Stanford DRO Toolkit: Digital Reference Objects for Standardization of Radiomic Features

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Abstract

Several institutions have developed image feature extraction software to compute quantitative descriptors of medical images for radiomics analyses. With radiomics increasingly proposed for use in research and clinical contexts, new techniques are necessary for standardizing and replicating radiomics findings across software implementations. We have developed a software toolkit for the creation of 3D digital reference objects with customizable size, shape, intensity, texture, and margin sharpness values. Using user-supplied input parameters, these objects are defined mathematically as continuous functions, discretized, and then saved as DICOM objects. Here, we present the definition of these objects, parameterized derivations of a subset of their radiomics values, computer code for object generation, example use cases, and a user-downloadable sample collection used for the examples cited in this paper.

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Repeatability and Reproducibility of Radiomic Features: A Systematic Review

Alberto Traverso, Leonard Wee, Andre Dekker … (2018)

Purpose: An ever-growing number of predictive models used to inform clinical decision making have included quantitative, computer-extracted imaging biomarkers, or “radiomic features.” Broadly generalizable validity of radiomics-assisted models may be impeded by concerns about reproducibility. We offer a qualitative synthesis of 41 studies that specifically investigated the repeatability and reproducibility of radiomic features, derived from a systematic review of published peer-reviewed literature. Methods and Materials: The PubMed electronic database was searched using combinations of the broad Haynes and Ingui filters along with a set of text words specific to cancer, radiomics (including texture analyses), reproducibility, and repeatability. This review has been reported in compliance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. From each full-text article, information was extracted regarding cancer type, class of radiomic feature examined, reporting quality of key processing steps, and statistical metric used to segregate stable features. Results: Among 624 unique records, 41 full-text articles were subjected to review. The studies primarily addressed non-small cell lung cancer and oropharyngeal cancer. Only 7 studies addressed in detail every methodologic aspect related to image acquisition, preprocessing, and feature extraction. The repeatability and reproducibility of radiomic features are sensitive at various degrees to processing details such as image acquisition settings, image reconstruction algorithm, digital image preprocessing, and software used to extract radiomic features. First-order features were overall more reproducible than shape metrics and textural features. Entropy was consistently reported as one of the most stable first-order features. There was no emergent consensus regarding either shape metrics or textural features; however, coarseness and contrast appeared among the least reproducible. Conclusions: Investigations of feature repeatability and reproducibility are currently limited to a small number of cancer types. Reporting quality could be improved regarding details of feature extraction software, digital image manipulation (preprocessing), and the cutoff value used to distinguish stable features.

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Measuring Computed Tomography Scanner Variability of Radiomics Features.

Dennis Mackin, Xenia Fave, Lifei Zhang … (2015)

The purpose of this study was to determine the significance of interscanner variability in CT image radiomics studies.

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IBEX: an open infrastructure software platform to facilitate collaborative work in radiomics.

Lifei Zhang, David V. Fried, Xenia Fave … (2015)

Radiomics, which is the high-throughput extraction and analysis of quantitative image features, has been shown to have considerable potential to quantify the tumor phenotype. However, at present, a lack of software infrastructure has impeded the development of radiomics and its applications. Therefore, the authors developed the imaging biomarker explorer (IBEX), an open infrastructure software platform that flexibly supports common radiomics workflow tasks such as multimodality image data import and review, development of feature extraction algorithms, model validation, and consistent data sharing among multiple institutions.

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Author and article information

Journal

Journal ID (nlm-ta): Tomography

Journal ID (iso-abbrev): Tomography

Journal ID (publisher-id): TOMOG

Title: Tomography

Publisher: Grapho Publications, LLC (Ann Abor, Michigan )

ISSN (Print): 2379-1381

ISSN (Electronic): 2379-139X

Publication date (Print): June 2020

Volume: 6

Issue: 2

Pages: 111-117

Affiliations

[1 ]Department of Radiology, Stanford University, Stanford, CA;

[2 ]Department of Medical Biophysics, The University of Western Ontario, London, Ontario, Canada; and

[3 ]Department of Radiology, University of California, Los Angeles, Los Angeles, CA

Author notes

Corresponding Author: Sandy Napel, PhD Department of Radiology, James H. Clark Center, 318 Campus Drive, Room S323, Stanford, CA 94305; E-mail: snapel@ 123456stanford.edu

Article

Publisher ID: TOMO.2019.00030

DOI: 10.18383/j.tom.2019.00030

PMC ID: 7289253

PubMed ID: 32548287

SO-VID: 26f514f5-f863-476b-b291-c020cbb0ea25

License:

This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

Stanford DRO Toolkit: Digital Reference Objects for Standardization of Radiomic Features

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Abstract

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Radiology and Natural Language Processing

Most cited references 21

Repeatability and Reproducibility of Radiomic Features: A Systematic Review

Measuring Computed Tomography Scanner Variability of Radiomics Features.

IBEX: an open infrastructure software platform to facilitate collaborative work in radiomics.

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