Academic Radiology最新文献

{"title":"The Redesigned American Board of Radiology 16-month Pathway in Nuclear Radiology: Initial Outcomes (2017-2022).","authors":"M Elizabeth Oates, Michelle Brugger, David Laszakovits","doi":"10.1016/j.acra.2024.11.036","DOIUrl":"https://doi.org/10.1016/j.acra.2024.11.036","url":null,"abstract":"<p><p>Launched on July 1, 2017, the redesigned American Board of Radiology 16-month Pathway in Nuclear Radiology is flourishing. The original goal of this accelerated training pathway was to help meet the ever-growing demand for nuclear radiology subspecialists in academic and community practices. As of March 1, 2024, 125 graduates of the 16-month pathway had achieved specialty certification in either diagnostic radiology or interventional radiology/diagnostic radiology; nearly 60% had also attained advanced certification in nuclear radiology and/or nuclear medicine. Between March and May 2024, we surveyed this group of 125 specialty board-certified pathway graduates to evaluate the impact of the pathway on their individual careers and on the overall workforce; 69/125 (55%) respondents completed the survey. The vast majority (86%) pursued at least one traditional fellowship after residency, thus becoming multi-subspecialized. The majority (62%) currently work in an academic setting. The vast majority (80%) currently practice nuclear radiology; 40% of those reported that nuclear radiology comprises at least 50% of their time or typical workload. PET/CT represents the predominant modality/service (59%) and a significant minority (11%) perform radiotheranostics/radiopharmaceutical therapies; the vast majority (80%) practice nuclear cardiology. We anticipate that the ABR 16-month pathway will continue to thrive and that its graduates will continue to bring their expertise in this rapidly expanding domain to their clinical practices and research pursuits to the benefit of radiology, medicine, patients, and society.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142792769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Growth Prediction of Ground-Glass Nodules Based on Pulmonary Vascular Morphology Nomogram.","authors":"Jingyan Wu, Keying Wang, Lin Deng, Hanzhou Tang, Limin Xue, Ting Yang, Jinwei Qiang","doi":"10.1016/j.acra.2024.11.041","DOIUrl":"https://doi.org/10.1016/j.acra.2024.11.041","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>To construct a nomogram combining conventional CT features (CCTFs), morphologically abnormal tumor-related vessels (MATRVs), and clinical features to predict the two-year growth of lung ground-glass nodule (GGN).</p><p><strong>Methods: </strong>High-resolution CT targeted scan images of 158 patients including 167 GGNs from January 2016 to September 2019 were retrospectively analyzed. The CCTF and MATRV of each GGN were recorded. All GGNs were randomly divided into a training set (n = 118) and a validation set (n = 49). Multiple stepwise regression was used to select the features. Multivariate logistic regression was used to construct the CCTF, CCTF-CTRV (category of tumor-related vessel), and CCTF-QTRV (quantity of tumor-related vessel) nomograms. The performance and utility of the nomograms were evaluated using the area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA).</p><p><strong>Results: </strong>The AUC of the CCTF-QTRV nomogram, which included the features of smoking history, nodule pattern, lobulation, and the number of distorted and dilated vessels, was higher than the AUCs of the CCTF and CCTF-CTRV nomograms in both the training set (AUC: 0.906 vs. 0.857; vs. 0.851) and the validation set (AUC: 0.909 vs. 0.796; vs. 0.871). DCA indicated that both patients and clinicians could benefit from using the nomogram.</p><p><strong>Conclusion: </strong>The nomogram constructed by combining MATRV, CCTF, and clinical information can more effectively predict the two-year growth of GGNs. This integrated approach enhances the predictive accuracy, making it a valuable tool for clinicians in managing and monitoring patients with GGNs.