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Mapping Tumor Heterogeneity via Local Entropy Assessment: Making Biomarkers Visible
Advanced imaging and analysis improve prediction of pathology data and outcomes in several tumors, with entropy-based measures being among the most promising biomarkers. However, entropy is often perceived as statistical data lacking clinical significance. We aimed to generate a voxel-by-voxel visua...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10287605/ https://www.ncbi.nlm.nih.gov/pubmed/36849835 http://dx.doi.org/10.1007/s10278-023-00799-9 |
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author | Costa, Guido Cavinato, Lara Fiz, Francesco Sollini, Martina Chiti, Arturo Torzilli, Guido Ieva, Francesca Viganò, Luca |
author_facet | Costa, Guido Cavinato, Lara Fiz, Francesco Sollini, Martina Chiti, Arturo Torzilli, Guido Ieva, Francesca Viganò, Luca |
author_sort | Costa, Guido |
collection | PubMed |
description | Advanced imaging and analysis improve prediction of pathology data and outcomes in several tumors, with entropy-based measures being among the most promising biomarkers. However, entropy is often perceived as statistical data lacking clinical significance. We aimed to generate a voxel-by-voxel visual map of local tumor entropy, thus allowing to (1) make entropy explainable and accessible to clinicians; (2) disclose and quantitively characterize any intra-tumoral entropy heterogeneity; (3) evaluate associations between entropy and pathology data. We analyzed the portal phase of preoperative CT of 20 patients undergoing liver surgery for colorectal metastases. A three-dimensional core kernel (5 × 5 × 5 voxels) was created and used to compute the local entropy value for each voxel of the tumor. The map was encoded with a color palette. We performed two analyses: (a) qualitative assessment of tumors’ detectability and pattern of entropy distribution; (b) quantitative analysis of the entropy values distribution. The latter data were compared with standard Hounsfield data as predictors of post-chemotherapy tumor regression grade (TRG). Entropy maps were successfully built for all tumors. Metastases were qualitatively hyper-entropic compared to surrounding parenchyma. In four cases hyper-entropic areas exceeded the tumor margin visible at CT. We identified four “entropic” patterns: homogeneous, inhomogeneous, peripheral rim, and mixed. At quantitative analysis, entropy-derived data (percentiles/mean/median/root mean square) predicted TRG (p < 0.05) better than Hounsfield-derived ones (p = n.s.). We present a standardized imaging technique to visualize tumor heterogeneity built on a voxel-by-voxel entropy assessment. The association of local entropy with pathology data supports its role as a biomarker. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10278-023-00799-9. |
format | Online Article Text |
id | pubmed-10287605 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-102876052023-06-24 Mapping Tumor Heterogeneity via Local Entropy Assessment: Making Biomarkers Visible Costa, Guido Cavinato, Lara Fiz, Francesco Sollini, Martina Chiti, Arturo Torzilli, Guido Ieva, Francesca Viganò, Luca J Digit Imaging Article Advanced imaging and analysis improve prediction of pathology data and outcomes in several tumors, with entropy-based measures being among the most promising biomarkers. However, entropy is often perceived as statistical data lacking clinical significance. We aimed to generate a voxel-by-voxel visual map of local tumor entropy, thus allowing to (1) make entropy explainable and accessible to clinicians; (2) disclose and quantitively characterize any intra-tumoral entropy heterogeneity; (3) evaluate associations between entropy and pathology data. We analyzed the portal phase of preoperative CT of 20 patients undergoing liver surgery for colorectal metastases. A three-dimensional core kernel (5 × 5 × 5 voxels) was created and used to compute the local entropy value for each voxel of the tumor. The map was encoded with a color palette. We performed two analyses: (a) qualitative assessment of tumors’ detectability and pattern of entropy distribution; (b) quantitative analysis of the entropy values distribution. The latter data were compared with standard Hounsfield data as predictors of post-chemotherapy tumor regression grade (TRG). Entropy maps were successfully built for all tumors. Metastases were qualitatively hyper-entropic compared to surrounding parenchyma. In four cases hyper-entropic areas exceeded the tumor margin visible at CT. We identified four “entropic” patterns: homogeneous, inhomogeneous, peripheral rim, and mixed. At quantitative analysis, entropy-derived data (percentiles/mean/median/root mean square) predicted TRG (p < 0.05) better than Hounsfield-derived ones (p = n.s.). We present a standardized imaging technique to visualize tumor heterogeneity built on a voxel-by-voxel entropy assessment. The association of local entropy with pathology data supports its role as a biomarker. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10278-023-00799-9. Springer International Publishing 2023-02-27 2023-06 /pmc/articles/PMC10287605/ /pubmed/36849835 http://dx.doi.org/10.1007/s10278-023-00799-9 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 Costa, Guido Cavinato, Lara Fiz, Francesco Sollini, Martina Chiti, Arturo Torzilli, Guido Ieva, Francesca Viganò, Luca Mapping Tumor Heterogeneity via Local Entropy Assessment: Making Biomarkers Visible |
title | Mapping Tumor Heterogeneity via Local Entropy Assessment: Making Biomarkers Visible |
title_full | Mapping Tumor Heterogeneity via Local Entropy Assessment: Making Biomarkers Visible |
title_fullStr | Mapping Tumor Heterogeneity via Local Entropy Assessment: Making Biomarkers Visible |
title_full_unstemmed | Mapping Tumor Heterogeneity via Local Entropy Assessment: Making Biomarkers Visible |
title_short | Mapping Tumor Heterogeneity via Local Entropy Assessment: Making Biomarkers Visible |
title_sort | mapping tumor heterogeneity via local entropy assessment: making biomarkers visible |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10287605/ https://www.ncbi.nlm.nih.gov/pubmed/36849835 http://dx.doi.org/10.1007/s10278-023-00799-9 |
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