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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". 为 "评估 IDH 突变状态对胶质瘤患者白质完整性 MRI 评估的影响:骨骼化平均扩散率峰值宽度和自由水指标的启示
IF 3.3 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-11-21 DOI: 10.1002/jmri.29651
Steven Benitez, Seena Dehkharghani
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引用次数: 0
On the Origin of fMRI Species. 关于 fMRI 物种的起源。
IF 3.3 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-11-17 DOI: 10.1002/jmri.29649
Peter A Bandettini, Denis Le Bihan
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引用次数: 0
Seizure Burden and Clinical Risk Factors in Glioma-Related Epilepsy: Insights From MRI Voxel-Based Lesion-Symptom Mapping. 胶质瘤相关癫痫的发作负担和临床风险因素:基于核磁共振成像体素的病灶-症状绘图的启示。
IF 3.3 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-11-15 DOI: 10.1002/jmri.29663
Tianshi Li, Qiuling Li, Xing Fan, Lei Wang, Gan You

Background: 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.

Purpose: To investigate the key brain areas and risk factors associated with preoperative seizures in glioma patients.

Study type: Retrospective.

Population: A total of 735 patients with primary diffuse supratentorial gliomas (372 low grade; 363 high grade) with preoperative MRI and pathology data.

Field strength/sequence: Axial T2-weighted fast spin-echo sequence at 3.0 T.

Assessment: 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.

Statistical tests: 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.

Results: 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.

Conclusion: 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.

Level of evidence: 4 TECHNICAL EFFICACY: Stage 1.

背景:癫痫是幕上胶质瘤患者术前最常见的症状。目的:研究与胶质瘤患者术前癫痫发作相关的关键脑区和风险因素:研究类型:回顾性研究:共有735名原发性弥漫性幕上胶质瘤患者(低级别372名;高级别363名)提供了术前MRI和病理数据:3.0T轴向T2加权快速自旋回波序列:癫痫发作负担定义为术前6个月内的癫痫发作次数。T2图像上的肿瘤和高信号水肿区被视为受累区。采用基于体素的病灶-症状映射分析来确定与癫痫发作相关的体素。峰值体素(与癫痫发作负荷最相关的体素)的累及情况和临床因素被评估为术前癫痫发作的风险因素:进行了单变量和多变量二元和序数逻辑回归分析以及卡方检验,结果以几率比(OR)和 95% 置信区间报告。A P值 结果:共有 448 名患者在术前出现癫痫发作。与癫痫发作相关的重要体素位于右侧海马和左侧岛叶皮层(基于1000次排列检验),在低度和高度肿瘤中均观察到显著差异。肿瘤累及峰值体素区域是术前癫痫发作负担增加的独立风险因素(OR = 6.98)。此外,多变量二元逻辑回归结果表明,1p/19q编码缺失(OR = 1.51)、中等肿瘤体积(24.299-97.066 cm3)和峰值体素受累(OR = 6.06)是术前胶质瘤相关癫痫的独立风险因素:结论:通过基于体素的病灶-症状图谱分析确定的体素区与临床因素一起显示出与临床癫痫发作负担的相关性,为评估胶质瘤患者的癫痫发作负担提供了启示:4 技术效率:第 1 阶段。
{"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 cm<sup>3</sup>), 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}
引用次数: 0
Assessing the Performance of Artificial Intelligence Assistance for Prostate MRI: A Two-Center Study Involving Radiologists With Different Experience Levels. 评估人工智能辅助前列腺 MRI 的性能:一项涉及不同经验水平放射科医生的双中心研究。
IF 3.3 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-11-14 DOI: 10.1002/jmri.29660
Zhaonan Sun, Kexin Wang, Ge Gao, Huihui Wang, Pengsheng Wu, Jialun Li, Xiaodong Zhang, Xiaoying Wang

Background: 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.

Purpose: To assess the performance of experienced and less-experienced radiologists in detecting csPCa, with and without AI assistance.

Study type: Retrospective.

