Radiology最新文献

{"title":"External Test of a Deep Learning Algorithm for Pulmonary Nodule Malignancy Risk Stratification Using European Screening Data.","authors":"Noa Antonissen, Kiran Vaidhya Venkadesh, Renate Dinnessen, Ernst Th Scholten, Zaigham Saghir, Mario Silva, Ugo Pastorino, Grigory Sidorenkov, Marjolein A Heuvelmans, Geertruida H de Bock, Firdaus A A Mohamed Hoesein, Pim A de Jong, Harry J M Groen, Rozemarijn Vliegenthart, Hester A Gietema, Mathias Prokop, Cornelia Schaefer-Prokop, Colin Jacobs","doi":"10.1148/radiol.250874","DOIUrl":"10.1148/radiol.250874","url":null,"abstract":"<p><p>Background Low-dose CT screening reduces lung cancer-related deaths but has high rates of false-positive findings. A deep learning (DL) algorithm could improve nodule risk stratification but requires robust external testing. Purpose To externally test a DL algorithm for nodule malignancy risk estimation using pooled data from three large European lung cancer screening trials. Materials and Methods In this retrospective study, a DL algorithm trained on National Lung Screening Trial data was externally tested using baseline CT scans from the Danish Lung Cancer Screening Trial, the Multicentric Italian Lung Detection trial, and the Dutch-Belgian Lung Cancer Screening Trial. Performance was assessed across the pooled cohort and two subsets: subset A, including indeterminate nodules (5-15 mm); and subset B, including cancers size-matched to benign nodules (1:2 ratio). Performance, including the area under the receiver operating characteristic curve (AUC), was compared with the Pan-Canadian Early Detection of Lung Cancer (PanCan) model. Results The pooled cohort included 4146 participants (median age, 58 years; 78% male participants; median smoking history, 38 pack-years) with 7614 benign and 180 malignant nodules. The DL algorithm achieved AUCs of 0.98, 0.96, and 0.94 for cancers diagnosed within 1 year, 2 years, and throughout screening, respectively, compared with 0.98, 0.94, and 0.93 (<i>P</i> = .19, .02, and .46, respectively) for the PanCan model. In subset A (129 malignant and 2086 benign nodules), DL significantly outperformed PanCan across the same cancer diagnosis timeframes (respective AUCs: 0.95, 0.94, and 0.90 vs 0.91, 0.88, and 0.86; all <i>P</i> < .05). At 100% sensitivity for cancers diagnosed within 1 year, DL classified 68.1% of benign cases as low risk versus 47.4% for the PanCan model, a 39.4% relative reduction in false-positive findings. In subset B (180 malignant and 360 benign nodules), the AUC of the DL algorithm versus the PanCan model was 0.79 versus 0.60 (<i>P</i> < .01), respectively. Conclusion The DL algorithm outperformed the PanCan model across multiple European screening datasets, demonstrating superior malignancy prediction while substantially reducing false-positive classifications for indeterminate nodules. © RSNA, 2025 <i>Supplemental material is available for this article.</i></p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"316 3","pages":"e250874"},"PeriodicalIF":15.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145070405","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}

