Suppr超能文献

测量方法对前列腺 MRI 病变表观扩散系数观察者间变异性的影响。

Impact of measurement method on interobserver variability of apparent diffusion coefficient of lesions in prostate MRI.

机构信息

Department of Radiology, Mayo Clinic, Rochester, Minnesota, United States of America.

Department of Radiology, Kanazawa University School of Medical Science, Ishikawa, Japan.

出版信息

PLoS One. 2022 May 23;17(5):e0268829. doi: 10.1371/journal.pone.0268829. eCollection 2022.

Abstract

PURPOSE

To compare the inter-observer variability of apparent diffusion coefficient (ADC) values of prostate lesions measured by 2D-region of interest (ROI) with and without specific measurement instruction.

METHODS

Forty lesions in 40 patients who underwent prostate MR followed by targeted prostate biopsy were evaluated. A multi-reader study (10 readers) was performed to assess the agreement of ADC values between 2D-ROI without specific instruction and 2D-ROI with specific instruction to place a 9-pixel size 2D-ROI covering the lowest ADC area. The computer script generated multiple overlapping 9-pixel 2D-ROIs within a 3D-ROI encompassing the entire lesion placed by a single reader. The lowest mean ADC values from each 2D-small-ROI were used as reference values. Inter-observer agreement was assessed using the Bland-Altman plot. Intraclass correlation coefficient (ICC) was assessed between ADC values measured by 10 readers and the computer-calculated reference values.

RESULTS

Ten lesions were benign, 6 were Gleason score 6 prostate carcinoma (PCa), and 24 were clinically significant PCa. The mean±SD ADC reference value by 9-pixel-ROI was 733 ± 186 (10-6 mm2/s). The 95% limits of agreement of ADC values among readers were better with specific instruction (±112) than those without (±205). ICC between reader-measured ADC values and computer-calculated reference values ranged from 0.736-0.949 with specific instruction and 0.349-0.919 without specific instruction.

CONCLUSION

Interobserver agreement of ADC values can be improved by indicating a measurement method (use of a specific ROI size covering the lowest ADC area).

摘要

目的

比较前列腺病变表观扩散系数(ADC)值的 2D 兴趣区(ROI)测量的观察者间变异性,有无特定测量指导。

方法

对 40 例接受前列腺 MRI 检查后行靶向前列腺活检的患者的 40 个病灶进行评估。进行了一项多读者研究(10 位读者),以评估无特定指导的 2D-ROI 和具有特定指导以放置覆盖最低 ADC 区域的 9 像素大小的 2D-ROI 的 ADC 值之间的一致性。计算机脚本在单个读者放置的包含整个病变的 3D-ROI 内生成多个重叠的 9 像素 2D-ROI。每个 2D-小 ROI 的最低平均 ADC 值用作参考值。使用 Bland-Altman 图评估观察者间一致性。评估了 10 位读者测量的 ADC 值与计算机计算的参考值之间的组内相关系数(ICC)。

结果

10 个病灶为良性,6 个为 Gleason 评分 6 前列腺癌(PCa),24 个为临床显著 PCa。9 像素 ROI 测量的平均±SD ADC 参考值为 733±186(10-6mm2/s)。有特定指导时(±112),读者间 ADC 值的 95%一致性界限比无特定指导时(±205)更好。有特定指导时,读者测量的 ADC 值与计算机计算的参考值之间的 ICC 范围为 0.736-0.949,无特定指导时为 0.349-0.919。

结论

通过指示测量方法(使用覆盖最低 ADC 区域的特定 ROI 大小)可以提高 ADC 值的观察者间一致性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e3c/9126398/f6a75c67948c/pone.0268829.g001.jpg

相似文献

1
Impact of measurement method on interobserver variability of apparent diffusion coefficient of lesions in prostate MRI.
PLoS One. 2022 May 23;17(5):e0268829. doi: 10.1371/journal.pone.0268829. eCollection 2022.
6
Apparent Diffusion Coefficient Values of Prostate Cancer: Comparison of 2D and 3D ROIs.
AJR Am J Roentgenol. 2018 Jan;210(1):113-117. doi: 10.2214/AJR.17.18495. Epub 2017 Oct 18.
8
Diffusion-Weighted MRI in Patients with Testicular Tumors-Intra- and Interobserver Variability.
Curr Oncol. 2022 Feb 2;29(2):837-847. doi: 10.3390/curroncol29020071.
10
Apparent diffusion coefficient (ADC) measurement in endometrial carcinoma: effect of region of interest methods on ADC values.
J Magn Reson Imaging. 2014 Jul;40(1):157-61. doi: 10.1002/jmri.24372. Epub 2013 Oct 31.

本文引用的文献

4
PI-RADS: what is new and how to use it.
Abdom Radiol (NY). 2020 Dec;45(12):3951-3960. doi: 10.1007/s00261-020-02482-x.
5
MRI of the Prostate With and Without Endorectal Coil at 3 T: Correlation With Whole-Mount Histopathologic Gleason Score.
AJR Am J Roentgenol. 2020 Jul;215(1):133-141. doi: 10.2214/AJR.19.22094. Epub 2020 Mar 11.
6
Correlations between Apparent Diffusion Coefficient and Gleason Score in Prostate Cancer: A Systematic Review.
Eur Urol Oncol. 2020 Aug;3(4):489-497. doi: 10.1016/j.euo.2018.12.006. Epub 2019 Jan 23.
8
RIL-Contour: a Medical Imaging Dataset Annotation Tool for and with Deep Learning.
J Digit Imaging. 2019 Aug;32(4):571-581. doi: 10.1007/s10278-019-00232-0.
9
Prostate Imaging Reporting and Data System Version 2.1: 2019 Update of Prostate Imaging Reporting and Data System Version 2.
Eur Urol. 2019 Sep;76(3):340-351. doi: 10.1016/j.eururo.2019.02.033. Epub 2019 Mar 18.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验