Population: Nine hundred patients who underwent prostate MRI and biopsy (median age 67 years; 356 with csPCa and 544 with non-csPCa).

Field strength/sequence: 3-T and 1.5-T, diffusion-weighted imaging using a single-shot gradient echo-planar sequence, turbo spin echo T2-weighted image.

Assessment: 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.

Statistical tests: 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.

Results: 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).

Data conclusion: AI-assisted reading improves the performance of detecting csPCa on MRI, particularly for less-experienced radiologists.

Evidence level: 3 TECHNICAL EFFICACY: Stage 2.

背景:人工智能(AI)辅助可能会提高放射科医生在核磁共振成像(MRI)上检测具有临床意义的前列腺癌(csPCa)的能力。目的:评估经验丰富和经验不足的放射科医生在有人工智能辅助和没有人工智能辅助的情况下检测前列腺癌的表现:研究类型:回顾性:900名接受前列腺磁共振成像和活检的患者(中位年龄67岁;356名患有csPCa,544名患有非csPCa):3T和1.5T,使用单次梯度回波平面序列进行扩散加权成像,涡轮自旋回波T2加权成像:以活检结果为参考标准的 CsPCa 区域。10名经验较少(1000次前列腺MRI)的放射科医生使用前列腺成像报告和数据系统v2.1版对每个病例进行了两次有AI和无AI的审查,间隔时间为4周。病例平均分配给经验较少的放射科医生,90 个病例随机分配给每位经验丰富的放射科医生。对读片时间和诊断信心进行了评估:使用 DeLong 检验、Chi-squared 检验、Fisher 精确检验或 Wilcoxon 秩和检验比较两个疗程之间的曲线下面积(AUC)、灵敏度、特异性、读片时间和诊断可信度。A P 值结果:对于经验不足的放射科医生来说,人工智能辅助显著提高了病灶级灵敏度(0.78 vs. 0.88)、六分仪级 AUC(0.84 vs. 0.93)和患者级 AUC(0.84 vs. 0.89)。对于经验丰富的放射科医生,人工智能辅助只提高了六分仪水平的 AUC(0.82 对 0.91)。无论经验如何,人工智能辅助都能大幅缩短中位读片时间(250 秒[四分位数间距:157,402] vs. 130 秒[四分位数间距:88,209]),提高诊断信心(5 [四分位数间距:4,5] vs. 5 [四分位数间距:4,5]),并增强经验丰富的放射科医生的一致性(Fleiss κ:0.53 vs. 0.61):数据结论:人工智能辅助读片提高了磁共振成像检测 csPCa 的性能,尤其是对经验不足的放射科医生而言。
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引用次数: 0
Advances in Neuroimaging and Multiple Post-Processing Techniques for Epileptogenic Zone Detection of Drug-Resistant Epilepsy 用于检测耐药性癫痫致痫区的神经影像学进展和多种后处理技术
IF 3.3 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-11-14 DOI: 10.1002/jmri.29658
Lei Yao MD, Nan Cheng MD, An-qiang Chen MD, Xun Wang MD, Ming Gao MD, Qing-xia Kong PhD, Yu Kong PhD

THE CLINICAL APPLICATION OF VARIOUS IMAGING METHODS IN HS DETECTION. A 39-YEAR-OLD FEMALE WITH EPILEPSY SEIZURE FOR MORE THAN 10 YEARS. V-EEG SHOWED MANY EPILEPTIC WAVES IN THE RIGHT PREFRONTAL AND ANTERIOR MIDDLE TEMPORAL REGIONS. (A) MRI T1WI SHOWED MARKED ATROPHY OF THE RIGHT HIPPOCAMPUS. (B, C) MRI T2WI AND FLAIR SHOWED THE RIGHT HIPPOCAMPUS HYPERINTENSITY. (D) INTERICTAL PET SCAN SHOWED HYPOMETABOLIC AREAS IN THE RIGHT TEMPORAL HIPPOCAMPUS. (E) PET/MRI SHOWED THE LESION IN THE RIGHT HIPPOCAMPUS. (F) UAI BY SHANGHAI UNITED IMAGING INTELLIGENT MEDICAL TECHNOLOGY CO. ALSO SHOWED MARKED ATROPHY OF THE RIGHT HIPPOCAMPUS. THE PATIENT UNDERWENT RIGHT ATL. THE PATHOLOGY CONFIRMED HS. BY YAO ET AL. (2309-2331)