{"title":"Left Atrial Minimum Volume Index at Cardiac MRI Predicts Adverse Outcomes after Acute Myocardial Infarction.","authors":"Jingping Wu, Jinyi Xiang, Xingyu Gu, Lei Zhao, Binghua Chen, Dong-Aolei An, Yan Zhou, Jun Pu, Lianming Wu","doi":"10.1148/radiol.250078","DOIUrl":"10.1148/radiol.250078","url":null,"abstract":"<p><p>Background Left atrial (LA) structural and functional parameters are associated with prognosis after acute myocardial infarction (AMI). Purpose To explore the prognostic value of LA minimum volume index (LAVI_min) as measured at cardiac MRI and its incremental predictive value beyond LA functional parameters for predicting major adverse cardiovascular events (MACE) after AMI in a large population. Materials and Methods This prospective study enrolled patients with AMI who underwent percutaneous coronary intervention and subsequent cardiac MRI between February 2014 and January 2024. MACE included all-cause death, reinfarction, unplanned revascularization, and heart failure hospitalization. Univariable and multivariable Cox regression analyses were used to evaluate the association between LAVI_min and MACE. Receiver operating characteristic analysis and Kaplan-Meier analysis were used to evaluate the prognostic value of LAVI_min in participants with AMI. Results A total of 1191 participants (mean age, 58 years ± 11 [SD]; 1007 male participants) were included. Among them, 183 individuals experienced MACE over a median follow-up of 38 months (IQR, 20-57 months). After adjusting for clinical risk factors and cardiac MRI parameters, a larger LAVI_min was independently associated with MACE (hazard ratio, 1.06 [95% CI: 1.05, 1.08]; <i>P</i> < .001). Receiver operating characteristic analysis revealed that LAVI_min (area under the receiver operating characteristic curve [AUC], 0.74) had better discriminative ability for MACE than LA maximum volume index (LAVI_max) (AUC, 0.65; <i>P</i> < .001) and LA conduit strain (AUC, 0.64<i>; P</i> < .001). Traditional risk predictors plus LAVI_min had greater prognostic value for MACE (C index, 0.75) than traditional risk factors alone (C index, 0.69; <i>P</i> < .001) or traditional risk predictors plus LAVI_max (C index, 0.72; <i>P</i> = .03). Conclusion LAVI_min was an independent predictor of MACE after AMI, with incremental prognostic value and improved discriminative ability over traditional risk factors including cardiac MRI parameters. © RSNA, 2025 <i>Supplemental material is available for this article.</i> See also the editorial by Weir-McCall and Hua in this issue.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"316 3","pages":"e250078"},"PeriodicalIF":15.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145070486","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}

{"title":"Crisis-Responsive Imaging: Lessons from a High-Volume Mass Casualty Incident.","authors":"Gal Ben-Arie, Tomer Krutik, Yonatan Serlin, Ran Abuhasira, Uriel Wachsman, Shlomit Tamir, Jacob Sosna, Larisa Dukhno, Tzachi Slutsky, Shlomi Codish, Ilan Shelef","doi":"10.1148/radiol.250713","DOIUrl":"https://doi.org/10.1148/radiol.250713","url":null,"abstract":"<p><p>Background Mass casualty incidents (MCIs) impose extraordinary demands on health care, requiring radiology departments to rapidly adapt workflows and resources. Speed and quality of imaging are pivotal for guiding clinical decisions in these high-stakes settings, highlighting the necessity for radiology teams to learn from and improve their response in real time. Purpose To assess the radiology department response, workflow adaptations, and operational impact during an MCI following the October 7, 2023, attack in southern Israel, and to provide recommendations for future crisis preparedness. Materials and Methods In this retrospective study, use of imaging and workflow at Soroka University Medical Center, the sole tertiary and level 1 trauma center in southern Israel, were analyzed. Data from 673 injured patients in the first 24 hours included demographics, injury patterns, imaging use (radiography, CT), turnaround times, and real-time protocol adaptations. Findings were compared with a 12-month baseline. Results A total of 461 patients underwent imaging during the crisis, and 93.5% of the imaging (431 patients) was related to the MCI. The mean patient age was 29.6 years ± 14.9 (SD); 53 patients (7.9%) were age 18 years or younger and 27 patients (4.0%) were age 65 years or older. Most patients were male (<i>n</i> = 520; 77.3%). Digital radiography was performed in 351 patients and CT was performed in 164 patients, and 54 patients underwent imaging with both modalities. The median time from a CT order to completion decreased from 54 minutes (baseline) to 28 minutes (<i>P</i> = .03), whereas radiography turnaround time increased modestly, from 43 to 49 minutes (<i>P</i> < .001). Both enhanced staffing, achieving more than a fourfold increase compared with routine operations, and flexible resource reallocation, including the repurposing of nontraditional CT scanners, were key in managing the patient surge and optimizing diagnostic workflows. Conclusion These findings underscored the critical importance of dynamic in-hospital triage protocols, rapid staff mobilization, and versatile management of imaging resources. These strategies are essential components of radiology preparedness plans to improve patient outcomes during future high-casualty incidents. © RSNA, 2025.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"316 3","pages":"e250713"},"PeriodicalIF":15.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145200881","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}

{"title":"Distinctive Structural Brain Abnormalities in Chudley-McCullough Syndrome.","authors":"Rosa Couto,Mariana C Diogo","doi":"10.1148/radiol.251109","DOIUrl":"https://doi.org/10.1148/radiol.251109","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"16 1","pages":"e251109"},"PeriodicalIF":19.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145018064","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}

