Multi-scale pathology image texture signature is a prognostic factor for resectable lung adenocarcinoma: a multi-center, retrospective study
Abstract Background Tumor histomorphology analysis plays a crucial role in predicting the prognosis of resectable lung adenocarcinoma (LUAD). Computer-extracted image texture features have been previously shown to be correlated with outcome. However, a comprehensive, quantitative, and interpretable...
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BMC
2022-12-01
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Series: | Journal of Translational Medicine |
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Online Access: | https://doi.org/10.1186/s12967-022-03777-x |
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author | Yumeng Wang Xipeng Pan Huan Lin Chu Han Yajun An Bingjiang Qiu Zhengyun Feng Xiaomei Huang Zeyan Xu Zhenwei Shi Xin Chen Bingbing Li Lixu Yan Cheng Lu Zhenhui Li Yanfen Cui Zaiyi Liu Zhenbing Liu |
author_facet | Yumeng Wang Xipeng Pan Huan Lin Chu Han Yajun An Bingjiang Qiu Zhengyun Feng Xiaomei Huang Zeyan Xu Zhenwei Shi Xin Chen Bingbing Li Lixu Yan Cheng Lu Zhenhui Li Yanfen Cui Zaiyi Liu Zhenbing Liu |
author_sort | Yumeng Wang |
collection | DOAJ |
description | Abstract Background Tumor histomorphology analysis plays a crucial role in predicting the prognosis of resectable lung adenocarcinoma (LUAD). Computer-extracted image texture features have been previously shown to be correlated with outcome. However, a comprehensive, quantitative, and interpretable predictor remains to be developed. Methods In this multi-center study, we included patients with resectable LUAD from four independent cohorts. An automated pipeline was designed for extracting texture features from the tumor region in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) at multiple magnifications. A multi-scale pathology image texture signature (MPIS) was constructed with the discriminative texture features in terms of overall survival (OS) selected by the LASSO method. The prognostic value of MPIS for OS was evaluated through univariable and multivariable analysis in the discovery set (n = 111) and the three external validation sets (V1, n = 115; V2, n = 116; and V3, n = 246). We constructed a Cox proportional hazards model incorporating clinicopathological variables and MPIS to assess whether MPIS could improve prognostic stratification. We also performed histo-genomics analysis to explore the associations between texture features and biological pathways. Results A set of eight texture features was selected to construct MPIS. In multivariable analysis, a higher MPIS was associated with significantly worse OS in the discovery set (HR 5.32, 95%CI 1.72–16.44; P = 0.0037) and the three external validation sets (V1: HR 2.63, 95%CI 1.10–6.29, P = 0.0292; V2: HR 2.99, 95%CI 1.34–6.66, P = 0.0075; V3: HR 1.93, 95%CI 1.15–3.23, P = 0.0125). The model that integrated clinicopathological variables and MPIS had better discrimination for OS compared to the clinicopathological variables-based model in the discovery set (C-index, 0.837 vs. 0.798) and the three external validation sets (V1: 0.704 vs. 0.679; V2: 0.728 vs. 0.666; V3: 0.696 vs. 0.669). Furthermore, the identified texture features were associated with biological pathways, such as cytokine activity, structural constituent of cytoskeleton, and extracellular matrix structural constituent. Conclusions MPIS was an independent prognostic biomarker that was robust and interpretable. Integration of MPIS with clinicopathological variables improved prognostic stratification in resectable LUAD and might help enhance the quality of individualized postoperative care. |
first_indexed | 2024-04-12T01:31:48Z |
format | Article |
id | doaj.art-69c7dfac3f984bfe881164568865dc3b |
institution | Directory Open Access Journal |
issn | 1479-5876 |
language | English |
last_indexed | 2024-04-12T01:31:48Z |
publishDate | 2022-12-01 |
publisher | BMC |
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series | Journal of Translational Medicine |
spelling | doaj.art-69c7dfac3f984bfe881164568865dc3b2022-12-22T03:53:27ZengBMCJournal of Translational Medicine1479-58762022-12-0120111710.1186/s12967-022-03777-xMulti-scale pathology image texture signature is a prognostic factor for resectable lung adenocarcinoma: a multi-center, retrospective studyYumeng Wang0Xipeng Pan1Huan Lin2Chu Han3Yajun An4Bingjiang Qiu5Zhengyun Feng6Xiaomei Huang7Zeyan Xu8Zhenwei Shi9Xin Chen10Bingbing Li11Lixu Yan12Cheng Lu13Zhenhui Li14Yanfen Cui15Zaiyi Liu16Zhenbing Liu17School of Computer Science and Information Security, Guilin University of Electronic TechnologySchool of Computer Science and Information Security, Guilin University of Electronic TechnologyDepartment of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical SciencesDepartment of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical SciencesSchool of Computer Science and Information Security, Guilin University of Electronic TechnologyDepartment of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical SciencesSchool of Computer Science and Information Security, Guilin