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Influence of key histological characteristics on 18F-fluorodeoxyglucose /18F-choline positron emission tomography positivity in hepatocellular carcinoma: A machine learning study

PURPOSE: To determine the characteristics influence of key histological on 18F-fluorodeoxyglucose (18F-FDG) and 18F-choline positron emission tomography (PET) positivity in hepatocellular carcinoma (HCC). MATERIALS AND METHODS: The 18F-FDG/18F-choline PET imaging findings of 103 histologically prove...

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Autores principales: Ghidaglia, Jérôme, Laurent, Vincent, Sebagh, Mylène, Pascale, Alina, Durand, Emmanuel, Golse, Nicolas, Besson, Florent L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9892182/
https://www.ncbi.nlm.nih.gov/pubmed/36744142
http://dx.doi.org/10.3389/fmed.2023.1087957
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author Ghidaglia, Jérôme
Laurent, Vincent
Sebagh, Mylène
Pascale, Alina
Durand, Emmanuel
Golse, Nicolas
Besson, Florent L.
author_facet Ghidaglia, Jérôme
Laurent, Vincent
Sebagh, Mylène
Pascale, Alina
Durand, Emmanuel
Golse, Nicolas
Besson, Florent L.
author_sort Ghidaglia, Jérôme
collection PubMed
description PURPOSE: To determine the characteristics influence of key histological on 18F-fluorodeoxyglucose (18F-FDG) and 18F-choline positron emission tomography (PET) positivity in hepatocellular carcinoma (HCC). MATERIALS AND METHODS: The 18F-FDG/18F-choline PET imaging findings of 103 histologically proven HCCs (from 62 patients, of which 47 underwent hepatectomy and 15 received liver transplantation) were retrospectively examined to assess the following key histological parameters: Grade, capsule, microvascular invasion (mVI), macrovascular invasion (MVI), and necrosis. Using a ratio of 70/30 for training and testing sets, respectively, a penalized classification model (Elastic Net) was trained using 100 repeated cross-validation procedures (10-fold cross-validation for hyperparameter optimization). The contribution of each histological parameter to the PET positivity was determined using the Shapley Additive Explanations method. Receiver operating characteristic curves with and without dimensionality reduction were finally estimated and compared. RESULTS: Among the five key histological characteristics of HCC (Grade, capsule, mVI, MVI, and necrosis), mVI and tumor Grade (I–III) showed the highest relevance and robustness in explaining HCC uptake of 18F-FDG and 18F-choline. MVI and necrosis status both showed high instability in outcome predictions. Tumor capsule had a minimal influence on the model predictions. On retaining only mVI and Grades I–III for the final analysis, the area under the receiver operating characteristic (ROC) curve values were maintained (0.68 vs. 0.63, 0.65 vs. 0.64, and 0.65 vs. 0.64 for 18F-FDG, 18F-choline, and their combination, respectively). CONCLUSION: 18F-FDG/18F-choline PET positivity appears driven by both the Grade and mVI components in HCC. Consideration of the tumor microenvironment will likely be necessary to improve our understanding of multitracer PET positivity.
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spelling pubmed-98921822023-02-03 Influence of key histological characteristics on 18F-fluorodeoxyglucose /18F-choline positron emission tomography positivity in hepatocellular carcinoma: A machine learning study Ghidaglia, Jérôme Laurent, Vincent Sebagh, Mylène Pascale, Alina Durand, Emmanuel Golse, Nicolas Besson, Florent L. Front Med (Lausanne) Medicine PURPOSE: To determine the characteristics influence of key histological on 18F-fluorodeoxyglucose (18F-FDG) and 18F-choline positron emission tomography (PET) positivity in hepatocellular carcinoma (HCC). MATERIALS AND METHODS: The 18F-FDG/18F-choline PET imaging findings of 103 histologically proven HCCs (from 62 patients, of which 47 underwent hepatectomy and 15 received liver transplantation) were retrospectively examined to assess the following key histological parameters: Grade, capsule, microvascular invasion (mVI), macrovascular invasion (MVI), and necrosis. Using a ratio of 70/30 for training and testing sets, respectively, a penalized classification model (Elastic Net) was trained using 100 repeated cross-validation procedures (10-fold cross-validation for hyperparameter optimization). The contribution of each histological parameter to the PET positivity was determined using the Shapley Additive Explanations method. Receiver operating characteristic curves with and without dimensionality reduction were finally estimated and compared. RESULTS: Among the five key histological characteristics of HCC (Grade, capsule, mVI, MVI, and necrosis), mVI and tumor Grade (I–III) showed the highest relevance and robustness in explaining HCC uptake of 18F-FDG and 18F-choline. MVI and necrosis status both showed high instability in outcome predictions. Tumor capsule had a minimal influence on the model predictions. On retaining only mVI and Grades I–III for the final analysis, the area under the receiver operating characteristic (ROC) curve values were maintained (0.68 vs. 0.63, 0.65 vs. 0.64, and 0.65 vs. 0.64 for 18F-FDG, 18F-choline, and their combination, respectively). CONCLUSION: 18F-FDG/18F-choline PET positivity appears driven by both the Grade and mVI components in HCC. Consideration of the tumor microenvironment will likely be necessary to improve our understanding of multitracer PET positivity. Frontiers Media S.A. 2023-01-19 /pmc/articles/PMC9892182/ /pubmed/36744142 http://dx.doi.org/10.3389/fmed.2023.1087957 Text en Copyright © 2023 Ghidaglia, Laurent, Sebagh, Pascale, Durand, Golse and Besson. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Medicine
Ghidaglia, Jérôme
Laurent, Vincent
Sebagh, Mylène
Pascale, Alina
Durand, Emmanuel
Golse, Nicolas
Besson, Florent L.
Influence of key histological characteristics on 18F-fluorodeoxyglucose /18F-choline positron emission tomography positivity in hepatocellular carcinoma: A machine learning study
title Influence of key histological characteristics on 18F-fluorodeoxyglucose /18F-choline positron emission tomography positivity in hepatocellular carcinoma: A machine learning study
title_full Influence of key histological characteristics on 18F-fluorodeoxyglucose /18F-choline positron emission tomography positivity in hepatocellular carcinoma: A machine learning study
title_fullStr Influence of key histological characteristics on 18F-fluorodeoxyglucose /18F-choline positron emission tomography positivity in hepatocellular carcinoma: A machine learning study
title_full_unstemmed Influence of key histological characteristics on 18F-fluorodeoxyglucose /18F-choline positron emission tomography positivity in hepatocellular carcinoma: A machine learning study
title_short Influence of key histological characteristics on 18F-fluorodeoxyglucose /18F-choline positron emission tomography positivity in hepatocellular carcinoma: A machine learning study
title_sort influence of key histological characteristics on 18f-fluorodeoxyglucose /18f-choline positron emission tomography positivity in hepatocellular carcinoma: a machine learning study
topic Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9892182/
https://www.ncbi.nlm.nih.gov/pubmed/36744142
http://dx.doi.org/10.3389/fmed.2023.1087957
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