{"title":"Molecular Breast Imaging with Digital Breast Tomosynthesis for Dense Breast Supplemental Screening.","authors":"Amy M Fowler","doi":"10.1148/radiol.252378","DOIUrl":"10.1148/radiol.252378","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"316 3","pages":"e252378"},"PeriodicalIF":15.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12501619/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145125828","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}

{"title":"Risk Stratification of Sudden Cardiac Death in Nonischemic Dilated Cardiomyopathy: Arrhythmogenic Substrate Assessment in Cardiac MRI.","authors":"Di Zhou,Huaying Zhang,Wenjing Yang,Yining Wang,Leyi Zhu,Mengdi Jiang,Jing Xu,Fei Teng,Xinxiang Zhao,Shaocheng Zhu,Doudou Liu,Qiang Zhang,Arlene Sirajuddin,Andrew E Arai,Shihua Zhao,Minjie Lu","doi":"10.1148/radiol.243427","DOIUrl":"https://doi.org/10.1148/radiol.243427","url":null,"abstract":"Background MRI-derived arrhythmogenic substrate, including late gadolinium enhancement (LGE) and extracellular volume fraction (ECV), is indicative of sudden cardiac death (SCD) risk in nonischemic dilated cardiomyopathy (DCM). The relative prognostic value of LGE and ECV remains unclear. Purpose To evaluate the performance of LGE and T1 mapping in predicting SCD in patients with DCM and to explore clinical implementation. Materials and Methods This study enrolled 1105 patients with DCM who underwent cardiac MRI at four centers. The data were analyzed in a development cohort (n = 837, single center) and an external validation cohort (n = 268, multicenter). The primary end point comprised SCD, appropriate implantable cardioverter-defibrillator shock, and resuscitated cardiac arrest. The secondary end point comprised heart failure-related death, heart transplant, and left ventricle (LV) assist device implantation. Risk algorithms and a clinical workflow for SCD risk assessment were developed based on validated MRI predictors. Results In the development cohort, 78 patients reached the primary end point and 120 reached the secondary end point over a median follow-up of 58.3 months. In the adjusted analysis, LGE of at least 7.2% of the LV mass (hazard ratio [HR], 4.75 [95% CI: 2.91, 7.74]; P < .001), an ECV of at least 31.8% (HR, 2.91 [95% CI: 1.63, 5.22]; P = .001), and a native T1 z score of at least 2.1 (HR, 1.69 [95% CI: 1.04, 2.74]; P = .04) were associated with SCD-related events. Patients with an ECV of at least 31.8% and no LGE were at a higher risk of SCD events compared with those with an ECV less than 31.8% and presence of LGE of less than 7.2% or midwall and/or focal LGE. Patients with an LV ejection fraction greater than 35%, LGE less than 7.2%, and an ECV less than 31.8% exhibited a low risk of SCD, with an annual event rate of 0.2%. Patients with LGE of at least 7.2% exhibited a high risk of SCD-related events (annual event rate, 4.65%) irrespective of ECV and native T1 value and LGE distribution and/or pattern. Conclusion In nonischemic DCM, LGE of at least 7.2% was strongly predictive of SCD risk irrespective of distribution and pattern. ECV significantly enhanced risk stratification, particularly in patients with negative or focal and/or midwall LGE. © RSNA, 2025 Supplemental material is available for this article. See also the editorial by Sakuma in this issue.","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"14 1","pages":"e243427"},"PeriodicalIF":19.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145018107","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}

