Journal of Magnetic Resonance Imaging最新文献

{"title":"Development and Validation of a Deep Learning System to Differentiate HER2-Zero, HER2-Low, and HER2-Positive Breast Cancer Based on Dynamic Contrast-Enhanced MRI.","authors":"Yi Dai, Chun Lian, Zhuo Zhang, Jing Gao, Fan Lin, Ziyin Li, Qi Wang, Tongpeng Chu, Dilinuer Aishanjiang, Meiying Chen, Ximing Wang, Guanxun Cheng, Rong Huang, Jianjun Dong, Haicheng Zhang, Ning Mao","doi":"10.1002/jmri.29670","DOIUrl":"https://doi.org/10.1002/jmri.29670","url":null,"abstract":"<p><strong>Background: </strong>Previous studies explored MRI-based radiomic features for differentiating between human epidermal growth factor receptor 2 (HER2)-zero, HER2-low, and HER2-positive breast cancer, but deep learning's effectiveness is uncertain.</p><p><strong>Purpose: </strong>This study aims to develop and validate a deep learning system using dynamic contrast-enhanced MRI (DCE-MRI) for automated tumor segmentation and classification of HER2-zero, HER2-low, and HER2-positive statuses.</p><p><strong>Study type: </strong>Retrospective.</p><p><strong>Population: </strong>One thousand two hundred ninety-four breast cancer patients from three centers who underwent DCE-MRI before surgery were included in the study (52 ± 11 years, 811/204/279 for training/internal testing/external testing).</p><p><strong>Field strength/sequence: </strong>3 T scanners, using T1-weighted 3D fast spoiled gradient-echo sequence, T1-weighted 3D enhanced fast gradient-echo sequence and T1-weighted turbo field echo sequence.</p><p><strong>Assessment: </strong>An automated model segmented tumors utilizing DCE-MRI data, followed by a deep learning models (ResNetGN) trained to classify HER2 statuses. Three models were developed to distinguish HER2-zero, HER2-low, and HER2-positive from their respective non-HER2 categories.</p><p><strong>Statistical tests: </strong>Dice similarity coefficient (DSC) was used to evaluate the segmentation performance of the model. Evaluation of the model performances for HER2 statuses involved receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC), accuracy, sensitivity, and specificity. The P-values <0.05 were considered statistically significant.</p><p><strong>Results: </strong>The automatic segmentation network achieved DSC values of 0.85 to 0.90 compared to the manual segmentation across different sets. The deep learning models using ResNetGN achieved AUCs of 0.782, 0.776, and 0.768 in differentiating HER2-zero from others in the training, internal test, and external test sets, respectively. Similarly, AUCs of 0.820, 0.813, and 0.787 were achieved for HER2-low vs. others, and 0.792, 0.745, and 0.781 for HER2-positive vs. others, respectively.</p><p><strong>Data conclusion: </strong>The proposed DCE-MRI-based deep learning system may have the potential to preoperatively distinct HER2 expressions of breast cancers with therapeutic implications.</p><p><strong>Evidence level: </strong>4 TECHNICAL EFFICACY: Stage 3.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142792013","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":"Editorial for \"Prenatal MR Diagnosis of Total Anomalous Pulmonary Venous Connection and Related Brain Growth Changes\".","authors":"Aviad Rabinowich, Livia Kapusta","doi":"10.1002/jmri.29673","DOIUrl":"https://doi.org/10.1002/jmri.29673","url":null,"abstract":"","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142785408","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":"Prenatal MR Diagnosis of Total Anomalous Pulmonary Venous Connection and Related Brain Growth Changes.","authors":"Jing-Ya Ren, Chang-An Chen, Ming Zhu, Ke Liu, Li-Jun Chen, Su-Zhen Dong","doi":"10.1002/jmri.29671","DOIUrl":"https://doi.org/10.1002/jmri.29671","url":null,"abstract":"<p><strong>Background: </strong>Prenatal diagnosis of total anomalous pulmonary venous connection (TAPVC) is challenging, and little is known about how it affects brain development.</p><p><strong>Purpose: </strong>To evaluate the utility of fetal MRI to diagnose TAPVC and related brain growth changes.</p><p><strong>Study type: </strong>Retrospective case-control study.</p><p><strong>Population: </strong>Twenty-one fetuses (23.0 to 30.8 weeks, mean 26.4 weeks) with pre-natal MRI diagnosis of TAPVC. Post-natal images and surgery were available in 18 fetuses. Brain volumes in TAPVC fetuses were compared with age and sex matched 100 cases of normal controls and 38 fetuses with tetralogy of Fallot (TOF).</p><p><strong>Sequence: </strong>Single shot turbo spin echo sequence for evaluating fetal brain, and steady-state free precession (SSFP) sequence for evaluating fetal cardiovascular structures at 1.5 T.</p><p><strong>Assessment: </strong>TAPVC type was determined by visualizing the drainage of the common pulmonary vein and dilated coronary sinus: supracardiac, intracardiac and infracardiac. The fetal pulmonary edema was evaluated, and fetal brain volumes were measured using automatic segmentation.