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自动化定量 MRI 报告在评估海马硬化症中的临床评估。

Clinical evaluation of automated quantitative MRI reports for assessment of hippocampal sclerosis.

机构信息

Centre for Medical Image Computing (CMIC), University College London, London, UK.

Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK.

出版信息

Eur Radiol. 2021 Jan;31(1):34-44. doi: 10.1007/s00330-020-07075-2. Epub 2020 Aug 4.

DOI:10.1007/s00330-020-07075-2
PMID:32749588
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7755617/
Abstract

OBJECTIVES

Hippocampal sclerosis (HS) is a common cause of temporal lobe epilepsy. Neuroradiological practice relies on visual assessment, but quantification of HS imaging biomarkers-hippocampal volume loss and T2 elevation-could improve detection. We tested whether quantitative measures, contextualised with normative data, improve rater accuracy and confidence.

METHODS

Quantitative reports (QReports) were generated for 43 individuals with epilepsy (mean age ± SD 40.0 ± 14.8 years, 22 men; 15 histologically unilateral HS; 5 bilateral; 23 MR-negative). Normative data was generated from 111 healthy individuals (age 40.0 ± 12.8 years, 52 men). Nine raters with different experience (neuroradiologists, trainees, and image analysts) assessed subjects' imaging with and without QReports. Raters assigned imaging normal, right, left, or bilateral HS. Confidence was rated on a 5-point scale.

RESULTS

Correct designation (normal/abnormal) was high and showed further trend-level improvement with QReports, from 87.5 to 92.5% (p = 0.07, effect size d = 0.69). Largest magnitude improvement (84.5 to 93.8%) was for image analysts (d = 0.87). For bilateral HS, QReports significantly improved overall accuracy, from 74.4 to 91.1% (p = 0.042, d = 0.7). Agreement with the correct diagnosis (kappa) tended to increase from 0.74 ('fair') to 0.86 ('excellent') with the report (p = 0.06, d = 0.81). Confidence increased when correctly assessing scans with the QReport (p < 0.001, η = 0.945).

CONCLUSIONS

QReports of HS imaging biomarkers can improve rater accuracy and confidence, particularly in challenging bilateral cases. Improvements were seen across all raters, with large effect sizes, greatest for image analysts. These findings may have positive implications for clinical radiology services and justify further validation in larger groups.

KEY POINTS

• Quantification of imaging biomarkers for hippocampal sclerosis-volume loss and raised T2 signal-could improve clinical radiological detection in challenging cases. • Quantitative reports for individual patients, contextualised with normative reference data, improved diagnostic accuracy and confidence in a group of nine raters, in particular for bilateral HS cases. • We present a pre-use clinical validation of an automated imaging assessment tool to assist clinical radiology reporting of hippocampal sclerosis, which improves detection accuracy.

摘要

目的

海马硬化(HS)是颞叶癫痫的常见病因。神经影像学实践依赖于视觉评估,但 HS 影像学生物标志物(海马体积损失和 T2 升高)的定量评估可以提高检测的准确性。我们测试了定量报告(QReport)是否能提高评估者的准确性和信心,这些报告结合了规范数据。

方法

为 43 名癫痫患者(平均年龄±标准差 40.0±14.8 岁,22 名男性;15 名组织学单侧 HS,5 名双侧 HS,23 名 MRI 阴性)生成了定量报告。从 111 名健康个体(年龄 40.0±12.8 岁,52 名男性)中生成了规范数据。9 名具有不同经验的评估者(神经放射科医生、培训生和图像分析师)使用和不使用 QReport 评估了受试者的影像学。评估者将影像学结果指定为正常、右侧、左侧或双侧 HS。置信度评分采用 5 分制。

结果

正确的指定(正常/异常)率很高,并且随着 QReport 的使用,准确率呈趋势性提高,从 87.5%提高到 92.5%(p=0.07,效应量 d=0.69)。图像分析师的改进幅度最大(从 84.5%提高到 93.8%,d=0.87)。对于双侧 HS,QReport 显著提高了整体准确率,从 74.4%提高到 91.1%(p=0.042,d=0.7)。有报告时,与正确诊断的一致性(kappa)从 0.74(“一般”)倾向于增加到 0.86(“优秀”)(p=0.06,d=0.81)。当使用 QReport 正确评估扫描时,信心会增加(p<0.001,η=0.945)。

结论

HS 影像学生物标志物的 QReport 可以提高评估者的准确性和信心,特别是在具有挑战性的双侧病例中。所有评估者都有改善,图像分析师的效果最大,效应量为 d=0.81。这些发现可能对临床放射科服务有积极影响,并证明在更大的人群中进一步验证是合理的。

关键点

  1. 海马硬化的影像学生物标志物(体积损失和 T2 信号升高)的定量评估可以提高在具有挑战性的病例中临床放射学检测的准确性。

  2. 为个别患者生成的、结合规范参考数据的 QReport,可以提高 9 名评估者的诊断准确性和信心,特别是在双侧 HS 病例中。

  3. 我们提出了一种自动化影像学评估工具的临床前使用验证,以协助临床放射科对海马硬化的报告,从而提高检测的准确性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2183/7755617/10ce35b440c8/330_2020_7075_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2183/7755617/4906d18f819a/330_2020_7075_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2183/7755617/fb974bcaaabc/330_2020_7075_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2183/7755617/10ce35b440c8/330_2020_7075_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2183/7755617/4906d18f819a/330_2020_7075_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2183/7755617/fb974bcaaabc/330_2020_7075_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2183/7755617/10ce35b440c8/330_2020_7075_Fig3_HTML.jpg

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