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Can 3D imaging modeling recognize functional tissue and predict liver failure? A retrospective study based on 3D modelling of the major hepatectomies after hepatic modulation

BACKGROUND: Thanks to the introduction of radiomics, 3d reconstruction can be able to analyse tissues and recognise true hypertrophy from non-functioning tissue in patients treated with major hepatectomies with hepatic modulation.The aim of this study is to evaluate the performance of 3D Imaging Mod...

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Autores principales: Vicente, Emilio, Quijano, Yolanda, Duran, Hipolito, Diaz, Eduardo, Fabra, Isabel, Malave, Luis, Ruiz, Pablo, Pizzuti, Giada, Naldini, Chiara, De Nobili, Giovanni, Caruso, Riccardo, Ferri, Valentina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10583474/
https://www.ncbi.nlm.nih.gov/pubmed/37853412
http://dx.doi.org/10.1186/s12893-023-02196-z
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author Vicente, Emilio
Quijano, Yolanda
Duran, Hipolito
Diaz, Eduardo
Fabra, Isabel
Malave, Luis
Ruiz, Pablo
Pizzuti, Giada
Naldini, Chiara
De Nobili, Giovanni
Caruso, Riccardo
Ferri, Valentina
author_facet Vicente, Emilio
Quijano, Yolanda
Duran, Hipolito
Diaz, Eduardo
Fabra, Isabel
Malave, Luis
Ruiz, Pablo
Pizzuti, Giada
Naldini, Chiara
De Nobili, Giovanni
Caruso, Riccardo
Ferri, Valentina
author_sort Vicente, Emilio
collection PubMed
description BACKGROUND: Thanks to the introduction of radiomics, 3d reconstruction can be able to analyse tissues and recognise true hypertrophy from non-functioning tissue in patients treated with major hepatectomies with hepatic modulation.The aim of this study is to evaluate the performance of 3D Imaging Modelling in predict liver failure. METHODS: Patients submitted to major hepatectomies after hepatic modulation at Sanchinarro University Hospital from May 2015 to October 2019 were analysed. Three-dimensional reconstruction was realised before and after surgical treatment. The volumetry of Future Liver Remnant was calculated, distinguishing in Functional Future Liver Remnant (FRFx) i.e. true hypertrophy tissue and Anatomic Future Liver Remnant (FRL) i.e. hypertrophy plus no functional tissue (oedema/congestion) These volumes were analysed in patients with and without post hepatic liver failure. RESULTS: Twenty-four procedures were realised (11 ALPPS and 13 PVE followed by major hepatectomy). Post hepatic liver failure grade B and C occurred in 6 patients. The ROC curve showed a better AUC for FRFxV (74%) with respect to FRLV (54%) in prediction PHLF > B. The increase of anatomical FRL (iFRL) was superior in the ALPPS group (120%) with respect to the PVE group (73%) (p = 0,041), while the increase of functional FRFX (iFRFx) was 35% in the ALLPS group and 46% in the PVE group (p > 0,05), showing no difference in the two groups. CONCLUSION: The 3D reconstruction model can allow optimal surgical planning, and through the use of specific algorithms, can contribute to differential functioning liver parenchyma of the FLR. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12893-023-02196-z.
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spelling pubmed-105834742023-10-19 Can 3D imaging modeling recognize functional tissue and predict liver failure? A retrospective study based on 3D modelling of the major hepatectomies after hepatic modulation Vicente, Emilio Quijano, Yolanda Duran, Hipolito Diaz, Eduardo Fabra, Isabel Malave, Luis Ruiz, Pablo Pizzuti, Giada Naldini, Chiara De Nobili, Giovanni Caruso, Riccardo Ferri, Valentina BMC Surg Research BACKGROUND: Thanks to the introduction of radiomics, 3d reconstruction can be able to analyse tissues and recognise true hypertrophy from non-functioning tissue in patients treated with major hepatectomies with hepatic modulation.The aim of this study is to evaluate the performance of 3D Imaging Modelling in predict liver failure. METHODS: Patients submitted to major hepatectomies after hepatic modulation at Sanchinarro University Hospital from May 2015 to October 2019 were analysed. Three-dimensional reconstruction was realised before and after surgical treatment. The volumetry of Future Liver Remnant was calculated, distinguishing in Functional Future Liver Remnant (FRFx) i.e. true hypertrophy tissue and Anatomic Future Liver Remnant (FRL) i.e. hypertrophy plus no functional tissue (oedema/congestion) These volumes were analysed in patients with and without post hepatic liver failure. RESULTS: Twenty-four procedures were realised (11 ALPPS and 13 PVE followed by major hepatectomy). Post hepatic liver failure grade B and C occurred in 6 patients. The ROC curve showed a better AUC for FRFxV (74%) with respect to FRLV (54%) in prediction PHLF > B. The increase of anatomical FRL (iFRL) was superior in the ALPPS group (120%) with respect to the PVE group (73%) (p = 0,041), while the increase of functional FRFX (iFRFx) was 35% in the ALLPS group and 46% in the PVE group (p > 0,05), showing no difference in the two groups. CONCLUSION: The 3D reconstruction model can allow optimal surgical planning, and through the use of specific algorithms, can contribute to differential functioning liver parenchyma of the FLR. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12893-023-02196-z. BioMed Central 2023-10-18 /pmc/articles/PMC10583474/ /pubmed/37853412 http://dx.doi.org/10.1186/s12893-023-02196-z Text en © The Author(s) 2023 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Vicente, Emilio
Quijano, Yolanda
Duran, Hipolito
Diaz, Eduardo
Fabra, Isabel
Malave, Luis
Ruiz, Pablo
Pizzuti, Giada
Naldini, Chiara
De Nobili, Giovanni
Caruso, Riccardo
Ferri, Valentina
Can 3D imaging modeling recognize functional tissue and predict liver failure? A retrospective study based on 3D modelling of the major hepatectomies after hepatic modulation
title Can 3D imaging modeling recognize functional tissue and predict liver failure? A retrospective study based on 3D modelling of the major hepatectomies after hepatic modulation
title_full Can 3D imaging modeling recognize functional tissue and predict liver failure? A retrospective study based on 3D modelling of the major hepatectomies after hepatic modulation
title_fullStr Can 3D imaging modeling recognize functional tissue and predict liver failure? A retrospective study based on 3D modelling of the major hepatectomies after hepatic modulation
title_full_unstemmed Can 3D imaging modeling recognize functional tissue and predict liver failure? A retrospective study based on 3D modelling of the major hepatectomies after hepatic modulation
title_short Can 3D imaging modeling recognize functional tissue and predict liver failure? A retrospective study based on 3D modelling of the major hepatectomies after hepatic modulation
title_sort can 3d imaging modeling recognize functional tissue and predict liver failure? a retrospective study based on 3d modelling of the major hepatectomies after hepatic modulation
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10583474/
https://www.ncbi.nlm.nih.gov/pubmed/37853412
http://dx.doi.org/10.1186/s12893-023-02196-z
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