</p><p><strong>Statistical tests: </strong>One-way analysis of variance and post hoc least square difference tests to evaluate differences in variables between TAPVC, TOF and control groups. A P value <0.05 was considered significant.</p><p><strong>Results: </strong>Of the 21 cases of TAPVC, 10 (47.6%) were identified as supracardiac, 8 (38.1%) as intracardiac, and 3 (14.3%) as infracardiac. Eighteen cases were confirmed by postnatal imaging and surgery; the remaining three cases had no confirmation. Six cases were associated with other cardiovascular abnormalities. Key MRI features of fetal TAPVC included a dilated coronary sinus and vertical vein. Fetal pulmonary edema was seen in six cases. Compared to controls, TAPVC fetuses had lower cerebellum and brainstem volumes and higher e-CSF, while had larger subcortical brain tissue, cerebellum, brainstem, e-CSF, and intracranial cavity volumes than those of TOF cases.</p><p><strong>Data conclusion: </strong>Fetal MRI may be a useful modality for evaluating fetal TAPVC and altered brain development.</p><p><strong>Evidence level: </strong>3 TECHNICAL EFFICACY: Stage 3.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142780274","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":"Long-Term Risk of Clinically Significant Prostate Cancer in Biopsy-Negative Patients With Baseline Biparametric Prostate MRI.","authors":"Laura Parhiala, Juha Knaapila, Ivan Jambor, Janne Verho, Kari Syvänen, Hannu Aronen, Peter Boström, Otto Ettala","doi":"10.1002/jmri.29668","DOIUrl":"https://doi.org/10.1002/jmri.29668","url":null,"abstract":"<p><strong>Background: </strong>The long-term prevalence of clinically significant prostate cancer (csPCa) in patients with initial negative prostate biopsy is unknown.</p><p><strong>Purpose: </strong>To investigate the rate of csPCa of men with initial negative biopsy.</p><p><strong>Study type: </strong>Retrospective analysis of prospectively collected data.</p><p><strong>Population: </strong>A total of 197 men (mean age 63 years [SD ±6.98, range 29-79]) without csPCa on initial biopsy and available baseline biparametric prostate MRI (bpMRI).</p><p><strong>Field strength/sequence: </strong>3.0 T, turbo spin-echo T2-weighted (axial and sagittal) and three sets of diffusion-weighted imaging using single-shot spin-echo planar imaging (5 b-values 0-500 seconds/mm²; 2 b-values 0 and 1500 seconds/mm², and 2 b-values 0 and 2000 seconds/mm²).</p><p><strong>Assessment: </strong>BpMRI was read using Prostate Imaging Reporting Data System (PI-RADS) v2.1. Systematic or targeted biopsy results served as reference standard.</p><p><strong>Statistical tests: </strong>Continuous variables were compared using Kruskal-Wallis rank sum test. Categorical variables were compared using either Fisher's exact test or Pearson's chi-square test. Uni- and multivariate regression odds ratios (95% confidence interval) were used to study factors affecting csPCa being diagnosed during follow-up. Time to diagnosis of csPCa is calculated using the Kaplan-Meier method.</p><p><strong>Results: </strong>Of 197 men, 74 (38%), 57 (29%), and 66 (34%) presented with PI-RADS 1-2, 3, and 4-5 findings in the baseline bpMRI. During the median follow-up of 52 months, 8.1%, 5.3%, and 18.2% of these men were diagnosed with csPCa, respectively. Baseline PI-RADS finding was the only factor that associated with csPCa found during the follow-up.</p><p><strong>Data conclusion: </strong>Baseline bpMRI with PI-RADS scores 1-3 and initial biopsies negative of csPCa had low rate of csPCa during follow-up, which supports more conservative follow-up for them but further research with longer follow-up is warranted.</p><p><strong>Level of evidence: </strong>3 TECHNICAL EFFICACY: Stage 2.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142729669","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":"Estimated Brain Age in Healthy Aging and Across Multiple Neurological Disorders.","authors":"Li Chai, Jun Sun, Zhizheng Zhuo, Ren Wei, Xiaolu Xu, Yunyun Duan, Decai Tian, Yutong Bai, Ningnannan Zhang, Haiqing Li, Yuxin Li, Yongmei Li, Fuqing Zhou, Jun Xu, James H Cole, Frederik Barkhof, Jianguo Zhang, Huaguang Zheng, Yaou Liu","doi":"10.1002/jmri.29667","DOIUrl":"https://doi.org/10.1002/jmri.29667","url":null,"abstract":"<p><strong>Background: </strong>The brain aging in the general population and patients with neurological disorders is not well understood.</p><p><strong>Purpose: </strong>To characterize brain aging in the above conditions and its clinical relevance.</p><p><strong>Study type: </strong>Retrospective.</p><p><strong>Population: </strong>A total of 2913 healthy controls (HC), with 1395 females; 331 multiple sclerosis (MS); 189 neuromyelitis optica spectrum disorder (NMOSD); 239 Alzheimer's disease (AD); 244 Parkinson's disease (PD); and 338 cerebral small vessel disease (cSVD).