{"title":"Node Reporting and Data System Evaluation of Axillary Nodes in Invasive Ductal and Lobular Carcinoma.","authors":"Hee Jeong Kim,Eun Young Chae,Hye Joung Eom,Woo Jung Choi,Hee Jung Shin,Joo Hee Cha,Hak Hee Kim","doi":"10.1148/radiol.243823","DOIUrl":"https://doi.org/10.1148/radiol.243823","url":null,"abstract":"Background Although the Node Reporting and Data System (Node-RADS) offers a standardized method for assessing lymph node metastasis, its performance may vary according to the histologic type of breast cancer. Purpose To evaluate the applicability of the Node-RADS score in assessing axillary lymph node involvement in patients with invasive ductal carcinoma (IDC) or invasive lobular carcinoma (ILC). Materials and Methods In this retrospective study, data from consecutive women with pathologically confirmed IDC or ILC who underwent preoperative breast MRI between January 2017 and December 2018 were analyzed. Axillary nodal status was assessed using Node-RADS, in which nodal size and configuration criteria are combined into a final assessment score ranging from 1 (very low suspicion) to 5 (very high suspicion). The performance of the Node-RADS score for predicting axillary lymph node metastasis was compared between the two histologic types using the χ2 test. Results A total of 1602 women (mean age, 50.6 years ± 9.8 [SD]), including 25 with bilateral cancers, were included, yielding 1627 breast cancers. Among these cancers, 1486 were IDC and 141 were ILC. The frequency of lymph node metastasis was 25% (377 of 1486) for IDC and 28% (40 of 141) for ILC (P = .44). A Node-RADS score of 3 or greater yielded the highest Youden index for predicting axillary lymph node metastasis for both histologic types. At this cutoff, the sensitivity and specificity were 71.1% (268 of 377) and 86.5% (959 of 1109) for IDC and 52.5% (21 of 40) and 85.1% (86 of 101) for ILC, respectively. Although there was no evidence of a difference in specificity between the histologic types, sensitivity was significantly lower for ILC (P = .02). The area under the receiver operating characteristic curve (AUC) was 0.83 for IDC and 0.74 for ILC (P = .08). Multivariable logistic regression analyses confirmed Node-RADS score as an independent predictor of axillary lymph node metastasis (odds ratio, 3.1; P < .001). Conclusion The Node-RADS score demonstrated comparable performance in terms of AUC in axillary nodal evaluation for IDC and ILC but lower sensitivity for ILC. © RSNA, 2025 Supplemental material is available for this article. See also the editorial by Freitas in this issue.","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"16 1","pages":"e243823"},"PeriodicalIF":19.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144959969","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}

{"title":"Clinical Validation of a Generative Artificial Intelligence Model for Chest Radiograph Reporting: A Multicohort Study.","authors":"Eui Jin Hwang, Jong Hyuk Lee, Woo Hyeon Lim, Won Gi Jeong, Wonju Hong, Jongsoo Park, Seung-Jin Yoo, Hyungjin Kim","doi":"10.1148/radiol.250568","DOIUrl":"https://doi.org/10.1148/radiol.250568","url":null,"abstract":"<p><p>Background Artificial intelligence (AI)-generated radiology reports have become available and require rigorous evaluation. Purpose To evaluate the clinical acceptability of chest radiograph reports generated by an AI algorithm and their accuracy in identifying referable abnormalities. Materials and Methods Chest radiographs from an intensive care unit (ICU), an emergency department, and health checkups were retrospectively collected between January 2020 and December 2022, and outpatient chest radiographs were sourced from a public dataset. An automated report-generating AI algorithm was then applied. A panel of seven thoracic radiologists evaluated the acceptability of generated reports, and acceptability was analyzed using a standard criterion (acceptable without revision or with minor revision) and a stringent criterion (acceptable without revision). Using chest radiographs from three of the contexts (excluding the ICU), AI-generated and radiologist-written reports were compared regarding the acceptability of the reports (generalized linear mixed model) and their sensitivity and specificity for identifying referable abnormalities (McNemar test). The radiologist panel was surveyed to evaluate their perspectives on the potential of AI-generated reports to replace radiologist-written reports. Results The chest radiographs of 1539 individuals (median age, 55 years; 656 male patients, 483 female patients, 400 patients of unknown sex) were included. There was no evidence of a difference in acceptability between AI-generated and radiologist-written reports under the standard criterion (88.4% vs 89.2%; <i>P</i> = .36), but AI-generated reports were less acceptable than radiologist-written reports under the stringent criterion (66.8% vs 75.7%; <i>P</i> < .001). Compared with radiologist-written reports, AI-generated reports identified radiographs with referable abnormalities with greater sensitivity (81.2% vs 59.4%; <i>P</i> < .001) and lower specificity (81.0% vs 93.6%; <i>P</i> < .001). In the survey, most radiologists indicated that AI-generated reports were not yet reliable enough to replace radiologist-written reports. Conclusion AI-generated chest radiograph reports had similar acceptability to radiologist-written reports, although a substantial proportion of AI-generated reports required minor revision. © RSNA, 2025 <i>Supplemental material is available for this article.</i> See also the editorial by Wu and Seo in this issue.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"316 3","pages":"e250568"},"PeriodicalIF":15.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145125857","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}

