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Investigation of radiomics based intra-patient inter-tumor heterogeneity and the impact of tumor subsampling strategies

While radiomics analysis has been applied for localized cancer disease, its application to the metastatic setting involves a non-exhaustive lesion subsampling strategy which may sidestep the intrapatient tumoral heterogeneity, hindering the reproducibility and the therapeutic response performance. O...

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Autores principales: Henry, T., Sun, R., Lerousseau, M., Estienne, T., Robert, C., Besse, B., Paragios, N., Deutsch, E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9568579/
https://www.ncbi.nlm.nih.gov/pubmed/36241749
http://dx.doi.org/10.1038/s41598-022-20931-z
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author Henry, T.
Sun, R.
Lerousseau, M.
Estienne, T.
Robert, C.
Besse, B.
Robert, C.
Paragios, N.
Deutsch, E.
author_facet Henry, T.
Sun, R.
Lerousseau, M.
Estienne, T.
Robert, C.
Besse, B.
Robert, C.
Paragios, N.
Deutsch, E.
author_sort Henry, T.
collection PubMed
description While radiomics analysis has been applied for localized cancer disease, its application to the metastatic setting involves a non-exhaustive lesion subsampling strategy which may sidestep the intrapatient tumoral heterogeneity, hindering the reproducibility and the therapeutic response performance. Our aim was to evaluate if radiomics features can capture intertumoral intrapatient heterogeneity, and the impact of tumor subsampling on the computed heterogeneity. To this end, We delineated and extracted radiomics features of all visible tumors from single acquisition pre-treatment computed tomography of patients with metastatic lung cancer (cohort L) and confirmed our results on a larger cohort of patients with metastatic melanoma (cohort M). To quantify the captured heterogeneity, the absolute coefficient of variation (CV) of each radiomics index was calculated at the patient-level and a sensitivity analysis was performed using only a subset of all extracted features robust to the segmentation step. The extent of information loss by six commonly used tumor sampling strategies was then assessed. A total of 602 lesions were segmented from 43 patients (median age 57, 4.9% female). All robust radiomics indexes exhibited at least 20% of variation with significant heterogeneity both in heavily and oligo metastasized patients, and also at the organ level. None of the segmentation subsampling strategies were able to recover the true tumoral heterogeneity obtained by exhaustive tumor sampling. Image-based inter-tumor intra-patient heterogeneity can be successfully grasped by radiomics analyses. Failing to take into account this kind of heterogeneity will lead to inconsistent predictive algorithms. Guidelines to standardize the tumor sampling step and/or AI-driven tools to alleviate the segmentation effort are required.
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spelling pubmed-95685792022-10-16 Investigation of radiomics based intra-patient inter-tumor heterogeneity and the impact of tumor subsampling strategies Henry, T. Sun, R. Lerousseau, M. Estienne, T. Robert, C. Besse, B. Robert, C. Paragios, N. Deutsch, E. Sci Rep Article While radiomics analysis has been applied for localized cancer disease, its application to the metastatic setting involves a non-exhaustive lesion subsampling strategy which may sidestep the intrapatient tumoral heterogeneity, hindering the reproducibility and the therapeutic response performance. Our aim was to evaluate if radiomics features can capture intertumoral intrapatient heterogeneity, and the impact of tumor subsampling on the computed heterogeneity. To this end, We delineated and extracted radiomics features of all visible tumors from single acquisition pre-treatment computed tomography of patients with metastatic lung cancer (cohort L) and confirmed our results on a larger cohort of patients with metastatic melanoma (cohort M). To quantify the captured heterogeneity, the absolute coefficient of variation (CV) of each radiomics index was calculated at the patient-level and a sensitivity analysis was performed using only a subset of all extracted features robust to the segmentation step. The extent of information loss by six commonly used tumor sampling strategies was then assessed. A total of 602 lesions were segmented from 43 patients (median age 57, 4.9% female). All robust radiomics indexes exhibited at least 20% of variation with significant heterogeneity both in heavily and oligo metastasized patients, and also at the organ level. None of the segmentation subsampling strategies were able to recover the true tumoral heterogeneity obtained by exhaustive tumor sampling. Image-based inter-tumor intra-patient heterogeneity can be successfully grasped by radiomics analyses. Failing to take into account this kind of heterogeneity will lead to inconsistent predictive algorithms. Guidelines to standardize the tumor sampling step and/or AI-driven tools to alleviate the segmentation effort are required. Nature Publishing Group UK 2022-10-14 /pmc/articles/PMC9568579/ /pubmed/36241749 http://dx.doi.org/10.1038/s41598-022-20931-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Henry, T.
Sun, R.
Lerousseau, M.
Estienne, T.
Robert, C.
Besse, B.
Robert, C.
Paragios, N.
Deutsch, E.
Investigation of radiomics based intra-patient inter-tumor heterogeneity and the impact of tumor subsampling strategies
title Investigation of radiomics based intra-patient inter-tumor heterogeneity and the impact of tumor subsampling strategies
title_full Investigation of radiomics based intra-patient inter-tumor heterogeneity and the impact of tumor subsampling strategies
title_fullStr Investigation of radiomics based intra-patient inter-tumor heterogeneity and the impact of tumor subsampling strategies
title_full_unstemmed Investigation of radiomics based intra-patient inter-tumor heterogeneity and the impact of tumor subsampling strategies
title_short Investigation of radiomics based intra-patient inter-tumor heterogeneity and the impact of tumor subsampling strategies
title_sort investigation of radiomics based intra-patient inter-tumor heterogeneity and the impact of tumor subsampling strategies
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9568579/
https://www.ncbi.nlm.nih.gov/pubmed/36241749
http://dx.doi.org/10.1038/s41598-022-20931-z
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