</p><p><strong>Field strength/sequence: </strong>3.0 T/Three-dimensional (3D) T1-weighted images.</p><p><strong>Assessment: </strong>The brain age was estimated by our previously developed model, using a 3D convolutional neural network trained on 9794 3D T1-weighted images of healthy individuals. Brain age gap (BAG), the difference between chronological age and estimated brain age, was calculated to represent accelerated and resilient brain conditions. We compared MRI metrics between individuals with accelerated (BAG ≥ 5 years) and resilient brain age (BAG ≤ -5 years) in HC, and correlated BAG with MRI metrics, and cognitive and physical measures across neurological disorders.</p><p><strong>Statistical tests: </strong>Student's t test, Wilcoxon test, chi-square test or Fisher's exact test, and correlation analysis. P < 0.05 was considered statistically significant.</p><p><strong>Results: </strong>In HC, individuals with accelerated brain age exhibited significantly higher white matter hyperintensity (WMH) and lower regional brain volumes than those with resilient brain age. BAG was significantly higher in MS (10.30 ± 12.6 years), NMOSD (2.96 ± 7.8 years), AD (6.50 ± 6.6 years), PD (4.24 ± 4.8 years), and cSVD (3.24 ± 5.9 years) compared to HC. Increased BAG was significantly associated with regional brain atrophy, WMH burden, and cognitive impairment across neurological disorders. Increased BAG was significantly correlated with physical disability in MS (r = 0.17).</p><p><strong>Data conclusion: </strong>Healthy individuals with accelerated brain age show high WMH burden and regional volume reduction. Neurological disorders exhibit distinct accelerated brain aging, correlated with impaired cognitive and physical function.</p><p><strong>Level of evidence: </strong>4 TECHNICAL EFFICACY: Stage 2.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142716147","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":"Fetal Cardiovascular Magnetic Resonance: History, Current Status, and Future Directions.","authors":"Dominika Suchá, Anneloes E Bohte, Pim van Ooij, Tim Leiner, Eric M Schrauben, Heynric B Grotenhuis","doi":"10.1002/jmri.29664","DOIUrl":"https://doi.org/10.1002/jmri.29664","url":null,"abstract":"<p><p>Fetal cardiovascular magnetic resonance imaging (MRI) has emerged as a complementary modality for prenatal imaging in suspected congenital heart disease. Ongoing technical improvements extend the potential clinical value of fetal cardiovascular MRI. Ascertaining equivocal prenatal diagnostics obtained with ultrasonography allows for appropriate parental counseling and planning of postnatal surgery. This work summarizes current acquisition techniques and clinical applications of fetal cardiovascular MRI in the prenatal diagnosis and follow-up of fetuses with congenital heart disease. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 3.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142693133","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":"Editorial for \"Assessment the Impact of IDH Mutation Status on MRI Assessments of White Matter Integrity in Glioma Patients: Insights From Peak Width of Skeletonized Mean Diffusivity and Free Water Metrics\".","authors":"Steven Benitez, Seena Dehkharghani","doi":"10.1002/jmri.29651","DOIUrl":"https://doi.org/10.1002/jmri.29651","url":null,"abstract":"","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142681783","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":"On the Origin of fMRI Species.","authors":"Peter A Bandettini, Denis Le Bihan","doi":"10.1002/jmri.29649","DOIUrl":"https://doi.org/10.1002/jmri.29649","url":null,"abstract":"","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142648250","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":"Seizure Burden and Clinical Risk Factors in Glioma-Related Epilepsy: Insights From MRI Voxel-Based Lesion-Symptom Mapping.","authors":"Tianshi Li, Qiuling Li, Xing Fan, Lei Wang, Gan You","doi":"10.1002/jmri.29663","DOIUrl":"10.1002/jmri.29663","url":null,"abstract":"<p><strong>Background: </strong>Epilepsy is the most common preoperative symptom in patients with supratentorial gliomas. Identifying tumor locations and clinical factors associated with preoperative epilepsy is important for understanding seizure risk.</p><p><strong>Purpose: </strong>To investigate the key brain areas and risk factors associated with preoperative seizures in glioma patients.</p><p><strong>Study type: </strong>Retrospective.</p><p><strong>Population: </strong>A total of 735 patients with primary diffuse supratentorial gliomas (372 low grade; 363 high grade) with preoperative MRI and pathology data.</p><p><strong>Field strength/sequence: </strong>Axial T2-weighted fast spin-echo sequence at 3.0 T.