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Uniqueness of radiomic features in non‐small cell lung cancer

PURPOSE: The uniqueness of radiomic features, combined with their reproducibility, determines the reliability of radiomic studies. This study is to test the hypothesis that radiomic features extracted from a defined region of interest (ROI) are unique to the underlying structure (e.g., tumor). APPRO...

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Autores principales: Ge, Gary, Zhang, Jie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9797180/
https://www.ncbi.nlm.nih.gov/pubmed/36173022
http://dx.doi.org/10.1002/acm2.13787
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author Ge, Gary
Zhang, Jie
author_facet Ge, Gary
Zhang, Jie
author_sort Ge, Gary
collection PubMed
description PURPOSE: The uniqueness of radiomic features, combined with their reproducibility, determines the reliability of radiomic studies. This study is to test the hypothesis that radiomic features extracted from a defined region of interest (ROI) are unique to the underlying structure (e.g., tumor). APPROACH: Two cohorts of non‐small cell lung cancer (NSCLC) patients were retrospectively retrieved from a GE and a Siemens CT scanner. The lung nodules (ROI) were delineated manually and radiomic features were extracted using IBEX. The same ROI was then translocated randomly to four other tissue regions of the same set of images: adipose, heart, lung beyond nodule, and muscle for radiomic feature extraction. Coefficient of variation (CV) within different ROIs and concordance correlation coefficient (CCC) between lung nodule and a given tissue region were calculated to test to determine feature uniqueness. The radiomic features were considered nonunique when (1) the CV < 10% and CCC > 0.85 for over 50% of patients; and (2) the CCC > 0.85 appeared in ≥2 tissue regions beyond the defined region. RESULTS: In total, 14 patients from GE and 18 patients from Siemens are analyzed. The results show that 12 features fall below the 10% CV threshold for over 50% of patients in the GE cohort and 29 features in the Siemens cohort. According to CCC, 18 radiomic features in GE and 16 features in Siemens are identified as nonunique, with 11 overlapping features. Combining CV and CCC, 9 of 123 calculated features (7.3%) are identified as nonunique to a defined ROI. CONCLUSIONS: Radiomic feature uniqueness should be considered to improve the reliability of radiomics study.
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spelling pubmed-97971802022-12-30 Uniqueness of radiomic features in non‐small cell lung cancer Ge, Gary Zhang, Jie J Appl Clin Med Phys Radiation Oncology Physics PURPOSE: The uniqueness of radiomic features, combined with their reproducibility, determines the reliability of radiomic studies. This study is to test the hypothesis that radiomic features extracted from a defined region of interest (ROI) are unique to the underlying structure (e.g., tumor). APPROACH: Two cohorts of non‐small cell lung cancer (NSCLC) patients were retrospectively retrieved from a GE and a Siemens CT scanner. The lung nodules (ROI) were delineated manually and radiomic features were extracted using IBEX. The same ROI was then translocated randomly to four other tissue regions of the same set of images: adipose, heart, lung beyond nodule, and muscle for radiomic feature extraction. Coefficient of variation (CV) within different ROIs and concordance correlation coefficient (CCC) between lung nodule and a given tissue region were calculated to test to determine feature uniqueness. The radiomic features were considered nonunique when (1) the CV < 10% and CCC > 0.85 for over 50% of patients; and (2) the CCC > 0.85 appeared in ≥2 tissue regions beyond the defined region. RESULTS: In total, 14 patients from GE and 18 patients from Siemens are analyzed. The results show that 12 features fall below the 10% CV threshold for over 50% of patients in the GE cohort and 29 features in the Siemens cohort. According to CCC, 18 radiomic features in GE and 16 features in Siemens are identified as nonunique, with 11 overlapping features. Combining CV and CCC, 9 of 123 calculated features (7.3%) are identified as nonunique to a defined ROI. CONCLUSIONS: Radiomic feature uniqueness should be considered to improve the reliability of radiomics study. John Wiley and Sons Inc. 2022-09-29 /pmc/articles/PMC9797180/ /pubmed/36173022 http://dx.doi.org/10.1002/acm2.13787 Text en © 2022 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, LLC on behalf of The American Association of Physicists in Medicine. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Radiation Oncology Physics
Ge, Gary
Zhang, Jie
Uniqueness of radiomic features in non‐small cell lung cancer
title Uniqueness of radiomic features in non‐small cell lung cancer
title_full Uniqueness of radiomic features in non‐small cell lung cancer
title_fullStr Uniqueness of radiomic features in non‐small cell lung cancer
title_full_unstemmed Uniqueness of radiomic features in non‐small cell lung cancer
title_short Uniqueness of radiomic features in non‐small cell lung cancer
title_sort uniqueness of radiomic features in non‐small cell lung cancer
topic Radiation Oncology Physics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9797180/
https://www.ncbi.nlm.nih.gov/pubmed/36173022
http://dx.doi.org/10.1002/acm2.13787
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