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142792764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning for Contrast Enhanced Mammography - A Systematic Review.","authors":"Vera Sorin, Miri Sklair-Levy, Benjamin S Glicksberg, Eli Konen, Girish N Nadkarni, Eyal Klang","doi":"10.1016/j.acra.2024.11.035","DOIUrl":"https://doi.org/10.1016/j.acra.2024.11.035","url":null,"abstract":"<p><strong>Background/aim: </strong>Contrast-enhanced mammography (CEM) is a relatively novel imaging technique that enables both anatomical and functional breast imaging, with improved diagnostic performance compared to standard 2D mammography. The aim of this study is to systematically review the literature on deep learning (DL) applications for CEM, exploring how these models can further enhance CEM diagnostic potential.</p><p><strong>Methods: </strong>This systematic review was reported according to the PRISMA guidelines. We searched for studies published up to April 2024. MEDLINE, Scopus and Google Scholar were used as search databases. Two reviewers independently implemented the search strategy. We included all types of original studies published in English that evaluated DL algorithms for automatic analysis of contrast-enhanced mammography CEM images. The quality of the studies was independently evaluated by two reviewers based on the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) criteria.</p><p><strong>Results: </strong>Sixteen relevant studies published between 2018 and 2024 were identified. All but one used convolutional neural network models (CNN) models. All studies evaluated DL algorithms for lesion classification, while six studies also assessed lesion detection or segmentation. Segmentation was performed manually in three studies, both manually and automatically in two studies and automatically in ten studies. For lesion classification on retrospective datasets, CNN models reported varied areas under the curve (AUCs) ranging from 0.53 to 0.99. Models incorporating attention mechanism achieved accuracies of 88.1% and 89.1%. Prospective studies reported AUC values of 0.89 and 0.91. Some studies demonstrated that combining DL models with radiomics featured improved classification. Integrating DL algorithms with radiologists' assessments enhanced diagnostic performance.</p><p><strong>Conclusion: </strong>While still at an early research stage, DL can improve CEM diagnostic precision. However, there is a relatively small number of studies evaluating different DL algorithms, and most studies are retrospective. Further prospective testing to assess performance of applications at actual clinical setting is warranted.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142792760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CT-guided Preoperative Localization via Adjacent Microcoil Implantation Prior to Laparoscopic Partial Nephrectomy for Totally Endophytic Renal Masses Reduces Operative Time: A CaseControl Study.","authors":"Zhiyuan Zhang, Tianhao Su, Siwei Yang, Xuanhao Li, Wei Wei, Jian Song, Kelei Mao, Long Jin","doi":"10.1016/j.acra.2024.10.032","DOIUrl":"https://doi.org/10.1016/j.acra.2024.10.032","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>This retrospective casecontrol study evaluated the efficacy and safety of CT-guided preoperative localization via adjacent microcoil implantation for reducing the operative time of laparoscopic partial nephrectomy (LPN) in patients with endophytic renal masses.</p><p><strong>Methods: </strong>Data from patients who underwent LPN for completely endophytic treatment (three points for the ''E'' domain of the R.E.N.A.L. score) renal masses were collected from Beijing Friendship Hospital, Capital Medical University, between January 2020 and May 2023. Microcoils were placed adjacent to the renal masses under CT guidance prior to LPN. The head of the microcoil was pinpointed adjacent to the target endophytic renal mass, and its end tail remained outside the renal surface. Baseline characteristics and clinical, surgical, and postoperative outcomes were compared.</p><p><strong>Results: </strong>Forty patients (microcoil localization, N = 8; standard LPN, N = 32) were included in the analysis. The median clinical tumor size was 15 mm (IQR: 10-22). In all patients in the microcoil localization group, the microcoil was successfully visualized laparoscopically by the surgeons. The microcoil localization technique demonstrated a significantly shorter operative time under general anesthesia (150 vs. 195 min, P = 0.012) and a nonsignificant trend towards a shorter hospital stay (8.6 vs. 11.7 days, P = 0.079). The microcoil localization technique showed a nonsignificant trend toward a reduced total operative time (189 vs. 195 min, P = 0.012). No significant differences were observed in histopathological findings, surgical approach, postoperative eGFR levels, eGFRs, or complication rates were observed between the groups.