CT Cadaveric dataset for Radiomics features stability assessment in lumbar vertebrae

Abstract Radiomics features (RFs) studies have showed limitations in the reproducibility of RFs in different acquisition settings. To date, reproducibility studies using CT images mainly rely on phantoms, due to the harness of patient exposure to X-rays. The provided CadAIver dataset has the aims of...

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Main Authors: Riccardo Levi, Maximiliano Mollura, Giovanni Savini, Federico Garoli, Massimiliano Battaglia, Angela Ammirabile, Luca A. Cappellini, Simona Superbi, Marco Grimaldi, Riccardo Barbieri, Letterio S. Politi
Format: Article
Language:English
Published: Nature Portfolio 2024-04-01
Series:Scientific Data
Online Access:https://doi.org/10.1038/s41597-024-03191-6
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author Riccardo Levi
Maximiliano Mollura
Giovanni Savini
Federico Garoli
Massimiliano Battaglia
Angela Ammirabile
Luca A. Cappellini
Simona Superbi
Marco Grimaldi
Riccardo Barbieri
Letterio S. Politi
author_facet Riccardo Levi
Maximiliano Mollura
Giovanni Savini
Federico Garoli
Massimiliano Battaglia
Angela Ammirabile
Luca A. Cappellini
Simona Superbi
Marco Grimaldi
Riccardo Barbieri
Letterio S. Politi
author_sort Riccardo Levi
collection DOAJ
description Abstract Radiomics features (RFs) studies have showed limitations in the reproducibility of RFs in different acquisition settings. To date, reproducibility studies using CT images mainly rely on phantoms, due to the harness of patient exposure to X-rays. The provided CadAIver dataset has the aims of evaluating how CT scanner parameters effect radiomics features on cadaveric donor. The dataset comprises 112 unique CT acquisitions of a cadaveric truck acquired on 3 different CT scanners varying KV, mA, field-of-view, and reconstruction kernel settings. Technical validation of the CadAIver dataset comprises a comprehensive univariate and multivariate GLM approach to assess stability of each RFs extracted from lumbar vertebrae. The complete dataset is publicly available to be applied for future research in the RFs field, and could foster the creation of a collaborative open CT image database to increase the sample size, the range of available scanners, and the available body districts.
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spelling doaj.art-7f42edc8c9a04dff8c5ef0e732f797292024-04-14T11:07:27ZengNature PortfolioScientific Data2052-44632024-04-011111510.1038/s41597-024-03191-6CT Cadaveric dataset for Radiomics features stability assessment in lumbar vertebraeRiccardo Levi0Maximiliano Mollura1Giovanni Savini2Federico Garoli3Massimiliano Battaglia4Angela Ammirabile5Luca A. Cappellini6Simona Superbi7Marco Grimaldi8Riccardo Barbieri9Letterio S. Politi10Neuroradiology Department, IRCCS Humanitas Research HospitalDepartment of Electronic, Information and Bioengineering, Politecnico di MilanoNeuroradiology Department, IRCCS Humanitas Research HospitalNeuroradiology Department, IRCCS Humanitas Research HospitalNeuroradiology Department, IRCCS Humanitas Research HospitalNeuroradiology Department, IRCCS Humanitas Research HospitalNeuroradiology Department, IRCCS Humanitas Research HospitalNeuroradiology Department, IRCCS Humanitas Research HospitalNeuroradiology Department, IRCCS Humanitas Research HospitalDepartment of Electronic, Information and Bioengineering, Politecnico di MilanoNeuroradiology Department, IRCCS Humanitas Research HospitalAbstract Radiomics features (RFs) studies have showed limitations in the reproducibility of RFs in different acquisition settings. To date, reproducibility studies using CT images mainly rely on phantoms, due to the harness of patient exposure to X-rays. The provided CadAIver dataset has the aims of evaluating how CT scanner parameters effect radiomics features on cadaveric donor. The dataset comprises 112 unique CT acquisitions of a cadaveric truck acquired on 3 different CT scanners varying KV, mA, field-of-view, and reconstruction kernel settings. Technical validation of the CadAIver dataset comprises a comprehensive univariate and multivariate GLM approach to assess stability of each RFs extracted from lumbar vertebrae. The complete dataset is publicly available to be applied for future research in the RFs field, and could foster the creation of a collaborative open CT image database to increase the sample size, the range of available scanners, and the available body districts.https://doi.org/10.1038/s41597-024-03191-6
spellingShingle Riccardo Levi
Maximiliano Mollura
Giovanni Savini
Federico Garoli
Massimiliano Battaglia
Angela Ammirabile
Luca A. Cappellini
Simona Superbi
Marco Grimaldi
Riccardo Barbieri
Letterio S. Politi
CT Cadaveric dataset for Radiomics features stability assessment in lumbar vertebrae
Scientific Data
title CT Cadaveric dataset for Radiomics features stability assessment in lumbar vertebrae
title_full CT Cadaveric dataset for Radiomics features stability assessment in lumbar vertebrae
title_fullStr CT Cadaveric dataset for Radiomics features stability assessment in lumbar vertebrae
title_full_unstemmed CT Cadaveric dataset for Radiomics features stability assessment in lumbar vertebrae
title_short CT Cadaveric dataset for Radiomics features stability assessment in lumbar vertebrae
title_sort ct cadaveric dataset for radiomics features stability assessment in lumbar vertebrae
url https://doi.org/10.1038/s41597-024-03191-6
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