ArticleTomography (Ann Arbor, Mich.)2020
Stanford DRO Toolkit: Digital Reference Objects for Standardization of Radiomic Features.
Article in Tomography (Ann Arbor, Mich.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Technical and clinical considerations of a physical liver phantom for CT radiomics analysis.Journal of applied clinical medical physics · 2024Article
- Multi-disturbance identification from mine wind-velocity data based on MSSW and WPT-GBDT.PloS one · 2023Article
- Radiomics: a primer on high-throughput image phenotyping.Abdominal radiology (New York) · 2022Review
- Radiotherapy Standardisation and Artificial Intelligence within the National Cancer Institute's Clinical Trials Network.Clinical oncology (Royal College of Radiologists (Great Britain)) · 2022Article
- Quantitative assessment of acquisition imaging parameters on MRI radiomics features: a prospective anthropomorphic phantom study using a 3D-T2W-TSE sequence for MR-guided-radiotherapy.Quantitative imaging in medicine and surgery · 2021Article
- Understanding Sources of Variation to Improve the Reproducibility of Radiomics.Frontiers in oncology · 2021Review
- Cardiac Magnetic Resonance Radiomics Reveal Differential Impact of Sex, Age, and Vascular Risk Factors on Cardiac Structure and Myocardial Tissue.Frontiers in cardiovascular medicine · 2021Article
- Review of Value of CT Texture Analysis and Machine Learning in Differentiating Fat-Poor Renal Angiomyolipoma from Renal Cell Carcinoma.Tomography (Ann Arbor, Mich.) · 2020Review
- Standardization in Quantitative Imaging: A Multicenter Comparison of Radiomic Features from Different Software Packages on Digital Reference Objects and Patient Data Sets.Tomography (Ann Arbor, Mich.) · 2020Article
- Quantitative Imaging Enters the Clinical Arena: A Personal Viewpoint.Tomography (Ann Arbor, Mich.) · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.