[论文解读] A multicenter study on radiomic features from T$_2$-weighted images of a customized MR pelvic phantom setting the basis for robust radiomic models in clinics

Linda Bianchini, João Santinha+8 more|arXiv (Cornell University)|May 14, 2020

Radiomics and Machine Learning in Medical Imaging参考文献 39被引用 5

一句话总结

这项多中心研究通过在多个扫描仪和扫描协议下使用定制的盆腔体模，评估了T2加权MRI影像中放射组学特征的可重复性和可重现性。研究识别出ICC > 0.9且CCC > 0.9的稳健特征，建立了一套筛选对协议敏感特征的工作流程，并表明仅有4.6%–15.6%的特征在不同扫描仪间具有可靠的可重现性，其中仅2.0%的非形状特征具有纯形状信息。

ABSTRACT

In this study we investigated the repeatability and reproducibility of radiomic features extracted from MRI images and provide a workflow to identify robust features. 2D and 3D T$_2$-weighted images of a pelvic phantom were acquired on three scanners of two manufacturers and two magnetic field strengths. The repeatability and reproducibility of the radiomic features were assessed respectively by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC), considering repeated acquisitions with or without phantom repositioning, and with different scanner/acquisition type, and acquisition parameters. The features showing ICC/CCC > 0.9 were selected, and their dependence on shape information (Spearman's $ρ$> 0.8) was analyzed. They were classified for their ability to distinguish textures, after shuffling voxel intensities. From 944 2D features, 79.9% to 96.4% showed excellent repeatability in fixed position across all scanners. Much lower range (11.2% to 85.4%) was obtained after phantom repositioning. 3D extraction did not improve repeatability performance. Excellent reproducibility between scanners was observed in 4.6% to 15.6% of the features, at fixed imaging parameters. 82.4% to 94.9% of features showed excellent agreement when extracted from images acquired with TEs 5 ms apart (values decreased when increasing TE intervals) and 90.7% of the features exhibited excellent reproducibility for changes in TR. 2.0% of non-shape features were identified as providing only shape information. This study demonstrates that radiomic features are affected by specific MRI protocols. The use of our radiomic pelvic phantom allowed to identify unreliable features for radiomic analysis on T$_2$-weighted images. This paper proposes a general workflow to identify repeatable, reproducible, and informative radiomic features, fundamental to ensure robustness of clinical studies.

研究动机与目标

• 评估在多个MRI扫描仪和扫描协议下放射组学特征的可重复性和可重现性。
• 识别对采集参数、扫描仪类型和体模位置变化具有鲁棒性的放射组学特征。
• 为临床应用开发一种标准化的工作流程，以筛选出可靠、可重现且具有信息量的放射组学特征。
• 评估序列参数（TR、TE）以及形状信息对特征稳定性的影响。
• 通过剔除不可靠特征，为临床肿瘤学中稳健的放射组学建模奠定基础。

提出的方法

• 在两家制造商的三台扫描仪上，于两种场强下，采集定制盆腔体模的2D和3D T2加权MRI图像。
• 通过在体模位置固定和重新定位条件下进行重复扫描，使用组内相关系数（ICC）评估可重复性。
• 在不同采集参数下，使用组内一致性相关系数（CCC）评估跨扫描仪的可重现性。
• 利用Spearman等级相关系数（ρ > 0.8）分析特征对形状的依赖性，以区分形状相关特征与纹理相关特征。
• 通过打乱体素强度来评估特征区分纹理的能力，从而隔离非形状信息内容。
• 提出一种系统性工作流程，用于筛选ICC > 0.9、CCC > 0.9且形状依赖性低的特征，以供临床使用。

实验结果

研究问题

• RQ1 在体模位置固定的情况下，哪些放射组学特征表现出高可重复性？
• RQ2 在不同MRI扫描仪和采集协议下，放射组学特征的可重现性如何？
• RQ3 序列参数（TR、TE）在多大程度上影响放射组学特征的稳定性？
• RQ4 哪些特征主要由形状驱动而非纹理？如何对它们进行筛选？
• RQ5 在可重现且对组织纹理表征具有信息量的特征中，其占比是多少？

主要发现

• 在所有扫描仪中体模位置固定时，79.9%至96.4%的2D放射组学特征表现出优异的可重复性（ICC > 0.9）。
• 体模重新定位后，可重复性显著下降，ICC > 0.9的特征比例降至11.2%至85.4%。
• 与2D特征相比，3D特征提取并未提高可重复性。
• 在参数固定的情况下，仅有4.6%至15.6%的特征表现出优异的可重现性（CCC > 0.9）。
• TR变化下，90.7%的特征表现出优异的可重现性，而随着TE间隔增加，性能下降。
• 在非形状特征中，仅有2.0%的特征仅提供形状信息，表明大多数特征对纹理具有高度特异性。