Department of Biomedical Engineering, Tel-Aviv University, Tel-Aviv, Israel.
Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel.
J Magn Reson Imaging. 2023 Aug;58(2):642-649. doi: 10.1002/jmri.28559. Epub 2022 Dec 9.
Magnetic resonance imaging (MRI) diagnosis is usually performed by analyzing contrast-weighted images, where pathology is detected once it reached a certain visual threshold. Computer-aided diagnosis (CAD) has been proposed as a way for achieving higher sensitivity to early pathology.
To compare conventional (i.e., visual) MRI assessment of artificially generated multiple sclerosis (MS) lesions in the brain's white matter to CAD based on a deep neural network.
Prospective.
A total of 25 neuroradiologists (15 males, age 39 ± 9, 9 ± 9.8 years of experience) independently assessed all synthetic lesions.
FIELD STRENGTH/SEQUENCE: A 3.0 T, T -weighted multi-echo spin-echo (MESE) sequence.
MS lesions of varying severity levels were artificially generated in healthy volunteer MRI scans by manipulating T values. Radiologists and a neural network were tasked with detecting these lesions in a series of 48 MR images. Sixteen images presented healthy anatomy and the rest contained a single lesion at eight increasing severity levels (6%, 9%, 12%, 15%, 18%, 21%, 25%, and 30% elevation in T ). True positive (TP) rates, false positive (FP) rates, and odds ratios (ORs) were compared between radiological diagnosis and CAD across the range lesion severity levels.
Diagnostic performance of the two approaches was compared using z-tests on TP rates, FP rates, and the logarithm of ORs across severity levels. A P-value <0.05 was considered statistically significant.
ORs of identifying pathology were significantly higher for CAD vis-à-vis visual inspection for all lesions' severity levels. For a 6% change in T value (lowest severity), radiologists' TP and FP rates were not significantly different (P = 0.12), while the corresponding CAD results remained statistically significant.
CAD is capable of detecting the presence or absence of more subtle lesions with greater precision than the representative group of 25 radiologists chosen in this study.
1 TECHNICAL EFFICACY: Stage 3.
磁共振成像(MRI)诊断通常通过分析对比加权图像进行,当病理学达到一定的视觉阈值时即可检测到。计算机辅助诊断(CAD)已被提出作为一种提高早期病理学敏感性的方法。
将基于深度神经网络的人工生成的多发性硬化症(MS)脑白质病变的传统(即视觉)MRI 评估与 CAD 进行比较。
前瞻性。
共有 25 名神经放射科医生(15 名男性,年龄 39±9,9±9.8 年经验)独立评估了所有合成病变。
场强/序列:3.0T,T1 加权多回波自旋回波(MESE)序列。
通过操纵 T 值,在健康志愿者的 MRI 扫描中人为生成不同严重程度水平的 MS 病变。放射科医生和神经网络的任务是在一系列 48 张 MRI 图像中检测这些病变。16 张图像呈现健康解剖结构,其余图像在 8 个逐渐增加的严重程度水平上包含单个病变(T 值升高 6%、9%、12%、15%、18%、21%、25%和 30%)。比较了放射学诊断和 CAD 在病变严重程度范围内的真阳性(TP)率、假阳性(FP)率和比值比(OR)。
使用 z 检验比较两种方法在严重程度水平上的 TP 率、FP 率和 OR 对数的诊断性能。P 值<0.05 被认为具有统计学意义。
对于所有病变严重程度水平,CAD 识别病理学的 OR 明显高于视觉检查。对于 T 值变化 6%(最低严重程度),放射科医生的 TP 和 FP 率没有显著差异(P=0.12),而相应的 CAD 结果仍然具有统计学意义。
CAD 能够比本研究中选择的 25 名代表性放射科医生更精确地检测到更细微病变的存在或不存在。
1 技术功效:3 级。