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Impact of Lesion Delineation and Intensity Quantisation on the Stability of Texture Features from Lung Nodules on CT: A Reproducible Study
Computer-assisted analysis of three-dimensional imaging data (radiomics) has received a lot of research attention as a possible means to improve the management of patients with lung cancer. Building robust predictive models for clinical decision making requires the imaging features to be stable enou...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8304812/ https://www.ncbi.nlm.nih.gov/pubmed/34359305 http://dx.doi.org/10.3390/diagnostics11071224 |
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author | Bianconi, Francesco Fravolini, Mario Luca Palumbo, Isabella Pascoletti, Giulia Nuvoli, Susanna Rondini, Maria Spanu, Angela Palumbo, Barbara |
author_facet | Bianconi, Francesco Fravolini, Mario Luca Palumbo, Isabella Pascoletti, Giulia Nuvoli, Susanna Rondini, Maria Spanu, Angela Palumbo, Barbara |
author_sort | Bianconi, Francesco |
collection | PubMed |
description | Computer-assisted analysis of three-dimensional imaging data (radiomics) has received a lot of research attention as a possible means to improve the management of patients with lung cancer. Building robust predictive models for clinical decision making requires the imaging features to be stable enough to changes in the acquisition and extraction settings. Experimenting on 517 lung lesions from a cohort of 207 patients, we assessed the stability of 88 texture features from the following classes: first-order (13 features), Grey-level Co-Occurrence Matrix (24), Grey-level Difference Matrix (14), Grey-level Run-length Matrix (16), Grey-level Size Zone Matrix (16) and Neighbouring Grey-tone Difference Matrix (five). The analysis was based on a public dataset of lung nodules and open-access routines for feature extraction, which makes the study fully reproducible. Our results identified 30 features that had good or excellent stability relative to lesion delineation, 28 to intensity quantisation and 18 to both. We conclude that selecting the right set of imaging features is critical for building clinical predictive models, particularly when changes in lesion delineation and/or intensity quantisation are involved. |
format | Online Article Text |
id | pubmed-8304812 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-83048122021-07-25 Impact of Lesion Delineation and Intensity Quantisation on the Stability of Texture Features from Lung Nodules on CT: A Reproducible Study Bianconi, Francesco Fravolini, Mario Luca Palumbo, Isabella Pascoletti, Giulia Nuvoli, Susanna Rondini, Maria Spanu, Angela Palumbo, Barbara Diagnostics (Basel) Article Computer-assisted analysis of three-dimensional imaging data (radiomics) has received a lot of research attention as a possible means to improve the management of patients with lung cancer. Building robust predictive models for clinical decision making requires the imaging features to be stable enough to changes in the acquisition and extraction settings. Experimenting on 517 lung lesions from a cohort of 207 patients, we assessed the stability of 88 texture features from the following classes: first-order (13 features), Grey-level Co-Occurrence Matrix (24), Grey-level Difference Matrix (14), Grey-level Run-length Matrix (16), Grey-level Size Zone Matrix (16) and Neighbouring Grey-tone Difference Matrix (five). The analysis was based on a public dataset of lung nodules and open-access routines for feature extraction, which makes the study fully reproducible. Our results identified 30 features that had good or excellent stability relative to lesion delineation, 28 to intensity quantisation and 18 to both. We conclude that selecting the right set of imaging features is critical for building clinical predictive models, particularly when changes in lesion delineation and/or intensity quantisation are involved. MDPI 2021-07-06 /pmc/articles/PMC8304812/ /pubmed/34359305 http://dx.doi.org/10.3390/diagnostics11071224 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Bianconi, Francesco Fravolini, Mario Luca Palumbo, Isabella Pascoletti, Giulia Nuvoli, Susanna Rondini, Maria Spanu, Angela Palumbo, Barbara Impact of Lesion Delineation and Intensity Quantisation on the Stability of Texture Features from Lung Nodules on CT: A Reproducible Study |
title | Impact of Lesion Delineation and Intensity Quantisation on the Stability of Texture Features from Lung Nodules on CT: A Reproducible Study |
title_full | Impact of Lesion Delineation and Intensity Quantisation on the Stability of Texture Features from Lung Nodules on CT: A Reproducible Study |
title_fullStr | Impact of Lesion Delineation and Intensity Quantisation on the Stability of Texture Features from Lung Nodules on CT: A Reproducible Study |
title_full_unstemmed | Impact of Lesion Delineation and Intensity Quantisation on the Stability of Texture Features from Lung Nodules on CT: A Reproducible Study |
title_short | Impact of Lesion Delineation and Intensity Quantisation on the Stability of Texture Features from Lung Nodules on CT: A Reproducible Study |
title_sort | impact of lesion delineation and intensity quantisation on the stability of texture features from lung nodules on ct: a reproducible study |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8304812/ https://www.ncbi.nlm.nih.gov/pubmed/34359305 http://dx.doi.org/10.3390/diagnostics11071224 |
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