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Deep learning predicts immune checkpoint inhibitor-related pneumonitis from pretreatment computed tomography images

Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of lung cancer, including both non-small cell lung cancer and small cell lung cancer. Despite the promising results of immunotherapies, ICI-related pneumonitis (ICIP) is a potentially fatal adverse event. Therefore, early detectio...

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Autores principales: Tan, Peixin, Huang, Wei, Wang, Lingling, Deng, Guanhua, Yuan, Ye, Qiu, Shili, Ni, Dong, Du, Shasha, Cheng, Jun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9358138/
https://www.ncbi.nlm.nih.gov/pubmed/35957985
http://dx.doi.org/10.3389/fphys.2022.978222
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author Tan, Peixin
Huang, Wei
Wang, Lingling
Deng, Guanhua
Yuan, Ye
Qiu, Shili
Ni, Dong
Du, Shasha
Cheng, Jun
author_facet Tan, Peixin
Huang, Wei
Wang, Lingling
Deng, Guanhua
Yuan, Ye
Qiu, Shili
Ni, Dong
Du, Shasha
Cheng, Jun
author_sort Tan, Peixin
collection PubMed
description Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of lung cancer, including both non-small cell lung cancer and small cell lung cancer. Despite the promising results of immunotherapies, ICI-related pneumonitis (ICIP) is a potentially fatal adverse event. Therefore, early detection of patients at risk for developing ICIP before the initiation of immunotherapy is critical for alleviating future complications with early interventions and improving treatment outcomes. In this study, we present the first reported work that explores the potential of deep learning to predict patients who are at risk for developing ICIP. To this end, we collected the pretreatment baseline CT images and clinical information of 24 patients who developed ICIP after immunotherapy and 24 control patients who did not. A multimodal deep learning model was constructed based on 3D CT images and clinical data. To enhance performance, we employed two-stage transfer learning by pre-training the model sequentially on a large natural image dataset and a large CT image dataset, as well as transfer learning. Extensive experiments were conducted to verify the effectiveness of the key components used in our method. Using five-fold cross-validation, our method accurately distinguished ICIP patients from non-ICIP patients, with area under the receiver operating characteristic curve of 0.918 and accuracy of 0.920. This study demonstrates the promising potential of deep learning to identify patients at risk for developing ICIP. The proposed deep learning model enables efficient risk stratification, close monitoring, and prompt management of ICIP, ultimately leading to better treatment outcomes.
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spelling pubmed-93581382022-08-10 Deep learning predicts immune checkpoint inhibitor-related pneumonitis from pretreatment computed tomography images Tan, Peixin Huang, Wei Wang, Lingling Deng, Guanhua Yuan, Ye Qiu, Shili Ni, Dong Du, Shasha Cheng, Jun Front Physiol Physiology Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of lung cancer, including both non-small cell lung cancer and small cell lung cancer. Despite the promising results of immunotherapies, ICI-related pneumonitis (ICIP) is a potentially fatal adverse event. Therefore, early detection of patients at risk for developing ICIP before the initiation of immunotherapy is critical for alleviating future complications with early interventions and improving treatment outcomes. In this study, we present the first reported work that explores the potential of deep learning to predict patients who are at risk for developing ICIP. To this end, we collected the pretreatment baseline CT images and clinical information of 24 patients who developed ICIP after immunotherapy and 24 control patients who did not. A multimodal deep learning model was constructed based on 3D CT images and clinical data. To enhance performance, we employed two-stage transfer learning by pre-training the model sequentially on a large natural image dataset and a large CT image dataset, as well as transfer learning. Extensive experiments were conducted to verify the effectiveness of the key components used in our method. Using five-fold cross-validation, our method accurately distinguished ICIP patients from non-ICIP patients, with area under the receiver operating characteristic curve of 0.918 and accuracy of 0.920. This study demonstrates the promising potential of deep learning to identify patients at risk for developing ICIP. The proposed deep learning model enables efficient risk stratification, close monitoring, and prompt management of ICIP, ultimately leading to better treatment outcomes. Frontiers Media S.A. 2022-07-25 /pmc/articles/PMC9358138/ /pubmed/35957985 http://dx.doi.org/10.3389/fphys.2022.978222 Text en Copyright © 2022 Tan, Huang, Wang, Deng, Yuan, Qiu, Ni, Du and Cheng. 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 Physiology
Tan, Peixin
Huang, Wei
Wang, Lingling
Deng, Guanhua
Yuan, Ye
Qiu, Shili
Ni, Dong
Du, Shasha
Cheng, Jun
Deep learning predicts immune checkpoint inhibitor-related pneumonitis from pretreatment computed tomography images
title Deep learning predicts immune checkpoint inhibitor-related pneumonitis from pretreatment computed tomography images
title_full Deep learning predicts immune checkpoint inhibitor-related pneumonitis from pretreatment computed tomography images
title_fullStr Deep learning predicts immune checkpoint inhibitor-related pneumonitis from pretreatment computed tomography images
title_full_unstemmed Deep learning predicts immune checkpoint inhibitor-related pneumonitis from pretreatment computed tomography images
title_short Deep learning predicts immune checkpoint inhibitor-related pneumonitis from pretreatment computed tomography images
title_sort deep learning predicts immune checkpoint inhibitor-related pneumonitis from pretreatment computed tomography images
topic Physiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9358138/
https://www.ncbi.nlm.nih.gov/pubmed/35957985
http://dx.doi.org/10.3389/fphys.2022.978222
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