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Integrated multi-dimensional deep neural network model improves prognosis prediction of advanced NSCLC patients receiving bevacizumab

BACKGROUND: The addition of bevacizumab was found to be associated with prolonged survival whether in combination with chemotherapy, tyrosine kinase inhibitors or immune checkpoint inhibitors in the treatment landscape of advanced non-small cell lung cancer (NSCLC) patients. However, the biomarkers...

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Autores principales: Li, Butuo, Yang, Linlin, Jiang, Chao, Yao, Yueyuan, Li, Haoqian, Cheng, Shuping, Zou, Bing, Fan, Bingjie, Wang, Linlin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9972089/
https://www.ncbi.nlm.nih.gov/pubmed/36865790
http://dx.doi.org/10.3389/fonc.2023.1052147
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author Li, Butuo
Yang, Linlin
Jiang, Chao
Yao, Yueyuan
Li, Haoqian
Cheng, Shuping
Zou, Bing
Fan, Bingjie
Wang, Linlin
author_facet Li, Butuo
Yang, Linlin
Jiang, Chao
Yao, Yueyuan
Li, Haoqian
Cheng, Shuping
Zou, Bing
Fan, Bingjie
Wang, Linlin
author_sort Li, Butuo
collection PubMed
description BACKGROUND: The addition of bevacizumab was found to be associated with prolonged survival whether in combination with chemotherapy, tyrosine kinase inhibitors or immune checkpoint inhibitors in the treatment landscape of advanced non-small cell lung cancer (NSCLC) patients. However, the biomarkers for efficacy of bevacizumab were still largely unknown. This study aimed to develop a deep learning model to provide individual assessment of survival in advanced NSCLC patients receiving bevacizumab. METHODS: All data were retrospectively collected from a cohort of 272 radiological and pathological proven advanced non-squamous NSCLC patients. A novel multi-dimensional deep neural network (DNN) models were trained based on clinicopathological, inflammatory and radiomics features using DeepSurv and N-MTLR algorithm. And concordance index (C-index) and bier score was used to demonstrate the discriminatory and predictive capacity of the model. RESULTS: The integration of clinicopathologic, inflammatory and radiomics features representation was performed using DeepSurv and N-MTLR with the C-index of 0.712 and 0.701 in testing cohort. And Cox proportional hazard (CPH) and random survival forest (RSF) models were also developed after data pre-processing and feature selection with the C-index of 0.665 and 0.679 respectively. DeepSurv prognostic model, indicated with best performance, was used for individual prognosis prediction. And patients divided in high-risk group were significantly associated with inferior PFS (median PFS: 5.4 vs 13.1 months, P<0.0001) and OS (median OS: 16.4 vs 21.3 months, P<0.0001). CONCLUSIONS: The integration of clinicopathologic, inflammatory and radiomics features representation based on DeepSurv model exhibited superior predictive accuracy as non-invasive method to assist in patients counseling and guidance of optimal treatment strategies.
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spelling pubmed-99720892023-03-01 Integrated multi-dimensional deep neural network model improves prognosis prediction of advanced NSCLC patients receiving bevacizumab Li, Butuo Yang, Linlin Jiang, Chao Yao, Yueyuan Li, Haoqian Cheng, Shuping Zou, Bing Fan, Bingjie Wang, Linlin Front Oncol Oncology BACKGROUND: The addition of bevacizumab was found to be associated with prolonged survival whether in combination with chemotherapy, tyrosine kinase inhibitors or immune checkpoint inhibitors in the treatment landscape of advanced non-small cell lung cancer (NSCLC) patients. However, the biomarkers for efficacy of bevacizumab were still largely unknown. This study aimed to develop a deep learning model to provide individual assessment of survival in advanced NSCLC patients receiving bevacizumab. METHODS: All data were retrospectively collected from a cohort of 272 radiological and pathological proven advanced non-squamous NSCLC patients. A novel multi-dimensional deep neural network (DNN) models were trained based on clinicopathological, inflammatory and radiomics features using DeepSurv and N-MTLR algorithm. And concordance index (C-index) and bier score was used to demonstrate the discriminatory and predictive capacity of the model. RESULTS: The integration of clinicopathologic, inflammatory and radiomics features representation was performed using DeepSurv and N-MTLR with the C-index of 0.712 and 0.701 in testing cohort. And Cox proportional hazard (CPH) and random survival forest (RSF) models were also developed after data pre-processing and feature selection with the C-index of 0.665 and 0.679 respectively. DeepSurv prognostic model, indicated with best performance, was used for individual prognosis prediction. And patients divided in high-risk group were significantly associated with inferior PFS (median PFS: 5.4 vs 13.1 months, P<0.0001) and OS (median OS: 16.4 vs 21.3 months, P<0.0001). CONCLUSIONS: The integration of clinicopathologic, inflammatory and radiomics features representation based on DeepSurv model exhibited superior predictive accuracy as non-invasive method to assist in patients counseling and guidance of optimal treatment strategies. Frontiers Media S.A. 2023-02-14 /pmc/articles/PMC9972089/ /pubmed/36865790 http://dx.doi.org/10.3389/fonc.2023.1052147 Text en Copyright © 2023 Li, Yang, Jiang, Yao, Li, Cheng, Zou, Fan and Wang 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 Oncology
Li, Butuo
Yang, Linlin
Jiang, Chao
Yao, Yueyuan
Li, Haoqian
Cheng, Shuping
Zou, Bing
Fan, Bingjie
Wang, Linlin
Integrated multi-dimensional deep neural network model improves prognosis prediction of advanced NSCLC patients receiving bevacizumab
title Integrated multi-dimensional deep neural network model improves prognosis prediction of advanced NSCLC patients receiving bevacizumab
title_full Integrated multi-dimensional deep neural network model improves prognosis prediction of advanced NSCLC patients receiving bevacizumab
title_fullStr Integrated multi-dimensional deep neural network model improves prognosis prediction of advanced NSCLC patients receiving bevacizumab
title_full_unstemmed Integrated multi-dimensional deep neural network model improves prognosis prediction of advanced NSCLC patients receiving bevacizumab
title_short Integrated multi-dimensional deep neural network model improves prognosis prediction of advanced NSCLC patients receiving bevacizumab
title_sort integrated multi-dimensional deep neural network model improves prognosis prediction of advanced nsclc patients receiving bevacizumab
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9972089/
https://www.ncbi.nlm.nih.gov/pubmed/36865790
http://dx.doi.org/10.3389/fonc.2023.1052147
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