协调对使用各种分割方法提取的 PET 放射体特征差异性的影响

IF 2.5 4区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Seyyed Ali Hosseini, Isaac Shiri, Pardis Ghaffarian, Ghasem Hajianfar, Atlas Haddadi Avval, Milad Seyfi, Stijn Servaes, Pedro Rosa-Neto, Habib Zaidi, Mohammad Reza Ay
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引用次数: 0

摘要

目的:本研究旨在检测非小细胞肺癌(NSCLC)患者在 ComBat 协调前后通过不同分割方法提取的正电子发射断层扫描(PET)放射学特征的稳健性。方法:我们纳入了 120 名患者(阳性复发患者 = 46 名,阴性复发患者 = 74 名),并将 PET 扫描作为常规治疗的一部分。所有患者均为活检证实的 NSCLC。对每张图像采用了九种分割方法,包括手动划分、K均值(KM)、分水岭、模糊C均值、区域生长、局部主动轮廓(LAC)和迭代阈值(IT)(阈值分别为40%、45%和50%)。对 PET 图像进行了不同的图像离散化处理,包括无滤波器和不同的小波分解。总体而言,从每幅图像中提取了 6741 个放射体特征(每个分割区域提取了 749 个放射体特征)。采用非参数经验贝叶斯(NPEB)ComBat 协调法对特征进行协调。使用 StratifiedKFold 对线性支持向量分类器(LinearSVC)进行特征选择,并使用支持向量机分类器(SVM)进行五重嵌套交叉验证,将 "n_splits "设为 5,以预测 NSCLC 患者的复发情况,并评估 ComBat 协调对结果的影响。结果在提取的749个放射学特征中,分别有206个(27%)和389个(51%)特征在NPEB ComBat协调前后对分割方法变化表现出极佳的可靠性(ICC≥0.90)。在所有特征中,有 39 个特征的可靠性较差,在 ComBat 协调后下降到 10 个。在 ComBat 统一前后,基于 64 个固定二进制宽度(不含任何滤波器)和小波(LLL)的放射体特征集在对抗各种分割技术时的鲁棒性表现最好。在 ComBat 协调前后,一阶和 GLRLM 以及一阶和 NGTDM 特征族分别显示了最多的鲁棒特征。在预测 NSCLC 复发方面,我们的研究结果表明,使用 ComBat 协调可以显著提高机器学习的结果,尤其是提高分水岭分割的准确性,因为最初分水岭分割的可靠特征比人工轮廓绘制的要少。应用 ComBat 协调技术后,大多数病例的灵敏度和特异性都有大幅提高。ComBat协调可被视为克服放射线学特征可靠性差问题的一种解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The effect of harmonization on the variability of PET radiomic features extracted using various segmentation methods

Purpose

This study aimed to examine the robustness of positron emission tomography (PET) radiomic features extracted via different segmentation methods before and after ComBat harmonization in patients with non-small cell lung cancer (NSCLC).

Methods

We included 120 patients (positive recurrence = 46 and negative recurrence = 74) referred for PET scanning as a routine part of their care. All patients had a biopsy-proven NSCLC. Nine segmentation methods were applied to each image, including manual delineation, K-means (KM), watershed, fuzzy-C-mean, region-growing, local active contour (LAC), and iterative thresholding (IT) with 40, 45, and 50% thresholds. Diverse image discretizations, both without a filter and with different wavelet decompositions, were applied to PET images. Overall, 6741 radiomic features were extracted from each image (749 radiomic features from each segmented area). Non-parametric empirical Bayes (NPEB) ComBat harmonization was used to harmonize the features. Linear Support Vector Classifier (LinearSVC) with L1 regularization For feature selection and Support Vector Machine classifier (SVM) with fivefold nested cross-validation was performed using StratifiedKFold with ‘n_splits’ set to 5 to predict recurrence in NSCLC patients and assess the impact of ComBat harmonization on the outcome.

Results

From 749 extracted radiomic features, 206 (27%) and 389 (51%) features showed excellent reliability (ICC ≥ 0.90) against segmentation method variation before and after NPEB ComBat harmonization, respectively. Among all, 39 features demonstrated poor reliability, which declined to 10 after ComBat harmonization. The 64 fixed bin widths (without any filter) and wavelets (LLL)-based radiomic features set achieved the best performance in terms of robustness against diverse segmentation techniques before and after ComBat harmonization. The first-order and GLRLM and also first-order and NGTDM feature families showed the largest number of robust features before and after ComBat harmonization, respectively. In terms of predicting recurrence in NSCLC, our findings indicate that using ComBat harmonization can significantly enhance machine learning outcomes, particularly improving the accuracy of watershed segmentation, which initially had fewer reliable features than manual contouring. Following the application of ComBat harmonization, the majority of cases saw substantial increase in sensitivity and specificity.

Conclusion

Radiomic features are vulnerable to different segmentation methods. ComBat harmonization might be considered a solution to overcome the poor reliability of radiomic features.

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来源期刊
Annals of Nuclear Medicine
Annals of Nuclear Medicine 医学-核医学
CiteScore
4.90
自引率
7.70%
发文量
111
审稿时长
4-8 weeks
期刊介绍: Annals of Nuclear Medicine is an official journal of the Japanese Society of Nuclear Medicine. It develops the appropriate application of radioactive substances and stable nuclides in the field of medicine. The journal promotes the exchange of ideas and information and research in nuclear medicine and includes the medical application of radionuclides and related subjects. It presents original articles, short communications, reviews and letters to the editor.
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