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A deep-learning-based prognostic nomogram integrating microscopic digital pathology and macroscopic magnetic resonance images in nasopharyngeal carcinoma: a multi-cohort study
BACKGROUND: To explore the prognostic value of radiomics-based and digital pathology-based imaging biomarkers from macroscopic magnetic resonance imaging (MRI) and microscopic whole-slide images for patients with nasopharyngeal carcinoma (NPC). METHODS: We recruited 220 NPC patients and divided them...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7739087/ https://www.ncbi.nlm.nih.gov/pubmed/33403013 http://dx.doi.org/10.1177/1758835920971416 |
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author | Zhang, Fan Zhong, Lian-Zhen Zhao, Xun Dong, Di Yao, Ji-Jin Wang, Si-Yang Liu, Ye Zhu, Ding Wang, Yin Wang, Guo-Jie Wang, Yi-Ming Li, Dan Wei, Jiang Tian, Jie Shan, Hong |
author_facet | Zhang, Fan Zhong, Lian-Zhen Zhao, Xun Dong, Di Yao, Ji-Jin Wang, Si-Yang Liu, Ye Zhu, Ding Wang, Yin Wang, Guo-Jie Wang, Yi-Ming Li, Dan Wei, Jiang Tian, Jie Shan, Hong |
author_sort | Zhang, Fan |
collection | PubMed |
description | BACKGROUND: To explore the prognostic value of radiomics-based and digital pathology-based imaging biomarkers from macroscopic magnetic resonance imaging (MRI) and microscopic whole-slide images for patients with nasopharyngeal carcinoma (NPC). METHODS: We recruited 220 NPC patients and divided them into training (n = 132), internal test (n = 44), and external test (n = 44) cohorts. The primary endpoint was failure-free survival (FFS). Radiomic features were extracted from pretreatment MRI and selected and integrated into a radiomic signature. The histopathological signature was extracted from whole-slide images of biopsy specimens using an end-to-end deep-learning method. Incorporating two signatures and independent clinical factors, a multi-scale nomogram was constructed. We also tested the correlation between the key imaging features and genetic alternations in an independent cohort of 16 patients (biological test cohort). RESULTS: Both radiomic and histopathologic signatures presented significant associations with treatment failure in the three cohorts (C-index: 0.689–0.779, all p < 0.050). The multi-scale nomogram showed a consistent significant improvement for predicting treatment failure compared with the clinical model in the training (C-index: 0.817 versus 0.730, p < 0.050), internal test (C-index: 0.828 versus 0.602, p < 0.050) and external test (C-index: 0.834 versus 0.679, p < 0.050) cohorts. Furthermore, patients were stratified successfully into two groups with distinguishable prognosis (log-rank p < 0.0010) using our nomogram. We also found that two texture features were related to the genetic alternations of chromatin remodeling pathways in another independent cohort. CONCLUSION: The multi-scale imaging features showed a complementary value in prognostic prediction and may improve individualized treatment in NPC. |
format | Online Article Text |
id | pubmed-7739087 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-77390872021-01-04 A deep-learning-based prognostic nomogram integrating microscopic digital pathology and macroscopic magnetic resonance images in nasopharyngeal carcinoma: a multi-cohort study Zhang, Fan Zhong, Lian-Zhen Zhao, Xun Dong, Di Yao, Ji-Jin Wang, Si-Yang Liu, Ye Zhu, Ding Wang, Yin Wang, Guo-Jie Wang, Yi-Ming Li, Dan Wei, Jiang Tian, Jie Shan, Hong Ther Adv Med Oncol Original Research BACKGROUND: To explore the prognostic value of radiomics-based and digital pathology-based imaging biomarkers from macroscopic magnetic resonance imaging (MRI) and microscopic whole-slide images for patients with nasopharyngeal carcinoma (NPC). METHODS: We recruited 220 NPC patients and divided them into training (n = 132), internal test (n = 44), and external test (n = 44) cohorts. The primary endpoint was failure-free survival (FFS). Radiomic features were extracted from pretreatment MRI and selected and integrated into a radiomic signature. The histopathological signature was extracted from whole-slide images of biopsy specimens using an end-to-end deep-learning method. Incorporating two signatures and independent clinical factors, a multi-scale nomogram was constructed. We also tested the correlation between the key imaging features and genetic alternations in an independent cohort of 16 patients (biological test cohort). RESULTS: Both radiomic and histopathologic signatures presented significant associations with treatment failure in the three cohorts (C-index: 0.689–0.779, all p < 0.050). The multi-scale nomogram showed a consistent significant improvement for predicting treatment failure compared with the clinical model in the training (C-index: 0.817 versus 0.730, p < 0.050), internal test (C-index: 0.828 versus 0.602, p < 0.050) and external test (C-index: 0.834 versus 0.679, p < 0.050) cohorts. Furthermore, patients were stratified successfully into two groups with distinguishable prognosis (log-rank p < 0.0010) using our nomogram. We also found that two texture features were related to the genetic alternations of chromatin remodeling pathways in another independent cohort. CONCLUSION: The multi-scale imaging features showed a complementary value in prognostic prediction and may improve individualized treatment in NPC. SAGE Publications 2020-12-14 /pmc/articles/PMC7739087/ /pubmed/33403013 http://dx.doi.org/10.1177/1758835920971416 Text en © The Author(s), 2020 https://creativecommons.org/licenses/by-nc/4.0/ This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Original Research Zhang, Fan Zhong, Lian-Zhen Zhao, Xun Dong, Di Yao, Ji-Jin Wang, Si-Yang Liu, Ye Zhu, Ding Wang, Yin Wang, Guo-Jie Wang, Yi-Ming Li, Dan Wei, Jiang Tian, Jie Shan, Hong A deep-learning-based prognostic nomogram integrating microscopic digital pathology and macroscopic magnetic resonance images in nasopharyngeal carcinoma: a multi-cohort study |
title | A deep-learning-based prognostic nomogram integrating microscopic digital pathology and macroscopic magnetic resonance images in nasopharyngeal carcinoma: a multi-cohort study |
title_full | A deep-learning-based prognostic nomogram integrating microscopic digital pathology and macroscopic magnetic resonance images in nasopharyngeal carcinoma: a multi-cohort study |
title_fullStr | A deep-learning-based prognostic nomogram integrating microscopic digital pathology and macroscopic magnetic resonance images in nasopharyngeal carcinoma: a multi-cohort study |
title_full_unstemmed | A deep-learning-based prognostic nomogram integrating microscopic digital pathology and macroscopic magnetic resonance images in nasopharyngeal carcinoma: a multi-cohort study |
title_short | A deep-learning-based prognostic nomogram integrating microscopic digital pathology and macroscopic magnetic resonance images in nasopharyngeal carcinoma: a multi-cohort study |
title_sort | deep-learning-based prognostic nomogram integrating microscopic digital pathology and macroscopic magnetic resonance images in nasopharyngeal carcinoma: a multi-cohort study |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7739087/ https://www.ncbi.nlm.nih.gov/pubmed/33403013 http://dx.doi.org/10.1177/1758835920971416 |
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