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The Image Biomarker Standardization Initiative: Standardized Convolutional Filters for Reproducible Radiomics and Enhanced Clinical Insights

医学 再现性 组内相关 人工智能 特征(语言学) 模式识别(心理学) 滤波器(信号处理) 标准化 核医学 计算机科学 计算机视觉 数学 统计 语言学 操作系统 哲学
作者
P. Whybra,Alex Zwanenburg,Vincent Andrearczyk,Roger Schaer,Aditya Apte,Alexandre Ayotte,Bhakti Baheti,Spyridon Bakas,Andrea Bettinelli,Ronald Boellaard,Luca Boldrini,Irène Buvat,Gary Cook,Florian Dietsche,N. Dinapoli,Hubert S. Gabryś,Vicky Goh,Matthias Gückenberger,Mathieu Hatt,Mahdi Hosseinzadeh
出处
期刊:Radiology [Radiological Society of North America]
卷期号:310 (2): e231319-e231319 被引量:147
标识
DOI:10.1148/radiol.231319
摘要

Filters are commonly used to enhance specific structures and patterns in images, such as vessels or peritumoral regions, to enable clinical insights beyond the visible image using radiomics. However, their lack of standardization restricts reproducibility and clinical translation of radiomics decision support tools. In this special report, teams of researchers who developed radiomics software participated in a three-phase study (September 2020 to December 2022) to establish a standardized set of filters. The first two phases focused on finding reference filtered images and reference feature values for commonly used convolutional filters: mean, Laplacian of Gaussian, Laws and Gabor kernels, separable and nonseparable wavelets (including decomposed forms), and Riesz transformations. In the first phase, 15 teams used digital phantoms to establish 33 reference filtered images of 36 filter configurations. In phase 2, 11 teams used a chest CT image to derive reference values for 323 of 396 features computed from filtered images using 22 filter and image processing configurations. Reference filtered images and feature values for Riesz transformations were not established. Reproducibility of standardized convolutional filters was validated on a public data set of multimodal imaging (CT, fluorodeoxyglucose PET, and T1-weighted MRI) in 51 patients with soft-tissue sarcoma. At validation, reproducibility of 486 features computed from filtered images using nine configurations × three imaging modalities was assessed using the lower bounds of 95% CIs of intraclass correlation coefficients. Out of 486 features, 458 were found to be reproducible across nine teams with lower bounds of 95% CIs of intraclass correlation coefficients greater than 0.75. In conclusion, eight filter types were standardized with reference filtered images and reference feature values for verifying and calibrating radiomics software packages. A web-based tool is available for compliance checking.
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