Reproducibility in Radiomics: A Comparison of Feature Extraction Methods and Two Independent Datasets Article Swipe
Hannah Thomas
,
Helen Y. C. Wang
,
Amal Joseph Varghese
,
Ellen M. Donovan
,
C. South
,
Helen Saxby
,
A. Nisbet
,
Vineet Prakash
,
Balu Krishna Sasidharan
,
Simon Pavamani
,
Delan Devakumar
,
Manu Mathew
,
Rajesh Isiah
,
Philip Evans
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202305.1872.v1
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202305.1872.v1
Radiomics involves the extraction of information from medical images not visible to the human eye. There is evidence these features can be used for treatment stratification and outcome prediction. However, there is much discussion about the reproducibility of results between different studies. This paper studies the reproducibility of CT texture features used in radiomics, comparing two feature extraction implementations namely Matlab toolkit and Pyradiomics when applied on independent datasets of CT scans of patients i) the open access RIDER dataset containing a set of repeat CT scans taken 15 minutes apart for 31 patients (RIDER Scan 1 and Scan 2 respectively) treated for lung cancer and ii) the open access HN1 dataset containing 137 patients treated for head and neck cancer. Gross tumor volume (GTV) manually outlined by an experienced observer available on both datasets was used. 43 common radiomics features available on Matlab and Pyradiomics were calculated using 2 intensity-level quantization methods with and without an intensity threshold. Cases were ranked for each feature for all combinations of quantization parameters and the Spearman’s rank coefficient, rs, calculated. Reproducibility was defined when a highly correlated feature in the RIDER dataset also correlated highly in the HN1 dataset and vice versa. 29 out of 43 reported stable features were found to be highly reproducible between Matlab and Pyradiomics implementations, having consistently high correlation in rank ordering for RIDER Scan 1 and RIDER Scan 2 (rs > 0.8). 18/43 reported features were common in RIDER and HN1 datasets, suggesting they may be agnostic to disease site. Useful radiomics features should be selected based on reproducibility. This study identified a set of features that meet this requirement and validated the methodology for evaluating reproducibility between datasets.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.20944/preprints202305.1872.v1
- https://www.preprints.org/manuscript/202305.1872/v1/download
- OA Status
- green
- Cited By
- 3
- References
- 16
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4378602323
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- OpenAlex ID
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https://openalex.org/W4378602323Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.20944/preprints202305.1872.v1Digital Object Identifier
- Title
-
Reproducibility in Radiomics: A Comparison of Feature Extraction Methods and Two Independent DatasetsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-05-26Full publication date if available
- Authors
-
Hannah Thomas, Helen Y. C. Wang, Amal Joseph Varghese, Ellen M. Donovan, C. South, Helen Saxby, A. Nisbet, Vineet Prakash, Balu Krishna Sasidharan, Simon Pavamani, Delan Devakumar, Manu Mathew, Rajesh Isiah, Philip EvansList of authors in order
- Landing page
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https://doi.org/10.20944/preprints202305.1872.v1Publisher landing page
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https://www.preprints.org/manuscript/202305.1872/v1/downloadDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.preprints.org/manuscript/202305.1872/v1/downloadDirect OA link when available
- Concepts
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Reproducibility, Computer science, Radiomics, Artificial intelligence, Pattern recognition (psychology), Feature extraction, MATLAB, Implementation, Data mining, Mathematics, Statistics, Programming language, Operating systemTop concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
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2025: 1, 2024: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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