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Prediction of In Vivo Laser-Induced Thermal Damage with Hyperspectral Imaging Using Deep Learning

Thermal ablation is an acceptable alternative treatment for primary liver cancer, of which laser ablation (LA) is one of the least invasive approaches, especially for tumors in high-risk locations. Precise control of the LA effect is required to safely destroy the tumor. Although temperature imaging...

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Autores principales: De Landro, Martina, Felli, Eric, Collins, Toby, Nkusi, Richard, Baiocchini, Andrea, Barberio, Manuel, Orrico, Annalisa, Pizzicannella, Margherita, Hostettler, Alexandre, Diana, Michele, Saccomandi, Paola
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8539534/
https://www.ncbi.nlm.nih.gov/pubmed/34696147
http://dx.doi.org/10.3390/s21206934
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author De Landro, Martina
Felli, Eric
Collins, Toby
Nkusi, Richard
Baiocchini, Andrea
Barberio, Manuel
Orrico, Annalisa
Pizzicannella, Margherita
Hostettler, Alexandre
Diana, Michele
Saccomandi, Paola
author_facet De Landro, Martina
Felli, Eric
Collins, Toby
Nkusi, Richard
Baiocchini, Andrea
Barberio, Manuel
Orrico, Annalisa
Pizzicannella, Margherita
Hostettler, Alexandre
Diana, Michele
Saccomandi, Paola
author_sort De Landro, Martina
collection PubMed
description Thermal ablation is an acceptable alternative treatment for primary liver cancer, of which laser ablation (LA) is one of the least invasive approaches, especially for tumors in high-risk locations. Precise control of the LA effect is required to safely destroy the tumor. Although temperature imaging techniques provide an indirect measurement of the thermal damage, a degree of uncertainty remains about the treatment effect. Optical techniques are currently emerging as tools to directly assess tissue thermal damage. Among them, hyperspectral imaging (HSI) has shown promising results in image-guided surgery and in the thermal ablation field. The highly informative data provided by HSI, associated with deep learning, enable the implementation of non-invasive prediction models to be used intraoperatively. Here we show a novel paradigm “peak temperature prediction model” (PTPM), convolutional neural network (CNN)-based, trained with HSI and infrared imaging to predict LA-induced damage in the liver. The PTPM demonstrated an optimal agreement with tissue damage classification providing a consistent threshold (50.6 ± 1.5 °C) for the damage margins with high accuracy (~0.90). The high correlation with the histology score (r = 0.9085) and the comparison with the measured peak temperature confirmed that PTPM preserves temperature information accordingly with the histopathological assessment.
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spelling pubmed-85395342021-10-24 Prediction of In Vivo Laser-Induced Thermal Damage with Hyperspectral Imaging Using Deep Learning De Landro, Martina Felli, Eric Collins, Toby Nkusi, Richard Baiocchini, Andrea Barberio, Manuel Orrico, Annalisa Pizzicannella, Margherita Hostettler, Alexandre Diana, Michele Saccomandi, Paola Sensors (Basel) Article Thermal ablation is an acceptable alternative treatment for primary liver cancer, of which laser ablation (LA) is one of the least invasive approaches, especially for tumors in high-risk locations. Precise control of the LA effect is required to safely destroy the tumor. Although temperature imaging techniques provide an indirect measurement of the thermal damage, a degree of uncertainty remains about the treatment effect. Optical techniques are currently emerging as tools to directly assess tissue thermal damage. Among them, hyperspectral imaging (HSI) has shown promising results in image-guided surgery and in the thermal ablation field. The highly informative data provided by HSI, associated with deep learning, enable the implementation of non-invasive prediction models to be used intraoperatively. Here we show a novel paradigm “peak temperature prediction model” (PTPM), convolutional neural network (CNN)-based, trained with HSI and infrared imaging to predict LA-induced damage in the liver. The PTPM demonstrated an optimal agreement with tissue damage classification providing a consistent threshold (50.6 ± 1.5 °C) for the damage margins with high accuracy (~0.90). The high correlation with the histology score (r = 0.9085) and the comparison with the measured peak temperature confirmed that PTPM preserves temperature information accordingly with the histopathological assessment. MDPI 2021-10-19 /pmc/articles/PMC8539534/ /pubmed/34696147 http://dx.doi.org/10.3390/s21206934 Text en © 2021 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
De Landro, Martina
Felli, Eric
Collins, Toby
Nkusi, Richard
Baiocchini, Andrea
Barberio, Manuel
Orrico, Annalisa
Pizzicannella, Margherita
Hostettler, Alexandre
Diana, Michele
Saccomandi, Paola
Prediction of In Vivo Laser-Induced Thermal Damage with Hyperspectral Imaging Using Deep Learning
title Prediction of In Vivo Laser-Induced Thermal Damage with Hyperspectral Imaging Using Deep Learning
title_full Prediction of In Vivo Laser-Induced Thermal Damage with Hyperspectral Imaging Using Deep Learning
title_fullStr Prediction of In Vivo Laser-Induced Thermal Damage with Hyperspectral Imaging Using Deep Learning
title_full_unstemmed Prediction of In Vivo Laser-Induced Thermal Damage with Hyperspectral Imaging Using Deep Learning
title_short Prediction of In Vivo Laser-Induced Thermal Damage with Hyperspectral Imaging Using Deep Learning
title_sort prediction of in vivo laser-induced thermal damage with hyperspectral imaging using deep learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8539534/
https://www.ncbi.nlm.nih.gov/pubmed/34696147
http://dx.doi.org/10.3390/s21206934
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