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Neural network based ensemble model to predict radiation induced lymphopenia after concurrent chemo-radiotherapy for non-small cell lung cancer from two institutions
The use of adjuvant Immune Checkpoint Inhibitors (ICI) after concurrent chemo-radiation therapy (CCRT) has become the standard of care for locally advanced non-small cell lung cancer (LA-NSCLC). However, prolonged radiotherapy regimens are known to cause radiation-induced lymphopenia (RIL), a long-n...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Neoplasia Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10025955/ https://www.ncbi.nlm.nih.gov/pubmed/36931040 http://dx.doi.org/10.1016/j.neo.2023.100889 |
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author | Kim, Yejin Chamseddine, Ibrahim Cho, Yeona Kim, Jin Sung Mohan, Radhe Shusharina, Nadya Paganetti, Harald Lin, Steven Yoon, Hong In Cho, Seungryong Grassberger, Clemens |
author_facet | Kim, Yejin Chamseddine, Ibrahim Cho, Yeona Kim, Jin Sung Mohan, Radhe Shusharina, Nadya Paganetti, Harald Lin, Steven Yoon, Hong In Cho, Seungryong Grassberger, Clemens |
author_sort | Kim, Yejin |
collection | PubMed |
description | The use of adjuvant Immune Checkpoint Inhibitors (ICI) after concurrent chemo-radiation therapy (CCRT) has become the standard of care for locally advanced non-small cell lung cancer (LA-NSCLC). However, prolonged radiotherapy regimens are known to cause radiation-induced lymphopenia (RIL), a long-neglected toxicity that has been shown to correlate with response to ICIs and survival of patients treated with adjuvant ICI after CCRT. In this study, we aim to develop a novel neural network (NN) approach that integrates patient characteristics, treatment related variables, and differential dose volume histograms (dDVH) of lung and heart to predict the incidence of RIL at the end of treatment. Multi-institutional data of 139 LA-NSCLC patients from two hospitals were collected for training and validation of our suggested model. Ensemble learning was combined with a bootstrap strategy to stabilize the model, which was evaluated internally using repeated cross validation. The performance of our proposed model was compared to conventional models using the same input features, such as Logistic Regression (LR) and Random Forests (RF), using the Area Under the Curve (AUC) of Receiver Operating Characteristics (ROC) curves. Our suggested model (AUC=0.77) outperformed the comparison models (AUC=0.72, 0.74) in terms of absolute performance, indicating that the convolutional structure of the network successfully abstracts additional information from the differential DVHs, which we studied using Gradient-weighted Class Activation Map. This study shows that clinical factors combined with dDVHs can be used to predict the risk of RIL for an individual patient and shows a path toward preventing lymphopenia using patient-specific modifications of the radiotherapy plan. |
format | Online Article Text |
id | pubmed-10025955 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Neoplasia Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-100259552023-03-21 Neural network based ensemble model to predict radiation induced lymphopenia after concurrent chemo-radiotherapy for non-small cell lung cancer from two institutions Kim, Yejin Chamseddine, Ibrahim Cho, Yeona Kim, Jin Sung Mohan, Radhe Shusharina, Nadya Paganetti, Harald Lin, Steven Yoon, Hong In Cho, Seungryong Grassberger, Clemens Neoplasia Radiation and Immunotherapy The use of adjuvant Immune Checkpoint Inhibitors (ICI) after concurrent chemo-radiation therapy (CCRT) has become the standard of care for locally advanced non-small cell lung cancer (LA-NSCLC). However, prolonged radiotherapy regimens are known to cause radiation-induced lymphopenia (RIL), a long-neglected toxicity that has been shown to correlate with response to ICIs and survival of patients treated with adjuvant ICI after CCRT. In this study, we aim to develop a novel neural network (NN) approach that integrates patient characteristics, treatment related variables, and differential dose volume histograms (dDVH) of lung and heart to predict the incidence of RIL at the end of treatment. Multi-institutional data of 139 LA-NSCLC patients from two hospitals were collected for training and validation of our suggested model. Ensemble learning was combined with a bootstrap strategy to stabilize the model, which was evaluated internally using repeated cross validation. The performance of our proposed model was compared to conventional models using the same input features, such as Logistic Regression (LR) and Random Forests (RF), using the Area Under the Curve (AUC) of Receiver Operating Characteristics (ROC) curves. Our suggested model (AUC=0.77) outperformed the comparison models (AUC=0.72, 0.74) in terms of absolute performance, indicating that the convolutional structure of the network successfully abstracts additional information from the differential DVHs, which we studied using Gradient-weighted Class Activation Map. This study shows that clinical factors combined with dDVHs can be used to predict the risk of RIL for an individual patient and shows a path toward preventing lymphopenia using patient-specific modifications of the radiotherapy plan. Neoplasia Press 2023-03-15 /pmc/articles/PMC10025955/ /pubmed/36931040 http://dx.doi.org/10.1016/j.neo.2023.100889 Text en © 2023 Published by Elsevier Inc. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Radiation and Immunotherapy Kim, Yejin Chamseddine, Ibrahim Cho, Yeona Kim, Jin Sung Mohan, Radhe Shusharina, Nadya Paganetti, Harald Lin, Steven Yoon, Hong In Cho, Seungryong Grassberger, Clemens Neural network based ensemble model to predict radiation induced lymphopenia after concurrent chemo-radiotherapy for non-small cell lung cancer from two institutions |
title | Neural network based ensemble model to predict radiation induced lymphopenia after concurrent chemo-radiotherapy for non-small cell lung cancer from two institutions |
title_full | Neural network based ensemble model to predict radiation induced lymphopenia after concurrent chemo-radiotherapy for non-small cell lung cancer from two institutions |
title_fullStr | Neural network based ensemble model to predict radiation induced lymphopenia after concurrent chemo-radiotherapy for non-small cell lung cancer from two institutions |
title_full_unstemmed | Neural network based ensemble model to predict radiation induced lymphopenia after concurrent chemo-radiotherapy for non-small cell lung cancer from two institutions |
title_short | Neural network based ensemble model to predict radiation induced lymphopenia after concurrent chemo-radiotherapy for non-small cell lung cancer from two institutions |
title_sort | neural network based ensemble model to predict radiation induced lymphopenia after concurrent chemo-radiotherapy for non-small cell lung cancer from two institutions |
topic | Radiation and Immunotherapy |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10025955/ https://www.ncbi.nlm.nih.gov/pubmed/36931040 http://dx.doi.org/10.1016/j.neo.2023.100889 |
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