</p><p><strong>Conclusion: </strong>This study suggested the use of a local microcoil on the surface of the kidney to locate the tumor accurately, which offers a more patient-centered surgical approach and can serve as a standard approach for treating totally endophytic renal masses that require localization.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142787735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and Validation of Multiparametric MRI-based Interpretable Deep Learning Radiomics Fusion Model for Predicting Lymph Node Metastasis and Prognosis in Rectal Cancer: A Two-center Study.","authors":"Yunjun Yang, Kaiting Han, Zhenyu Xu, Zhiping Cai, Hai Zhao, Julu Hong, Jiawei Pan, Li Guo, Weijun Huang, Qiugen Hu, Zhifeng Xu","doi":"10.1016/j.acra.2024.11.045","DOIUrl":"https://doi.org/10.1016/j.acra.2024.11.045","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>To develop interpretable machine learning models that utilize deep learning (DL) and radiomics based on multiparametric Magnetic resonance imaging (MRI) to predict preoperative lymph node (LN) metastasis in rectal cancer.</p><p><strong>Materials and methods: </strong>This retrospective study involved 286 cancer patients confirmed by histopathology from center 1 (Training set) and 66 patients from center 2 (External test set). Radiomics features were extracted from T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) sequences, whereas DL features were obtained using four models: MobileNet-V3-large, Inception-V3, ResNet50, and VGG16. These DL radiomics (DLR) features were then combined to construct a machine learning model. The Shapley additive interpretation (SHAP) tool was utilized to investigate the interpretability of the model. We evaluated and compared the diagnostic performance of senior and junior radiologists, with and without the aid of the optimal DLR model. Kaplan-Meier survival curve was used to analyze the prognosis of patients.</p><p><strong>Results: </strong>The DLR model outperforms individual DL models and the radiomics model. The MobileNet-V3-large combination radiomics signature demonstrated the best performance, achieving an AUC of 0.878 on the Training set and 0.752 on the External test set. Compared to the traditional radiomics model, the AUC for the Training set increased by 0.094 and by 0.051 for the External test set. This model facilitated improved diagnostic performance among both junior and senior radiologists. Specifically, the AUC values for junior and senior radiologists increased by 0.162 and 0.232, respectively, on the Training set; and by 0.096 and 0.113, respectively, on the External test set. The DLR model demonstrated strong performance in risk stratification for disease-free survival.</p><p><strong>Conclusion: </strong>The DLR model developed from multiparametric MRI can effectively distinguish cancer LN metastasis and enhance radiologists' diagnostic performance.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142787736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trends in Faculty Tenure Status and Diversity in Academic Radiology Departments in the United States.","authors":"Ajay Malhotra, Dheeman Futela, Seyedmehdi Payabvash, Max Wintermark, John E Jordan, Dheeraj Gandhi, Richard Duszak","doi":"10.1016/j.acra.2024.11.025","DOIUrl":"https://doi.org/10.1016/j.acra.2024.11.025","url":null,"abstract":"<p><strong>Background: </strong>Faculty tenure at U.S. medical schools has become less commonplace over the last several decades.</p><p><strong>Purpose: </strong>This study aimed to assess the long-term trends in tenure status, according to gender and underrepresented in medicine (URiM) status for academic radiology faculty in US medical schools.</p><p><strong>Materials and methods: </strong>The Association of American Medical Colleges (AAMC) Faculty Roster was used to study the number and proportions of academic radiology faculty (including radiologists and radiation oncologists) from 2000 to 2023 by tenure status stratified by gender and underrepresented in medicine (URiM) status. Simple linear regression was used for statistical comparisons.