RadiologyPub Date : 2025-06-01DOI: 10.1148/radiol.251731
Daniel Pinto Dos Santos
{"title":"Fully Automated De-Identification of Medical Imaging Data.","authors":"Daniel Pinto Dos Santos","doi":"10.1148/radiol.251731","DOIUrl":"10.1148/radiol.251731","url":null,"abstract":"<p><p>\u0000 <i>\"Just Accepted\" papers have undergone full peer review and have been accepted for publication in <i>Radiology</i>. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content.</i>\u0000 </p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"315 3","pages":"e251731"},"PeriodicalIF":12.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144476500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
RadiologyPub Date : 2025-06-01DOI: 10.1148/radiol.242408
Ok Hee Woo, Sung Eun Song, Su Jin Choe, Minhye Kim, Kyu Ran Cho, Bo Kyoung Seo
{"title":"Invasive Breast Cancers Missed by AI Screening of Mammograms.","authors":"Ok Hee Woo, Sung Eun Song, Su Jin Choe, Minhye Kim, Kyu Ran Cho, Bo Kyoung Seo","doi":"10.1148/radiol.242408","DOIUrl":"10.1148/radiol.242408","url":null,"abstract":"<p><p>Background Little is known about the features of invasive breast cancers missed by artificial intelligence (AI) on mammograms. Purpose To assess the false-negative rate (FNR) of AI mammogram evaluation according to molecular subtype and to investigate the features of and reasons for AI-missed cancers. Materials and Methods This retrospective study identified consecutive patients diagnosed with breast cancer between January 2014 and December 2020. Commercial AI software was used to read the mammograms, and abnormality score (AS) was acquired. AI-missed cancers were defined as those for which AI did not identify a precise location matching the reference standard. The FNR was calculated by counting AI-missed cancers according to molecular subtype (hormone receptor-positive [luminal] vs human epidermal growth factor receptor 2 [HER2]-enriched vs triple-negative). Three blinded radiologists classified AI-missed cancers as either actionable or under threshold, and reasons for misses were determined through nonblinded reviews. Features were compared according to AI detection with the χ<sup>2</sup> test. Results A total of 1082 consecutive women diagnosed with 1097 cancers (mean age, 54.3 years ± 11 [SD]) were included. AI missed 14% (154 of 1097) of cancers. The FNR was lowest in the HER2-enriched subtype (9% [36 of 398] in the HER2-enriched subtype, 17.2% [106 of 616] in the luminal subtype, and 14.5% [12 of 83] in the triple-negative subtype; <i>P</i> = .001). Compared with AI-detected cancers, AI-missed cancers were associated with younger age, a tumor size less than or equal to 2 cm, a lower histologic grade, fewer lymph node metastases, more Breast Imaging Reporting and Data System category 4 findings, lower Ki-67 expression, and nonmammary zone locations (all, <i>P</i> < .05). In blinded reviews, 61.7% (95 of 154) of AI-missed cancers were actionable; the reasons for misses were dense breasts (<i>n</i> = 56), nonmammary zone locations (<i>n</i> = 22), architectural distortions (<i>n</i> = 12), and amorphous microcalcifications (<i>n</i> = 5). Conclusion To reduce AI-missed cancers on mammograms, attention should be given to luminal cancer, dense breasts, nonmammary zone locations, architectural distortions, and amorphous calcifications. Published under a CC BY 4.0 license. <i>Supplemental material is available for this article.</i> See also the editorial by Mullen in this issue.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"315 3","pages":"e242408"},"PeriodicalIF":12.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144476523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
RadiologyPub Date : 2025-06-01DOI: 10.1148/radiol.241904
Kuan Zhang, Ali Ganjizadeh, Sanaz Vahdati, John Huston, Matt A Bernstein, Bradley J Erickson, Yunhong Shu
{"title":"Recent Advances in Compact Portable Platforms and Gradient Hardware for Brain MRI.","authors":"Kuan Zhang, Ali Ganjizadeh, Sanaz Vahdati, John Huston, Matt A Bernstein, Bradley J Erickson, Yunhong Shu","doi":"10.1148/radiol.241904","DOIUrl":"10.1148/radiol.241904","url":null,"abstract":"<p><p>While pivotal in modern radiology for brain imaging, conventional whole-body MRI scanners face challenges related to their size, cost, and technical limitations, restricting accessibility for a wide range of patients. This article explores recent advances aiming to address these issues, with a focus on compact MRI scanners, portable low-field-strength MRI systems, and high-performance gradient inserts. Compact MRI scanners, specifically those at field strengths ranging from 0.5 to 7 T, in contrast to their whole-body counterparts, improve gradient performance and simplify installation. These compact scanners are typically fixed systems designed for cost reduction, space saving, and easy siting while also requiring much less cryogen yet supporting the use of high-performance gradients. Portable low-field-strength MRI systems (<0.5 T) provide flexible and cost-effective on-site imaging solutions. These portable systems are designed for mobility and increased accessibility, albeit with some quality trade-offs. All these compact scanners and portable systems have smaller physical footprints than conventional scanners due to reduced magnet size and bore width. Many are designed mainly for brain imaging. High-performance gradient inserts enhance existing MRI systems by providing superior spatial resolution or imaging speed, crucial for advanced neuroimaging. These innovations collectively promise to make MRI more accessible or versatile, transforming radiology practices across diverse clinical settings.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"315 3","pages":"e241904"},"PeriodicalIF":12.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12207646/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144258902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
RadiologyPub Date : 2025-06-01DOI: 10.1148/radiol.251493
Samuel J Galgano, Elainea N Smith
{"title":"Cost-Effectiveness and Efficacy of Noninvasive Colorectal Cancer Screening: An Important Step Toward Widespread Adoption of CT Colonography.","authors":"Samuel J Galgano, Elainea N Smith","doi":"10.1148/radiol.251493","DOIUrl":"https://doi.org/10.1148/radiol.251493","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"315 3","pages":"e251493"},"PeriodicalIF":12.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144258896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
RadiologyPub Date : 2025-06-01DOI: 10.1148/radiol.259011
Tina Y Poussaint
{"title":"Congratulations to the 2025 Editorial Fellows.","authors":"Tina Y Poussaint","doi":"10.1148/radiol.259011","DOIUrl":"https://doi.org/10.1148/radiol.259011","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"315 3","pages":"e259011"},"PeriodicalIF":12.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144476497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
RadiologyPub Date : 2025-06-01DOI: 10.1148/radiol.243750
Nooshin Abbasi, Neena Kapoor, Ronilda Lacson, Jeffrey P Guenette, Sonali Desai, David Lucier, Sanjay Saini, Rachel Sisodia, Ali S Raja, David W Bates, Ramin Khorasani