions.","authors":"Caitlin Coughlin, Orit Ariel Glenn","doi":"10.1016/j.jacr.2024.07.015","DOIUrl":"10.1016/j.jacr.2024.07.015","url":null,"abstract":"","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141908575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-06DOI: 10.1016/j.jacr.2024.07.016
Mariam E Dogar, Brittany M Dacier, Javier Mora, Showly Nicholson, Jinel A Scott, Adil H Haider, Efren J Flores, Gezzer Ortega
{"title":"Applying Cultural Dexterity to Patients in Radiology.","authors":"Mariam E Dogar, Brittany M Dacier, Javier Mora, Showly Nicholson, Jinel A Scott, Adil H Haider, Efren J Flores, Gezzer Ortega","doi":"10.1016/j.jacr.2024.07.016","DOIUrl":"10.1016/j.jacr.2024.07.016","url":null,"abstract":"","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141908573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-05DOI: 10.1016/j.jacr.2024.07.013
Jeshwanth Mohan, Samantha L Heller
{"title":"Patient-Friendly Summary of the ACR Appropriateness Criteria®: Radiologic Management of Iliofemoral Venous Thrombosis.","authors":"Jeshwanth Mohan, Samantha L Heller","doi":"10.1016/j.jacr.2024.07.013","DOIUrl":"10.1016/j.jacr.2024.07.013","url":null,"abstract":"","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141903876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-02DOI: 10.1016/j.jacr.2024.07.012
Sania Choudhary, Sonya Bhole
{"title":"Patient-Friendly Summary of the ACR Appropriateness Criteria®: Occupational Lung Diseases.","authors":"Sania Choudhary, Sonya Bhole","doi":"10.1016/j.jacr.2024.07.012","DOIUrl":"10.1016/j.jacr.2024.07.012","url":null,"abstract":"","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141891136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-02DOI: 10.1016/j.jacr.2024.07.011
Ronald Chow, Shaakir Hasan, Ajay Zheng, Chenxi Gao, Gilmer Valdes, Francis Yu, Arpit Chhabra, Srinivas Raman, J Isabelle Choi, Haibo Lin, Charles B Simone
The aim of this study is to assess the accuracy of Chat Generative Pretrained Transformer (ChatGPT) in response to oncology examination questions in the setting of one-shot learning. Consecutive national radiation oncology in-service multiple-choice examinations were collected and inputted into ChatGPT 4o and ChatGPT 3.5 to determine ChatGPT's answers. ChatGPT's answers were then compared with the answer keys to determine whether ChatGPT correctly or incorrectly answered each question and to determine if improvements in responses were seen with the newer ChatGPT version. A total of 600 consecutive questions were inputted into ChatGPT. ChatGPT 4o answered 72.2% questions correctly, whereas 3.5 answered 53.8% questions correctly. There was a significant difference in performance by question category (P < .01). ChatGPT performed poorer with respect to knowledge of landmark studies and treatment recommendations and planning. ChatGPT is a promising technology, with the latest version showing marked improvement. Although it still has limitations, with further evolution, it may be considered a reliable resource for medical training and decision making in the oncology space.
{"title":"The Accuracy of Artificial Intelligence ChatGPT in Oncology Examination Questions.","authors":"Ronald Chow, Shaakir Hasan, Ajay Zheng, Chenxi Gao, Gilmer Valdes, Francis Yu, Arpit Chhabra, Srinivas Raman, J Isabelle Choi, Haibo Lin, Charles B Simone","doi":"10.1016/j.jacr.2024.07.011","DOIUrl":"10.1016/j.jacr.2024.07.011","url":null,"abstract":"<p><p>The aim of this study is to assess the accuracy of Chat Generative Pretrained Transformer (ChatGPT) in response to oncology examination questions in the setting of one-shot learning. Consecutive national radiation oncology in-service multiple-choice examinations were collected and inputted into ChatGPT 4o and ChatGPT 3.5 to determine ChatGPT's answers. ChatGPT's answers were then compared with the answer keys to determine whether ChatGPT correctly or incorrectly answered each question and to determine if improvements in responses were seen with the newer ChatGPT version. A total of 600 consecutive questions were inputted into ChatGPT. ChatGPT 4o answered 72.2% questions correctly, whereas 3.5 answered 53.8% questions correctly. There was a significant difference in performance by question category (P < .01). ChatGPT performed poorer with respect to knowledge of landmark studies and treatment recommendations and planning. ChatGPT is a promising technology, with the latest version showing marked improvement. Although it still has limitations, with further evolution, it may be considered a reliable resource for medical training and decision making in the oncology space.</p>","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141891137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-25DOI: 10.1016/j.jacr.2024.07.005
Elizabeth H Dibble, Eric Rubin, Richard Duszak, Dominick Parris, Mary Jo Tarrant, Jay R Parikh
{"title":"Response to Letter to the Editor.","authors":"Elizabeth H Dibble, Eric Rubin, Richard Duszak, Dominick Parris, Mary Jo Tarrant, Jay R Parikh","doi":"10.1016/j.jacr.2024.07.005","DOIUrl":"10.1016/j.jacr.2024.07.005","url":null,"abstract":"","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141725203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-19DOI: 10.1016/j.jacr.2024.07.006
Subha Ghosh, James Stoller
{"title":"Social Constructionism in Radiology: An Introduction.","authors":"Subha Ghosh, James Stoller","doi":"10.1016/j.jacr.2024.07.006","DOIUrl":"10.1016/j.jacr.2024.07.006","url":null,"abstract":"","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141735901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-17DOI: 10.1016/j.jacr.2024.05.013
Edward I Bluth
{"title":"Letter to the Editor.","authors":"Edward I Bluth","doi":"10.1016/j.jacr.2024.05.013","DOIUrl":"10.1016/j.jacr.2024.05.013","url":null,"abstract":"","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141728399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-15DOI: 10.1016/j.jacr.2024.06.023
Katherine Frederick-Dyer, Sarah Averill, Kirti Magudia, Sasha Kurumety, Elizabeth H Dibble
{"title":"Imaging Pregnant or Potentially Pregnant Patients With Ionizing Radiation: Navigating a Changing Medicolegal Landscape.","authors":"Katherine Frederick-Dyer, Sarah Averill, Kirti Magudia, Sasha Kurumety, Elizabeth H Dibble","doi":"10.1016/j.jacr.2024.06.023","DOIUrl":"10.1016/j.jacr.2024.06.023","url":null,"abstract":"","PeriodicalId":73968,"journal":{"name":"Journal of the American College of Radiology : JACR","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141636030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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