各种成像方法在 HS 检测中的临床应用。一名 39 岁女性,癫痫发作超过 10 年。V-eeg 显示右侧前额叶和前中颞区有许多癫痫波。(a) mri t1wi 显示右侧海马体明显萎缩。(b, c) mri t2wi 和 flair 显示右侧海马高密度。 (d) 发作间期宠物扫描显示右侧颞叶海马代谢减退区。(e)PET/MRI显示右侧海马有病变。(f) 上海联影智能医疗科技有限公司的 UAI 也显示右侧海马明显萎缩。也显示右侧海马明显萎缩。患者接受了右侧 Atl.病理证实为 HS。BY YAO ET AL.(2309-2331)
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引用次数: 0
Editorial for "Magnetic Resonance Elastography Combined With PI-RADS v2.1 for the Identification of Clinically Significant Prostate Cancer". 磁共振弹性成像与 PI-RADS v2.1 结合用于临床重大前列腺癌的鉴别》的社论。
IF 3.3 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-11-12 DOI: 10.1002/jmri.29659
Kang-Lung Lee, Dimitri A Kessler
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引用次数: 0
Editorial for "Characterization of Brain Abnormalities in a Lactational Neurodevelopmental Poly I:C Rat Model of Schizophrenia and Depression Using Machine-Learning and Quantitative MRI". 使用机器学习和定量 MRI 分析哺乳期神经发育多聚 I:C 大鼠精神分裂症和抑郁症模型中大脑异常的特征》的编辑。
IF 3.3 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-11-11 DOI: 10.1002/jmri.29644
John D Port
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引用次数: 0
Quantitative Estimation of Iron and Fat Content in Prostate Cancer by Multiparametric MRI and Its Application in Optimizing D'Amico Score. 通过多参数磁共振成像定量估算前列腺癌中的铁和脂肪含量及其在优化达米科评分中的应用
IF 3.3 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-11-11 DOI: 10.1002/jmri.29661
Yunshu Zhao, Guangzheng Li, Zhen Tian, Mengying Zhu, Shuting Han, Minmin Jin, Yuhua Huang, Yonggang Li

Background: The risk of biochemical recurrence (BCR) in prostate cancer (PCa) is typically assessed using D'Amico score. However, iron and fat content in PCa are closely related to tumor cell proliferation and the risk of BCR may be estimated using multiparametric MRI (mpMRI).

Purpose: To noninvasively estimate fat and iron content in PCa and to evaluate their utility in enhancing D'Amico scores for predicting BCR in PCa patients.

Study type: Prospective.

Subjects: Forty-eight male patients in the BCR group (age 71.31 ± 5.74 years) and 27 male patients in the non-BCR group (age 70.3 ± 6.04 years).

Field strength/sequence: 3.0 T, Turbo-spin echo T2-weighted imaging, diffusion-weighted imaging (DWI), dynamic contrast-enhanced (DCE) imaging, Gradient echo Q-Dixon sequence.

Assessment: The mean fat fraction (FF) and T2* values of lesions were extracted from the FF map and the T2* map. Additionally, prostate volume, mean apparent diffusion coefficient (ADC) value, periprostatic fat thickness (PPFT), subcutaneous fat thickness (SFT), blood lipid content, pre- and post-operative prostate-specific antigen (PSA) values were collected.

Statistical tests: Stepwise-COX regression analysis was employed to identify the significant predictors of BCR, which led to the construction of an improvement-adjusted (IA) model. Then the IA model as well as the D'Amico score were evaluated using C-index and time-dependent AUC, decision-curve analysis, and Kaplan-Meier curve. P < 0.05 was statistically significant.