{"title":"Erratum for: Calcifications Affect Pathologic Complete Response and MRI Prediction after Neoadjuvant Chemotherapy in Human Epidermal Growth Factor Receptor 2-positive Breast Cancer.","authors":"Eun Sook Ko, Jong Han Yu, Sook-Young Woo, Seok Won Kim, Boo-Kyung Han, Eun Young Ko, Ji Soo Choi, Jeongmin Lee, Myoung Kyoung Kim, Haejung Kim","doi":"10.1148/radiol.259015","DOIUrl":"https://doi.org/10.1148/radiol.259015","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"316 3","pages":"e259015"},"PeriodicalIF":15.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145200879","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}

{"title":"Molecular Breast Imaging and Digital Breast Tomosynthesis for Dense Breast Screening: The Density MATTERS Trial.","authors":"Carrie B Hruska, Katie N Hunt, Nicholas B Larson, Patricia A Miller, Richard L Ellis, Robin B Shermis, Gaiane M Rauch, Amy Lynn Conners, Jeannette Gasal Spilde, Dominic T Semaan, Emily C Siegal, Shannon N Zingula, Sabala R Mandava, Tamara S Martin, Riffat K Ahmed, Dana H Whaley, Beatriz E Adrada, Lacey R Gray, Ramila A Mehta, Rebecca J Roll, Roberta E Redfern, Michael K O'Connor, Deborah J Rhodes","doi":"10.1148/radiol.243953","DOIUrl":"10.1148/radiol.243953","url":null,"abstract":"<p><p>Background Molecular breast imaging (MBI) relies on the functional uptake of a radiotracer, technetium 99m sestamibi, to reveal cancers that are occult on mammograms due to breast density. Purpose To assess the performance of screening MBI as a supplement to digital breast tomosynthesis (DBT) in women with dense breasts. Materials and Methods In this prospective, multiyear, multicenter trial from five sites, women with dense breasts were prospectively enrolled from 2017 to 2022 and underwent two annual screening rounds of DBT and MBI to assess the incremental cancer detection rate (CDR, reported as cancers per 1000 screenings) of supplemental MBI and to compare other performance metrics of DBT and MBI. Results A total of 2978 participants were included. Participants had a mean age of 56.8 years ± 9.3 (SD) and a mean lifetime Tyrer-Cuzick risk of 12.0% ± 7.9 (SD). At year 1, the CDR was 5.0‰ (15 of 2978 participants) with DBT and 11.8‰ (35 of 2978 participants) with DBT plus prevalence screening MBI (incremental CDR, 6.7‰ [95% CI: 4.2, 10.6]; <i>P</i> < .001); the invasive CDR was 3.0‰ (nine of 2978 participants) with DBT and 7.7‰ (23 of 2978 participants) with DBT plus prevalence screening MBI (invasive incremental CDR, 4.7‰ [95% CI: 2.7, 8.1]; <i>P</i> < .001). At year 2, the CDR was 5.8‰ (15 of 2590 participants) with DBT and 9.3‰ (24 of 2590 participants) with DBT plus incidence screening MBI (incremental CDR, 3.5‰ [95% CI: 1.7, 6.8]; <i>P</i> = .001); the invasive CDR was 1.5‰ (four of 2590 participants) with DBT and 3.9‰ (10 of 2590 participants) with DBT plus incidence screening MBI (invasive incremental CDR, 2.3‰ [95% CI: 0.9, 5.3]; <i>P</i> = .048). The year 1 recall rate was 8.6% (255 of 2978 participants) with DBT and 17.9% (534 of 2978 participants) with DBT plus prevalence screening MBI (difference, 9.4% [95% CI: 8.4, 10.5]). The year 2 recall rate was 8.9% (231 of 2590 participants) with DBT and 13.8% (356 of 2590 participants) with DBT plus incidence screening MBI (difference, 4.8% [95% CI: 4.1, 5.7]). Twenty-nine participants had cancers detected only with MBI: 21 (72%) had invasive cancers (median size, 0.9 cm), 26 (90%) had node-negative cancers, and six (20%) had advanced cancers. The interval cancer rate was 0.7‰ (two of 2978 participants) in year 1 and 0.8‰ (two of 2590 participants) in year 2. Conclusion The addition of MBI to DBT screening increased invasive cancer detection by 2.5-fold and modestly increased the recall rate at the second screening round. © RSNA, 2025 See also the editorial by Fowler in this issue.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"316 3","pages":"e243953"},"PeriodicalIF":15.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12501625/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145125859","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}