Outlier data in volume calculations of uterine fibroids comparing ellipsoid formula and voxel-based segmentation.

IF 2.9 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Viktor Bérczi, Kolos György Turtóczki, Szuzina Fazekas, Anna Dolla-Takács, Róbert Stollmayer, Pál Novák Kaposi, Ildikó Kalina, Bettina Katalin Budai
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Abstract

Background: The ellipsoidal formula is the most common method used to determine the volume of fibroids on MR images. Labor-intensive manual segmentation provides the opportunity to measure the volume of a given lesion on a voxel basis. The aim of this study is to compare the volume of the uterine fibroid calculated using voxel-based segmentation and the ellipsoid formula.

Methods: In this study, pretreatment MRI scans of patients who underwent uterine artery embolization due to symptomatic fibroids were retrospectively collected between 2016 and 2022. The volume data of the largest fibroids was determined by segmentation (group S) as the reference standard. In addition, the largest diameters of the fibroids in three planes (D1/D2/D3) were also measured and the volumes were also estimated by using the ellipsoidal formula (D1*D2*D3*0.5233) (group E). The interobserver reproducibility of the diameter measurements was tested. The volume values (median, IQR) were compared; in addition, the differences between the segmented and ellipsoidal volumes were recorded. Statistical analysis was performed using the Kruskal-Wallis test, Wilcoxon's two-sided signed rank test, intraclass correlation (ICC) analysis, and Bland-Altman plots.

Results: Pretreatment MRI scans of 113 patients were identified. Fibroids where the interobserver difference of diameter-based ellipsoidal volumes reached 30% were excluded resulting in 99 patients in the final dataset. The volumes of group S and group E showed no significant differences with 134.1 (257.3) cm3 and 133.5 (269.1) cm3, respectively, with an average difference of 3.47 cm3 (0.25%; p = 0.377). The agreement between the two methods was excellent (ICC = 0.979), without difference across fibroid locations. In 46 cases (46.5%), group S values were larger, and in 53 fibroids (53.5%), group E volume values were larger. However, volume difference was outside the ± 20% range in 21 cases (21.2%) and outside the ± 30% range in 10 cases (10.1%); the largest difference was approximately 56.5% (156.5 cm3).

Conclusions: The ellipsoid formula-based and the voxel-based volume calculation showed no significant difference for the group as a whole. However, there was a difference of > 20% in 21.2% of cases and > 30% in 10.1% of cases. In the era of personalized medicine, it is not only the average difference between the two methods that need to be considered but also cases where there is a 20% or 30% difference in results should be highlighted, as these may change the treatment plan in individual cases. This methodology should also be tested for other tumor-type volume calculations.

子宫肌瘤体积计算的异常数据比较椭球公式和基于体素的分割。
背景:椭球公式是最常用的方法,用于确定肌瘤的体积上的磁共振图像。劳动密集型的人工分割提供了在体素基础上测量给定病变体积的机会。本研究的目的是比较基于体素分割和椭球体公式计算的子宫肌瘤体积。方法:本研究回顾性收集2016 - 2022年间因症状性肌瘤行子宫动脉栓塞术患者的预处理MRI扫描。采用分割法(S组)确定最大肌瘤的体积数据作为参考标准。此外,还测量了三个平面(D1/D2/D3)的肌瘤最大直径,并采用椭球公式(D1*D2*D3*0.5233)估算肌瘤体积(E组)。测试了直径测量的观察者间再现性。比较体积值(中位数,IQR);此外,还记录了分割体和椭球体体积之间的差异。统计分析采用Kruskal-Wallis检验、Wilcoxon双侧符号秩检验、类内相关(ICC)分析和Bland-Altman图。结果:对113例患者进行MRI预处理扫描。排除了基于直径的椭球体积的观察者间差异达到30%的肌瘤,最终数据集中有99例患者。S组和E组的体积差异无统计学意义,分别为134.1 (257.3)cm3和133.5 (269.1)cm3,平均差异3.47 cm3 (0.25%);p = 0.377)。两种方法的一致性极好(ICC = 0.979),在不同的子宫肌瘤位置上无差异。46例(46.5%)肌瘤体积值大于S组,53例(53.5%)肌瘤体积值大于E组。21例(21.2%)体积差在±20%范围之外,10例(10.1%)体积差在±30%范围之外;最大的差异约为56.5% (156.5 cm3)。结论:基于椭球体公式的体积计算与基于体素的体积计算在整体上无显著性差异。然而,在21.2%的病例中,>差异为20%,在10.1%的病例中,>差异为30%。在个体化医疗时代,不仅要考虑两种方法的平均差异,也要重视结果相差20%或30%的情况,因为这可能会改变个别病例的治疗方案。这种方法也应该用于其他肿瘤类型的体积计算。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
BMC Medical Imaging
BMC Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
4.60
自引率
3.70%
发文量
198
审稿时长
27 weeks
期刊介绍: BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.
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