</p><p><strong>Results: </strong>The total number of academic radiology faculty increased from 5411 in 2000 to 10,597 in 2023. The proportion of non-URiM men decreased (from 73% to 65%), largely replaced by non-URiM women (from 21% to 27%), URiM men (3.7% to 4.4%), and URiM women (1.9% to 3%). The proportion of tenure-line (both tenured and on tenure track) radiology faculty members decreased across all groups, from 40% to 20% of total, an approximate 1%-point per year on average. Representation of women among tenure-line faculty increased (17% to 23% in 2023), but URiM representation remained stagnant (4.6% to 4.8% in 2023). When ranked by representation of female and URiM faculty among total tenured faculty, radiology placed 16th (of 18) for female representation (above surgery and orthopedics), and 18th (last) for URiM representation.</p><p><strong>Conclusion: </strong>Since 2000, academic radiology faculty nationally has enlarged with increased representation of women but remains dominated by non-underrepresented men (65%). Underrepresented groups have increased only marginally. Tenure-line faculty positions decreased across all groups. Similar to other clinical departments, women and underrepresented groups in radiology had a lower proportion of tenure-line faculty than non-underrepresented men.</p><p><strong>Clinical relevance statement: </strong>Since 2000, gender and racial/ethnic diversity in academic radiology has improved only marginally, particularly for tenure-line faculty. Increases in non-tenure positions nationwide likely represent an overall shift in academic practice prioritization of clinical over educational and research missions.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142781843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large Language Models with Vision on Diagnostic Radiology Board Exam Style Questions.","authors":"Shawn H Sun, Kasha Chen, Samuel Anavim, Michael Phillipi, Leslie Yeh, Kenneth Huynh, Gillean Cortes, Julia Tran, Mark Tran, Vahid Yaghmai, Roozbeh Houshyar","doi":"10.1016/j.acra.2024.11.028","DOIUrl":"https://doi.org/10.1016/j.acra.2024.11.028","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>The expansion of large language models to process images offers new avenues for application in radiology. This study aims to assess the multimodal capabilities of contemporary large language models, which allow analysis of image inputs in addition to textual data, on radiology board-style examination questions with images.</p><p><strong>Materials and methods: </strong>280 questions were retrospectively selected from the AuntMinnie public test bank. The test questions were converted into three formats of prompts; (1) Multimodal, (2) Image-only, and (3) Text-only input. Three models, GPT-4V, Gemini 1.5 Pro, and Claude 3.5 Sonnet, were evaluated using these prompts. The Cochran Q test and pairwise McNemar test were used to compare performances between prompt formats and models.</p><p><strong>Results: </strong>No difference was found for the performance in terms of % correct answers between the text, image, and multimodal prompt formats for GPT-4V (54%, 52%, and 57%, respectively; p = .31) and Gemini 1.5 Pro (53%, 54%, and 57%, respectively; p = .53). For Claude 3.5 Sonnet, the image input (48%) significantly underperformed compared to the text input (63%, p < .001) and the multimodal input (66%, p < .001), but no difference was found between the text and multimodal inputs (p = .29). Claude significantly outperformed GPT and Gemini in the text and multimodal formats (p < .01).</p><p><strong>Conclusion: </strong>Vision-capable large language models cannot effectively use images to increase performance on radiology board-style examination questions. When using textual data alone, Claude 3.5 Sonnet outperforms GPT-4V and Gemini 1.5 Pro, highlighting the advancements in the field and its potential for use in further research.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142781817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of Neoadjuvant Chemotherapy Response based on Ultrasound Deep Learning Radiomics Nomogram.","authors":"Yu Du","doi":"10.1016/j.acra.2024.11.040","DOIUrl":"https://doi.org/10.1016/j.acra.2024.11.040","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142781821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PSMA PET vs. mpMRI for Lymph Node Metastasis of Prostate Cancer: A Systematic Review and Head-to-Head Comparative Meta-analysis.","authors":"Bin Yang, Hao Dong, Shuwei Zhang, Shaoxing Ming, Rui Yang, Yonghan Peng, Xiaofeng Gao","doi":"10.1016/j.acra.2024.11.029","DOIUrl":"https://doi.org/10.1016/j.acra.2024.11.029","url":null,"abstract":"<p><strong>Purpose: </strong>To compare prostate-specific membrane antigen (PSMA) PET with multiparametric MRI (mpMRI) in the diagnosis of lymph node metastasis (LNM) in prostate cancer.