Cargando…
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...
Autores principales: | , , , , , , , , |
---|---|
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 |
_version_ | 1784885380310368256 |
---|---|
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. |
format | Online Article Text |
id | pubmed-9913254 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT cavinatolara radiomicsbasedinterlesionrelationnetworktodescribe18ffmchpetctimagingphenotypesinprostatecancer AT sollinimartina radiomicsbasedinterlesionrelationnetworktodescribe18ffmchpetctimagingphenotypesinprostatecancer AT ragnialessandra radiomicsbasedinterlesionrelationnetworktodescribe18ffmchpetctimagingphenotypesinprostatecancer AT bartolifrancesco radiomicsbasedinterlesionrelationnetworktodescribe18ffmchpetctimagingphenotypesinprostatecancer AT zancaroberta radiomicsbasedinterlesionrelationnetworktodescribe18ffmchpetctimagingphenotypesinprostatecancer AT pasqualettifrancesco radiomicsbasedinterlesionrelationnetworktodescribe18ffmchpetctimagingphenotypesinprostatecancer AT marcianoandrea radiomicsbasedinterlesionrelationnetworktodescribe18ffmchpetctimagingphenotypesinprostatecancer AT ievafrancesca radiomicsbasedinterlesionrelationnetworktodescribe18ffmchpetctimagingphenotypesinprostatecancer AT erbapaolaanna radiomicsbasedinterlesionrelationnetworktodescribe18ffmchpetctimagingphenotypesinprostatecancer |