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Radiomics-Based Inter-Lesion Relation Network to Describe [(18)F]FMCH PET/CT Imaging Phenotypes in Prostate Cancer

SIMPLE SUMMARY: Advanced image analysis, specifically radiomics, has been recognized as a potential source of biomarkers for cancers. However, there are challenges to its application in the clinic, such as proper description of diseases where multiple lesions coexist. In this study, we aimed to char...

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Autores principales: Cavinato, Lara, Sollini, Martina, Ragni, Alessandra, Bartoli, Francesco, Zanca, Roberta, Pasqualetti, Francesco, Marciano, Andrea, Ieva, Francesca, Erba, Paola Anna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9913254/
https://www.ncbi.nlm.nih.gov/pubmed/36765781
http://dx.doi.org/10.3390/cancers15030823
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author Cavinato, Lara
Sollini, Martina
Ragni, Alessandra
Bartoli, Francesco
Zanca, Roberta
Pasqualetti, Francesco
Marciano, Andrea
Ieva, Francesca
Erba, Paola Anna
author_facet Cavinato, Lara
Sollini, Martina
Ragni, Alessandra
Bartoli, Francesco
Zanca, Roberta
Pasqualetti, Francesco
Marciano, Andrea
Ieva, Francesca
Erba, Paola Anna
author_sort Cavinato, Lara
collection PubMed
description SIMPLE SUMMARY: Advanced image analysis, specifically radiomics, has been recognized as a potential source of biomarkers for cancers. However, there are challenges to its application in the clinic, such as proper description of diseases where multiple lesions coexist. In this study, we aimed to characterize the intra-tumor heterogeneity of metastatic prostate cancer using an innovative approach. This approach consisted of a transformation method to build a radiomic profile of lesions extracted from [(18)F]FMCH PET/CT images, a qualitative assessment of intra-tumor heterogeneity of patients, and a quantitative representation of the intra-tumor heterogeneity of patients in terms of the relationship between their lesions’ profiles. We found that metastatic prostate cancer patients had lesions with different radiomic profiles that exhibited intra-tumor radiomic heterogeneity and that the presence of many radiomic profiles within the same patient impacted the outcome. ABSTRACT: Advanced image analysis, including radiomics, has recently acquired recognition as a source of biomarkers, although there are some technical and methodological challenges to face for its application in the clinic. Among others, proper phenotyping of metastatic or systemic disease where multiple lesions coexist is an issue, since each lesion contributes to characterization of the disease. Therefore, the radiomic profile of each lesion should be modeled into a more complex architecture able to reproduce each “unit” (lesion) as a part of the “entire” (patient). This work aimed to characterize intra-tumor heterogeneity underpinning metastatic prostate cancer using an exhaustive innovative approach which consist of a i) feature transformation method to build an agnostic (i.e., irrespective of pre-existence knowledge, experience, and expertise) radiomic profile of lesions extracted from [(18)F]FMCH PET/CT, ii) qualitative assessment of intra-tumor heterogeneity of patients, iii) quantitative representation of the intra-tumor heterogeneity of patients in terms of the relationship between their lesions’ profiles, to be associated with prognostic factors. We confirmed that metastatic prostate cancer patients encompassed lesions with different radiomic profiles that exhibited intra-tumor radiomic heterogeneity and that the presence of many radiomic profiles within the same patient impacted the outcome.
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spelling pubmed-99132542023-02-11 Radiomics-Based Inter-Lesion Relation Network to Describe [(18)F]FMCH PET/CT Imaging Phenotypes in Prostate Cancer Cavinato, Lara Sollini, Martina Ragni, Alessandra Bartoli, Francesco Zanca, Roberta Pasqualetti, Francesco Marciano, Andrea Ieva, Francesca Erba, Paola Anna Cancers (Basel) Article SIMPLE SUMMARY: Advanced image analysis, specifically radiomics, has been recognized as a potential source of biomarkers for cancers. However, there are challenges to its application in the clinic, such as proper description of diseases where multiple lesions coexist. In this study, we aimed to characterize the intra-tumor heterogeneity of metastatic prostate cancer using an innovative approach. This approach consisted of a transformation method to build a radiomic profile of lesions extracted from [(18)F]FMCH PET/CT images, a qualitative assessment of intra-tumor heterogeneity of patients, and a quantitative representation of the intra-tumor heterogeneity of patients in terms of the relationship between their lesions’ profiles. We found that metastatic prostate cancer patients had lesions with different radiomic profiles that exhibited intra-tumor radiomic heterogeneity and that the presence of many radiomic profiles within the same patient impacted the outcome. ABSTRACT: Advanced image analysis, including radiomics, has recently acquired recognition as a source of biomarkers, although there are some technical and methodological challenges to face for its application in the clinic. Among others, proper phenotyping of metastatic or systemic disease where multiple lesions coexist is an issue, since each lesion contributes to characterization of the disease. Therefore, the radiomic profile of each lesion should be modeled into a more complex architecture able to reproduce each “unit” (lesion) as a part of the “entire” (patient). This work aimed to characterize intra-tumor heterogeneity underpinning metastatic prostate cancer using an exhaustive innovative approach which consist of a i) feature transformation method to build an agnostic (i.e., irrespective of pre-existence knowledge, experience, and expertise) radiomic profile of lesions extracted from [(18)F]FMCH PET/CT, ii) qualitative assessment of intra-tumor heterogeneity of patients, iii) quantitative representation of the intra-tumor heterogeneity of patients in terms of the relationship between their lesions’ profiles, to be associated with prognostic factors. We confirmed that metastatic prostate cancer patients encompassed lesions with different radiomic profiles that exhibited intra-tumor radiomic heterogeneity and that the presence of many radiomic profiles within the same patient impacted the outcome. MDPI 2023-01-29 /pmc/articles/PMC9913254/ /pubmed/36765781 http://dx.doi.org/10.3390/cancers15030823 Text en © 2023 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
Cavinato, Lara
Sollini, Martina
Ragni, Alessandra
Bartoli, Francesco
Zanca, Roberta
Pasqualetti, Francesco
Marciano, Andrea
Ieva, Francesca
Erba, Paola Anna
Radiomics-Based Inter-Lesion Relation Network to Describe [(18)F]FMCH PET/CT Imaging Phenotypes in Prostate Cancer
title Radiomics-Based Inter-Lesion Relation Network to Describe [(18)F]FMCH PET/CT Imaging Phenotypes in Prostate Cancer
title_full Radiomics-Based Inter-Lesion Relation Network to Describe [(18)F]FMCH PET/CT Imaging Phenotypes in Prostate Cancer
title_fullStr Radiomics-Based Inter-Lesion Relation Network to Describe [(18)F]FMCH PET/CT Imaging Phenotypes in Prostate Cancer
title_full_unstemmed Radiomics-Based Inter-Lesion Relation Network to Describe [(18)F]FMCH PET/CT Imaging Phenotypes in Prostate Cancer
title_short Radiomics-Based Inter-Lesion Relation Network to Describe [(18)F]FMCH PET/CT Imaging Phenotypes in Prostate Cancer
title_sort radiomics-based inter-lesion relation network to describe [(18)f]fmch pet/ct imaging phenotypes in prostate cancer
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9913254/
https://www.ncbi.nlm.nih.gov/pubmed/36765781
http://dx.doi.org/10.3390/cancers15030823
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