Results: Significant differences were observed in PSA, D'Amico score, ISUP grade, T2*, FF, and ADC values of the lesions in the BCR group compared with the non-BCR group. Mean T2*, FF, and ADC values of the lesions were screened to construct the IA model incorporated into the D'Amico score (IA Model: C-index = 0.749; AUC = 0.812; D'Amico score: C-index = 0.672; AUC = 0.723).

Data conclusion: This study demonstrated that mpMRI can quantitatively estimate fat and iron within PCa lesions. By integrating ADC, FF, and T2* values into the D'Amico score, the preoperative-risk assessment for BCR can be improved.

Evidence level: 2 TECHNICAL EFFICACY: Stage 2.

背景:前列腺癌(PCa)的生化复发(BCR)风险通常使用D'Amico评分进行评估。然而,PCa中的铁和脂肪含量与肿瘤细胞的增殖密切相关,因此可使用多参数磁共振成像(mpMRI)估算生化复发的风险。研究类型:前瞻性研究:研究对象48名BCR组男性患者(年龄为71.31 ± 5.74岁)和27名非BCR组男性患者(年龄为70.3 ± 6.04岁):3.0 T、涡轮自旋回波 T2 加权成像、弥散加权成像(DWI)、动态对比增强(DCE)成像、梯度回波 Q-Dixon 序列:从 FF 图和 T2* 图中提取病变的平均脂肪分数(FF)和 T2* 值。此外,还收集了前列腺体积、平均表观扩散系数(ADC)值、前列腺周围脂肪厚度(PPFT)、皮下脂肪厚度(SFT)、血脂含量、术前和术后前列腺特异性抗原(PSA)值:采用逐步-COX 回归分析法确定 BCR 的重要预测因素,从而建立改进调整(IA)模型。然后使用 C 指数和随时间变化的 AUC、决策曲线分析和 Kaplan-Meier 曲线对 IA 模型和 D'Amico 评分进行评估。P 结果:与非 BCR 组相比,BCR 组病变的 PSA、D'Amico 评分、ISUP 分级、T2*、FF 和 ADC 值均有显著差异。对病变的平均 T2*、FF 和 ADC 值进行筛选,以构建纳入 D'Amico 评分的 IA 模型(IA 模型:C-index = 0.749; AUC = 0.812; D'Amico score:数据结论:这项研究表明,mpMRI 可以定量估算 PCa 病灶内的脂肪和铁含量。通过将 ADC、FF 和 T2* 值纳入 D'Amico 评分,可以改善 BCR 的术前风险评估。
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引用次数: 0
Magnetic Resonance Elastography Combined With PI-RADS v2.1 for the Identification of Clinically Significant Prostate Cancer. 磁共振弹性成像与 PI-RADS v2.1 版相结合,用于鉴别具有临床意义的前列腺癌。
IF 3.3 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-11-08 DOI: 10.1002/jmri.29653
Jie Chen, Yuntian Chen, Guoyong Chen, Liping Deng, Yuan Yuan, Hehan Tang, Zhen Zhang, Tingyu Chen, Hao Zeng, Enyu Yuan, Meng Yin, Jun Chen, Bin Song, Jin Yao

Background: Multiparametric MRI may cause overdiagnosis of clinically significant prostate cancer (csPCa) with the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1).

Objectives: To investigate the diagnostic performance of stiffness as a standalone and complementary marker to PI-RADS v2.1 for diagnosing csPCa.

Study type: Prospective.

Subjects: One hundred forty-seven participants with pathologically confirmed prostate lesions (≥1 cm), including 71 with csPCa.

Field strength/sequence: T1-weighted fast spin-echo, T2-weighted fast spin-echo, single-shot echo-planar diffusion-weighted imaging, fast 3D gradient-echo T1-weighted dynamic contrast-enhanced imaging, and 3D single-shot spin-echo based echo-planar MR elastography at 3.0 T.