University of Electronic TechnologyDepartment of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical SciencesDepartment of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical SciencesDepartment of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical SciencesDepartment of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of TechnologyDepartment of Pathology, Guangdong Provincial People’s Hospital Ganzhou Hospital (Ganzhou Municipal Hospital)Department of Pathology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical SciencesDepartment of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical SciencesDepartment of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical SciencesDepartment of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical SciencesDepartment of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical SciencesSchool of Computer Science and Information Security, Guilin University of Electronic TechnologyAbstract Background Tumor histomorphology analysis plays a crucial role in predicting the prognosis of resectable lung adenocarcinoma (LUAD). Computer-extracted image texture features have been previously shown to be correlated with outcome. However, a comprehensive, quantitative, and interpretable predictor remains to be developed. Methods In this multi-center study, we included patients with resectable LUAD from four independent cohorts. An automated pipeline was designed for extracting texture features from the tumor region in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) at multiple magnifications. A multi-scale pathology image texture signature (MPIS) was constructed with the discriminative texture features in terms of overall survival (OS) selected by the LASSO method. The prognostic value of MPIS for OS was evaluated through univariable and multivariable analysis in the discovery set (n = 111) and the three external validation sets (V1, n = 115; V2, n = 116; and V3, n = 246). We constructed a Cox proportional hazards model incorporating clinicopathological variables and MPIS to assess whether MPIS could improve prognostic stratification. We also performed histo-genomics analysis to explore the associations between texture features and biological pathways. Results A set of eight texture features was selected to construct MPIS. In multivariable analysis, a higher MPIS was associated with significantly worse OS in the discovery set (HR 5.32, 95%CI 1.72–16.44; P = 0.0037) and the three external validation sets (V1: HR 2.63, 95%CI 1.10–6.29, P = 0.0292; V2: HR 2.99, 95%CI 1.34–6.66, P = 0.0075; V3: HR 1.93, 95%CI 1.15–3.23, P = 0.0125). The model that integrated clinicopathological variables and MPIS had better discrimination for OS compared to the clinicopathological variables-based model in the discovery set (C-index, 0.837 vs. 0.798) and the three external validation sets (V1: 0.704 vs. 0.679; V2: 0.728 vs. 0.666; V3: 0.696 vs. 0.669). Furthermore, the identified texture features were associated with biological pathways, such as cytokine activity, structural constituent of cytoskeleton, and extracellular matrix structural constituent. Conclusions MPIS was an independent prognostic biomarker that was robust and interpretable. Integration of MPIS with clinicopathological variables improved prognostic stratification in resectable LUAD and might help enhance the quality of individualized postoperative care.https://doi.org/10.1186/s12967-022-03777-xLung adenocarcinomaPrognosisTexture analysisWhole slide imageArtificial intelligence |
spellingShingle | Yumeng Wang Xipeng Pan Huan Lin Chu Han Yajun An Bingjiang Qiu Zhengyun Feng Xiaomei Huang Zeyan Xu Zhenwei Shi Xin Chen Bingbing Li Lixu Yan Cheng Lu Zhenhui Li Yanfen Cui Zaiyi Liu Zhenbing Liu Multi-scale pathology image texture signature is a prognostic factor for resectable lung adenocarcinoma: a multi-center, retrospective study Journal of Translational Medicine Lung adenocarcinoma Prognosis Texture analysis Whole slide image Artificial intelligence |
title | Multi-scale pathology image texture signature is a prognostic factor for resectable lung adenocarcinoma: a multi-center, retrospective study |
title_full | Multi-scale pathology image texture signature is a prognostic factor for resectable lung adenocarcinoma: a multi-center, retrospective study |
title_fullStr | Multi-scale pathology image texture signature is a prognostic factor for resectable lung adenocarcinoma: a multi-center, retrospective study |
title_full_unstemmed | Multi-scale pathology image texture signature is a prognostic factor for resectable lung adenocarcinoma: a multi-center, retrospective study |
title_short | Multi-scale pathology image texture signature is a prognostic factor for resectable lung adenocarcinoma: a multi-center, retrospective study |
title_sort | multi scale pathology image texture signature is a prognostic factor for resectable lung adenocarcinoma a multi center retrospective study |
topic | Lung adenocarcinoma Prognosis Texture analysis Whole slide image Artificial intelligence |
url | https://doi.org/10.1186/s12967-022-03777-x |
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