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Automated whole-slide images assessment of immune infiltration in resected non-small-cell lung cancer: towards better risk-stratification

BACKGROUND: High immune infiltration is associated with favourable prognosis in patients with non-small-cell lung cancer (NSCLC), but an automated workflow for characterizing immune infiltration, with high validity and reliability, remains to be developed. METHODS: We performed a multicentre retrosp...

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Autores principales: Lin, Huan, Pan, Xipeng, Feng, Zhengyun, Yan, Lixu, Hua, Junjie, Liang, Yanting, Han, Chu, Xu, Zeyan, Wang, Yumeng, Wu, Lin, Cui, Yanfen, Huang, Xiaomei, Shi, Zhenwei, Chen, Xin, Chen, Xiaobo, Zhang, Qingling, Liang, Changhong, Zhao, Ke, Li, Zhenhui, Liu, Zaiyi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9172185/
https://www.ncbi.nlm.nih.gov/pubmed/35672787
http://dx.doi.org/10.1186/s12967-022-03458-9
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author Lin, Huan
Pan, Xipeng
Feng, Zhengyun
Yan, Lixu
Hua, Junjie
Liang, Yanting
Han, Chu
Xu, Zeyan
Wang, Yumeng
Wu, Lin
Cui, Yanfen
Huang, Xiaomei
Shi, Zhenwei
Chen, Xin
Chen, Xiaobo
Zhang, Qingling
Liang, Changhong
Zhao, Ke
Li, Zhenhui
Liu, Zaiyi
author_facet Lin, Huan
Pan, Xipeng
Feng, Zhengyun
Yan, Lixu
Hua, Junjie
Liang, Yanting
Han, Chu
Xu, Zeyan
Wang, Yumeng
Wu, Lin
Cui, Yanfen
Huang, Xiaomei
Shi, Zhenwei
Chen, Xin
Chen, Xiaobo
Zhang, Qingling
Liang, Changhong
Zhao, Ke
Li, Zhenhui
Liu, Zaiyi
author_sort Lin, Huan
collection PubMed
description BACKGROUND: High immune infiltration is associated with favourable prognosis in patients with non-small-cell lung cancer (NSCLC), but an automated workflow for characterizing immune infiltration, with high validity and reliability, remains to be developed. METHODS: We performed a multicentre retrospective study of patients with completely resected NSCLC. We developed an image analysis workflow for automatically evaluating the density of CD3(+) and CD8(+) T-cells in the tumour regions on immunohistochemistry (IHC)-stained whole-slide images (WSIs), and proposed an immune scoring system “I-score” based on the automated assessed cell density. RESULTS: A discovery cohort (n = 145) and a validation cohort (n = 180) were used to assess the prognostic value of the I-score for disease-free survival (DFS). The I-score (two-category) was an independent prognostic factor after adjusting for other clinicopathologic factors. Compared with a low I-score (two-category), a high I-score was associated with significantly superior DFS in the discovery cohort (adjusted hazard ratio [HR], 0.54; 95% confidence interval [CI] 0.33–0.86; P = 0.010) and validation cohort (adjusted HR, 0.57; 95% CI 0.36–0.92; P = 0.022). The I-score improved the prognostic stratification when integrating it into the Cox proportional hazard regression models with other risk factors (discovery cohort, C-index 0.742 vs. 0.728; validation cohort, C-index 0.695 vs. 0.685). CONCLUSION: This automated workflow and immune scoring system would advance the clinical application of immune microenvironment evaluation and support the clinical decision making for patients with resected NSCLC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-022-03458-9.
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spelling pubmed-91721852022-06-08 Automated whole-slide images assessment of immune infiltration in resected non-small-cell lung cancer: towards better risk-stratification Lin, Huan Pan, Xipeng Feng, Zhengyun Yan, Lixu Hua, Junjie Liang, Yanting Han, Chu Xu, Zeyan Wang, Yumeng Wu, Lin Cui, Yanfen Huang, Xiaomei Shi, Zhenwei Chen, Xin Chen, Xiaobo Zhang, Qingling Liang, Changhong Zhao, Ke Li, Zhenhui Liu, Zaiyi J Transl Med Research BACKGROUND: High immune infiltration is associated with favourable prognosis in patients with non-small-cell lung cancer (NSCLC), but an automated workflow for characterizing immune infiltration, with high validity and reliability, remains to be developed. METHODS: We performed a multicentre retrospective study of patients with completely resected NSCLC. We developed an image analysis workflow for automatically evaluating the density of CD3(+) and CD8(+) T-cells in the tumour regions on immunohistochemistry (IHC)-stained whole-slide images (WSIs), and proposed an immune scoring system “I-score” based on the automated assessed cell density. RESULTS: A discovery cohort (n = 145) and a validation cohort (n = 180) were used to assess the prognostic value of the I-score for disease-free survival (DFS). The I-score (two-category) was an independent prognostic factor after adjusting for other clinicopathologic factors. Compared with a low I-score (two-category), a high I-score was associated with significantly superior DFS in the discovery cohort (adjusted hazard ratio [HR], 0.54; 95% confidence interval [CI] 0.33–0.86; P = 0.010) and validation cohort (adjusted HR, 0.57; 95% CI 0.36–0.92; P = 0.022). The I-score improved the prognostic stratification when integrating it into the Cox proportional hazard regression models with other risk factors (discovery cohort, C-index 0.742 vs. 0.728; validation cohort, C-index 0.695 vs. 0.685). CONCLUSION: This automated workflow and immune scoring system would advance the clinical application of immune microenvironment evaluation and support the clinical decision making for patients with resected NSCLC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-022-03458-9. BioMed Central 2022-06-07 /pmc/articles/PMC9172185/ /pubmed/35672787 http://dx.doi.org/10.1186/s12967-022-03458-9 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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
Lin, Huan
Pan, Xipeng
Feng, Zhengyun
Yan, Lixu
Hua, Junjie
Liang, Yanting
Han, Chu
Xu, Zeyan
Wang, Yumeng
Wu, Lin
Cui, Yanfen
Huang, Xiaomei
Shi, Zhenwei
Chen, Xin
Chen, Xiaobo
Zhang, Qingling
Liang, Changhong
Zhao, Ke
Li, Zhenhui
Liu, Zaiyi
Automated whole-slide images assessment of immune infiltration in resected non-small-cell lung cancer: towards better risk-stratification
title Automated whole-slide images assessment of immune infiltration in resected non-small-cell lung cancer: towards better risk-stratification
title_full Automated whole-slide images assessment of immune infiltration in resected non-small-cell lung cancer: towards better risk-stratification
title_fullStr Automated whole-slide images assessment of immune infiltration in resected non-small-cell lung cancer: towards better risk-stratification
title_full_unstemmed Automated whole-slide images assessment of immune infiltration in resected non-small-cell lung cancer: towards better risk-stratification
title_short Automated whole-slide images assessment of immune infiltration in resected non-small-cell lung cancer: towards better risk-stratification
title_sort automated whole-slide images assessment of immune infiltration in resected non-small-cell lung cancer: towards better risk-stratification
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9172185/
https://www.ncbi.nlm.nih.gov/pubmed/35672787
http://dx.doi.org/10.1186/s12967-022-03458-9
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