Assessment: The PI-RADS v2.1 score was assessed by three radiologists independently. Lesion shear stiffness (SS) values at 60 Hz and 90 Hz were measured. A modified PI-RADS integrating stiffness with PI-RADS v2.1 was developed. Diagnostic performance for csPCa was compared between stiffness, PI-RADS v2.1 and the modified PI-RADS.

Statistical test: Spearman's correlation, Fleiss κ and intraclass correlation, Pearson correlation, one-way analysis of variance, area under the receiver operating characteristic curve (AUC), and the Delong test. Significance level was P < 0.05.

Results: In the peripheral zone, csPCa (N = 35) had significantly higher SS than non-csPCa at 60 Hz (3.22 ± 0.66 kPa vs. 2.56 ± 0.56 kPa) and at 90 Hz (5.64 ± 1.30 kPa vs. 4.48 ± 0.84 kPa). PI-RADS v2.1 showed 100% sensitivity, 58% specificity, and 0.79 AUC for detecting csPCa. SS achieved 97% sensitivity, 52% specificity, and 0.80 AUC at 60 Hz, while SS had 63% sensitivity, 87% specificity, and 0.78 AUC at 90 Hz. The modified PI-RADS, combing SS at 60 Hz with PI-RADS v2.1, resulted in a significantly increased AUC (0.86) compared to that of PI-RADS v2.1, with a sensitivity of 97% and specificity of 75%.

Data conclusion: Stiffness can help identifying csPCa in the peripheral zone. Combining stiffness with the PI-RADS v2.1 improved the diagnostic accuracy and specificity for csPCa.

Evidence level: 1 TECHNICAL EFFICACY: Stage 2.

背景:使用前列腺成像报告和数据系统2.1版(PI-RADS v2.1)进行多参数磁共振成像可能会导致对有临床意义的前列腺癌(csPCa)的过度诊断:研究类型: 前瞻性:研究对象147名经病理证实患有前列腺病变(≥1厘米)的参与者,其中71人患有csPCa:场强/序列:T1加权快速自旋回波、T2加权快速自旋回波、单次回波平面弥散加权成像、快速三维梯度回波T1加权动态对比增强成像、基于回波平面的三维单次自旋回波MR弹性成像(3.0 T):PI-RADS v2.1 评分由三名放射科医生独立评估。测量病变在 60 Hz 和 90 Hz 下的剪切硬度(SS)值。开发了将僵硬度与 PI-RADS v2.1 相结合的改良 PI-RADS。对僵硬度、PI-RADS v2.1 和修改后的 PI-RADS 的 csPCa 诊断性能进行了比较:Spearman相关性、Fleiss κ和类内相关性、Pearson相关性、单因素方差分析、接收者操作特征曲线下面积(AUC)和Delong检验。显著性水平为 P 结果:在外周区,csPCa(N = 35)在 60 Hz(3.22 ± 0.66 kPa 对 2.56 ± 0.56 kPa)和 90 Hz(5.64 ± 1.30 kPa 对 4.48 ± 0.84 kPa)时的 SS 明显高于非 csPCa。PI-RADS v2.1 检测 csPCa 的灵敏度为 100%,特异性为 58%,AUC 为 0.79。在 60 Hz 时,SS 的灵敏度为 97%,特异性为 52%,AUC 为 0.80;在 90 Hz 时,SS 的灵敏度为 63%,特异性为 87%,AUC 为 0.78。与 PI-RADS v2.1 相比,将 60 Hz 时的 SS 与 PI-RADS v2.1 结合使用的改良 PI-RADS 使 AUC(0.86)显著增加,灵敏度为 97%,特异性为 75%:数据结论:僵硬度有助于识别外周区的 csPCa。将僵硬度与 PI-RADS v2.1 结合使用可提高 csPCa 的诊断准确性和特异性。
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引用次数: 0
Editorial for "Free Water MRI of White Matter in Wilson's Disease". 为 "威尔逊氏病白质的自由水磁共振成像 "撰写的社论。
IF 3.3 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2024-11-08 DOI: 10.1002/jmri.29662
Emanuele Siravo
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引用次数: 0
期刊
Journal of Magnetic Resonance Imaging
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