</p><p><strong>Methods: </strong>A comprehensive search of PubMed, Embase, and Web of Science identified studies published up to August 24, 2024. Studies comparing PSMA PET and mpMRI accuracy in detecting LNM in prostate cancer were included. The quality of each study was assessed using the Quality Assessment of Diagnostic Performance Studies-2 tool.</p><p><strong>Results: </strong>This study included 23 articles with a total of 3041 patients. The pooled analysis showed PSMA PET had a sensitivity of 0.74 (95% CI:0.62-0.85) and specificity of 0.96 (95% CI:0.93-0.98) for detecting prostate cancer LNM, while mpMRI had a sensitivity of 0.45 (95% CI:0.32-0.57) and specificity at 0.92 (95% CI:0.86-0.97). PSMA PET shows notably higher sensitivity than mpMRI, (P < 0.01) with no significant difference in specificity (P = 0.18). For initial staging, PSMA PET shows significantly higher sensitivity than mpMRI (P < 0.01), with no significant specificity difference (P = 0.17). Subgroup analysis showed that both [⁶⁸Ga]Ga-PSMA-11 PET and [¹⁸F]F-PSMA-1007 PET had higher sensitivity than mpMRI (P = 0.03, P < 0.01) without significant differences in specificity (P = 0.10, P = 0.73). Meanwhile, there was no significant difference in the sensitivity (P = 0.20) and specificity (P = 0.43) of [¹⁸F]F-DCFPyL PET.</p><p><strong>Conclusion: </strong>PSMA PET is more sensitive than mpMRI in detecting LNM in prostate cancer, especially for initial staging; however, there is no significant difference in specificity between the two. Due to the high heterogeneity, more subgroup-based studies are needed to standardize imaging practices and validate these findings.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142781831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-Based Evaluation of Prostate MR Imaging at a Modern Low-field 0.55 T Scanner Compared to 3 T in a Screening Cohort.","authors":"Martin Segeroth, Hanns-Christian Breit, Jakob Wasserthal, Michael Bach, Cyrill Rentsch, Marc Matthias, Christian Wetterauer, Elmar Max Merkle, Daniel Tobias Boll","doi":"10.1016/j.acra.2024.11.024","DOIUrl":"https://doi.org/10.1016/j.acra.2024.11.024","url":null,"abstract":"<p><strong>Purpose: </strong>To evaluate the effect of lower field strength on quantitative apparent-diffusion-coefficient (ADC) values, contrast of the T2-weighted MR images and the performance of an AI-based segmentation.</p><p><strong>Materials and methods: </strong>25 screening clients (61.6 ± 7.5 years) from a study on a 3 T scanner were included and underwent a second examination on a 0.55 T scanner. Axial T2 weighted and diffusion-weighted images (DWI) sequences were acquired. An AI-based segmentation was performed. Based on this, the segmentation, volumetry, ADC values and the ratio of central gland (CG) and peripheral zone (PZ) in T2 weighting were compared by using correlations coefficient (Pearson), Bland-Altman plots and a non-inferior test with a paired t-test and a margin of ± 20% (lower and upper boundary).</p><p><strong>Results: </strong>Volumetric assessment (peripheral zone//central gland) showed no significant (p = 0.13//0.38) difference between 3 T (mean volume: 14.81 (12.53-17.09)//23.07 (15.06-31.08)mL) and 0.55 T (mean volume of 14.29 (12.03-16.54; p = 0.13)//22.77 (14.41-31.12)mL). The deviation of the 0.55 T ADC values from the 3 T values was -10.14% (-16.09% to -4.18%) for the PZ and -4.68% (-10.12-0.76%) for the CG. Therefore, all confidence intervals remained within a margin of + /- 20% and thus demonstrated significant non-inferiority.</p><p><strong>Conclusion: </strong>Biparametric prostate imaging is feasible at 0.55 T: ADC values vary within a common inter-scanner range compared to a 3 T and no difference can be observed regarding contrast ratio between peripheral zone and central gland in T2 weighted images, volumetry and AI-based segmentation compared to 3 T.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142774355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}