为无钆对比度增强乳腺磁共振成像提供人工智能生成的内容。

IF 3.3 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Pingping Wang, Hongyu Wang, Pin Nie, Yanli Dang, Rumei Liu, Mingzhu Qu, Jiawei Wang, Gengming Mu, Tianju Jia, Lei Shang, Kaiguo Zhu, Jun Feng, Baoying Chen
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引用次数: 0

摘要

背景:目的:为无钆对比剂增强乳腺 MRI 开发一个生成模型,并评估生成扫描的诊断效用:研究类型:回顾性研究:研究类型:回顾性研究。研究对象:276 名女性,共进行了 304 次乳腺 MRI 检查(49 ± 13 岁,243/61 人参加了培训/测试):场强/序列:ZOOMit 扩散加权成像(DWI)、T1 加权容积插值屏气检查(T1W VIBE)和 3.0 T 的轴向 T2 3D SPACE:开发了一个生成模型,利用对比前的 T1W VIBE 和 DWI 图像生成对比增强扫描。使用结构相似性指数(SSIM)、平均绝对误差(MAE)和 Dice 相似性系数对生成的图像和真实图像进行定量比较。三位分别有 8 年、5 年和 5 年经验的放射科医生采用五级评分法对人工智能生成的图像和真实图像在不同亚组中的图像质量和病灶可见度进行了独立评分。四名分别有 8 年、8 年、5 年和 5 年经验的乳腺放射科医生独立盲法解读了四种阅片方案:单独和结合人工智能生成扫描的未增强核磁共振成像方案、缩略核磁共振成像方案和全核磁共振成像方案:采用 t 检验和 McNemar 检验对结果进行评估。以病理诊断为参考标准,计算每种阅读方案的敏感性、特异性、阳性预测值(PPV)和阴性预测值(NPV)。A P 值 结果:在测试集中,生成的图像与真实图像显示出相似性(SSIM:0.935 ± 0.047 [SD],MAE:0.015 ± 0.012 [SD],Dice系数:0.726 ± 0.177 [SD])。在肿块、非肿块和良性病变亚组中,真实扫描与人工智能生成的扫描在病变可见度上没有明显差异。在未增强磁共振成像方案中加入人工智能生成的扫描结果可略微提高乳腺癌的检出率(灵敏度:92.86% 对 85.71%,净现值:76.92% 对 70.00%);与 AB-MRI 方案和全方案相比,诊断效用并不逊色(灵敏度:92.86%,95.24%;净现值:75.00%,81.82%):数据结论:人工智能生成的无钆对比剂增强乳腺 MRI 有可能提高未增强 MRI 检测乳腺癌的灵敏度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enabling AI-Generated Content for Gadolinium-Free Contrast-Enhanced Breast Magnetic Resonance Imaging.

Background: There is increasing interest in utilizing AI-generated content for gadolinium-free contrast-enhanced breast MRI.

Purpose: To develop a generative model for gadolinium-free contrast-enhanced breast MRI and evaluate the diagnostic utility of the generated scans.

Study type: Retrospective.

Population: Two hundred seventy-six women with 304 breast MRI examinations (49 ± 13 years, 243/61 for training/testing).

Field strength/sequence: ZOOMit diffusion-weighted imaging (DWI), T1-weighted volumetric interpolated breath-hold examination (T1W VIBE), and axial T2 3D SPACE at 3.0 T.

Assessment: A generative model was developed to generate contrast-enhanced scans using precontrast T1W VIBE and DWI images. The generated and real images were quantitatively compared using the structural similarity index (SSIM), mean absolute error (MAE), and Dice similarity coefficient. Three radiologists with 8, 5, and 5 years of experience independently rated the image quality and lesion visibility on AI-generated and real images within various subgroups using a five-point scale. Four breast radiologists, with 8, 8, 5, and 5 years of experience, independently and blindly interpreted four reading protocols: unenhanced MRI protocol alone and combined with AI-generated scans, abbreviated MRI protocol, and full-MRI protocol.

Statistical analysis: Results were assessed using t-tests and McNemar tests. Using pathology diagnosis as reference standard, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for each reading protocol. A P value <0.05 was considered significant.

Results: In the test set, the generated images showed similarity to the real images (SSIM: 0.935 ± 0.047 [SD], MAE: 0.015 ± 0.012 [SD], and Dice coefficient: 0.726 ± 0.177 [SD]). No significant difference in lesion visibility was observed between real and AI-generated scans of the mass, non-mass, and benign lesion subgroups. Adding AI-generated scans to the unenhanced MRI protocol slightly improved breast cancer detection (sensitivity: 92.86% vs. 85.71%, NPV: 76.92% vs. 70.00%); achieved non-inferior diagnostic utility compared to the AB-MRI protocol and full-protocol (sensitivity: 92.86%, 95.24%; NPV: 75.00%, 81.82%).

Data conclusion: AI-generated gadolinium-free contrast-enhanced breast MRI has potential to improve the sensitivity of unenhanced MRI in detecting breast cancer.

Evidence level: 4 TECHNICAL EFFICACY: Stage 3.

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来源期刊
CiteScore
9.70
自引率
6.80%
发文量
494
审稿时长
2 months
期刊介绍: The Journal of Magnetic Resonance Imaging (JMRI) is an international journal devoted to the timely publication of basic and clinical research, educational and review articles, and other information related to the diagnostic applications of magnetic resonance.
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