</p><p><strong>Assessment: </strong>Seizure burden was defined as the number of preoperative seizures within 6 months. Tumor and high-signal edema areas on T2 images were considered involved regions. A voxel-based lesion-symptom mapping analysis was used to identify voxels associated with seizure burden. The involvement of peak voxels (those most associated with seizure burden) and clinical factors were assessed as risk factors for preoperative seizure.</p><p><strong>Statistical tests: </strong>Univariable and multivariable binary and ordinal logistic regression analyses and chi-square tests were performed, with results reported as odds ratios (ORs) and 95% confidence intervals. A P-value <0.05 was considered significant.</p><p><strong>Results: </strong>A total of 448 patients experienced preoperative seizures. Significant seizure burden-related voxels were located in the right hippocampus and left insular cortex (based on 1000 permutation tests), with significant differences observed in both low- and high-grade tumors. Tumor involvement in the peak voxel region was an independent risk factor for an increased burden of preoperative seizures (OR = 6.98). Additionally, multivariable binary logistic regression results indicated that 1p/19q codeletion (OR = 1.51), intermediate tumor volume (24.299-97.066 cm³), and involvement of the peak voxel (OR = 6.06) were independent risk factors for preoperative glioma-related epilepsy.</p><p><strong>Conclusion: </strong>Voxel areas identified through voxel-based lesion-symptom mapping analysis, along with clinical factors, show associations with clinical seizure burden, offering insights for assessing seizure burden for glioma patients.</p><p><strong>Level of evidence: </strong>4 TECHNICAL EFFICACY: Stage 1.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142621914","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":"Assessing the Performance of Artificial Intelligence Assistance for Prostate MRI: A Two-Center Study Involving Radiologists With Different Experience Levels.","authors":"Zhaonan Sun, Kexin Wang, Ge Gao, Huihui Wang, Pengsheng Wu, Jialun Li, Xiaodong Zhang, Xiaoying Wang","doi":"10.1002/jmri.29660","DOIUrl":"https://doi.org/10.1002/jmri.29660","url":null,"abstract":"<p><strong>Background: </strong>Artificial intelligence (AI) assistance may enhance radiologists' performance in detecting clinically significant prostate cancer (csPCa) on MRI. Further validation is needed for radiologists with different experiences.</p><p><strong>Purpose: </strong>To assess the performance of experienced and less-experienced radiologists in detecting csPCa, with and without AI assistance.</p><p><strong>Study type: </strong>Retrospective.</p><p><strong>Population: </strong>Nine hundred patients who underwent prostate MRI and biopsy (median age 67 years; 356 with csPCa and 544 with non-csPCa).</p><p><strong>Field strength/sequence: </strong>3-T and 1.5-T, diffusion-weighted imaging using a single-shot gradient echo-planar sequence, turbo spin echo T2-weighted image.</p><p><strong>Assessment: </strong>CsPCa regions based on biopsy results served as the reference standard. Ten less-experienced (<500 prostate MRIs) and six experienced (>1000 prostate MRIs) radiologists reviewed each case twice using Prostate Imaging Reporting and Data System v2.1, with and without AI, separated by 4-week intervals. Cases were equally distributed among less-experienced radiologists, and 90 cases were randomly assigned to each experienced radiologist. Reading time and diagnostic confidence were assessed.</p><p><strong>Statistical tests: </strong>Area under the curve (AUC), sensitivity, specificity, reading time, and diagnostic confidence were compared using the DeLong test, Chi-squared test, Fisher exact test, or Wilcoxon rank-sum test between the two sessions. A P-value <0.05 was considered significant. Adjusting threshold using Bonferroni correction was performed for multiple comparisons.</p><p><strong>Results: </strong>For less-experienced radiologists, AI assistance significantly improved lesion-level sensitivity (0.78 vs. 0.88), sextant-level AUC (0.84 vs. 0.93), and patient-level AUC (0.84 vs. 0.89). For experienced radiologists, AI assistance only improved sextant-level AUC (0.82 vs. 0.91). AI assistance significantly reduced median reading time (250 s [interquartile range, IQR: 157, 402] vs. 130 s [IQR: 88, 209]) and increased diagnostic confidence (5 [IQR: 4, 5] vs. 5 [IQR: 4, 5]) irrespective of experience and enhanced consistency among experienced radiologists (Fleiss κ: 0.53 vs. 0.61).</p><p><strong>Data conclusion: </strong>AI-assisted reading improves the performance of detecting csPCa on MRI, particularly for less-experienced radiologists.</p><p><strong>Evidence level: </strong>3 TECHNICAL EFFICACY: Stage 2.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142621893","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}