Nuclear shape, architecture and orientation features from H&E images are able to predict recurrence in node-negative gastric adenocarcinoma
Abstract Background Identifying intestinal node-negative gastric adenocarcinoma (INGA) patients with high risk of recurrence could help perceive benefit of adjuvant therapy for INGA patients following surgical resection. This study evaluated whether the computer-extracted image features of nuclear s...
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doaj-f42e2ecb53c24e028ad4b68f32a419392020-11-25T02:17:51ZengBMCJournal of Translational Medicine1479-58762019-03-0117111210.1186/s12967-019-1839-xNuclear shape, architecture and orientation features from H&E images are able to predict recurrence in node-negative gastric adenocarcinomaMeng-Yao Ji0Lei Yuan1Xiao-Da Jiang2Zhi Zeng3Na Zhan4Ping-Xiao Huang5Cheng Lu6Wei-Guo Dong7Department of Gastroenterology, Wuhan University Renmin HospitalDepartment of Information Center, Wuhan University Renmin HospitalDepartment of Gastroenterology, Wuhan University Renmin HospitalDepartment of Pathology, Wuhan University Renmin HospitalDepartment of Pathology, Wuhan University Renmin HospitalDepartment of Gastroenterology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and TechnologyCollege of Computer Science, Shaanxi Normal UniversityDepartment of Gastroenterology, Wuhan University Renmin HospitalAbstract Background Identifying intestinal node-negative gastric adenocarcinoma (INGA) patients with high risk of recurrence could help perceive benefit of adjuvant therapy for INGA patients following surgical resection. This study evaluated whether the computer-extracted image features of nuclear shapes, texture, orientation, and tumor architecture on digital images of hematoxylin and eosin stained tissue, could help to predict recurrence in INGA patients. Methods A tissue microarrays cohort of 160 retrospectively INGA cases were digitally scanned, and randomly selected as training cohort (D1 = 60), validation cohort (D2 = 100 and D3 = 100, D2 and D3 are different tumor TMA spots from the same patient), accompanied with immunohistochemistry data cohort (D3′ = 100, a duplicate cohort of D3) and negative controls data cohort (D5 = 100, normal adjacent tissues). After nuclear segmentation by watershed-based method, 189 local nuclear features were captured on each TMA core and the top 5 features were selected by Wilcoxon rank sum test within D1. A morphometric-based image classifier (NGAHIC) was composed across the discriminative features and predicted the recurrence in INGA on D2. The intra-tumor heterogeneity was assessed on D3. Manual nuclear atypia grading was conducted on D1 and D2 by two pathologists. The expression of HER2 and Ki67 were detected by immunohistochemistry on D3 and D3′, respectively. The association between manual grading and INGA outcome was analysis. Results Independent validation results showed the NGAHIC achieved an AUC of 0.76 for recurrence prediction. NGAHIC-positive patients had poorer overall survival (P = 0.017) by univariate survival analysis. Multivariate survival analysis, controlling for T-stage, histology stage, invasion depth, demonstrated NGAHIC-positive was a reproducible prognostic factor for poorer disease-specific survival (HR = 17.24, 95% CI 3.93–75.60, P < 0.001). In contrast, human grading was only prognostic for one reader on D2. Moreover, significant correlations were observed between NGAHIC-positive patients and positivity of HER2 and Ki67 labeling index. Conclusions The NGAHIC could provide precision oncology, personalized cancer management.http://link.springer.com/article/10.1186/s12967-019-1839-xDigital H&E imagesPredicationNegative-node gastric adenocarcinomaQuantitative histomorphometric |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Meng-Yao Ji Lei Yuan Xiao-Da Jiang Zhi Zeng Na Zhan Ping-Xiao Huang Cheng Lu Wei-Guo Dong |
spellingShingle |
Meng-Yao Ji Lei Yuan Xiao-Da Jiang Zhi Zeng Na Zhan Ping-Xiao Huang Cheng Lu Wei-Guo Dong Nuclear shape, architecture and orientation features from H&E images are able to predict recurrence in node-negative gastric adenocarcinoma Journal of Translational Medicine Digital H&E images Predication Negative-node gastric adenocarcinoma Quantitative histomorphometric |
author_facet |
Meng-Yao Ji Lei Yuan Xiao-Da Jiang Zhi Zeng Na Zhan Ping-Xiao Huang Cheng Lu Wei-Guo Dong |
author_sort |
Meng-Yao Ji |
title |
Nuclear shape, architecture and orientation features from H&E images are able to predict recurrence in node-negative gastric adenocarcinoma |
title_short |
Nuclear shape, architecture and orientation features from H&E images are able to predict recurrence in node-negative gastric adenocarcinoma |
title_full |
Nuclear shape, architecture and orientation features from H&E images are able to predict recurrence in node-negative gastric adenocarcinoma |
title_fullStr |
Nuclear shape, architecture and orientation features from H&E images are able to predict recurrence in node-negative gastric adenocarcinoma |
title_full_unstemmed |
Nuclear shape, architecture and orientation features from H&E images are able to predict recurrence in node-negative gastric adenocarcinoma |
title_sort |
nuclear shape, architecture and orientation features from h&e images are able to predict recurrence in node-negative gastric adenocarcinoma |
publisher |
BMC |
series |
Journal of Translational Medicine |
issn |
1479-5876 |
publishDate |
2019-03-01 |
description |
Abstract Background Identifying intestinal node-negative gastric adenocarcinoma (INGA) patients with high risk of recurrence could help perceive benefit of adjuvant therapy for INGA patients following surgical resection. This study evaluated whether the computer-extracted image features of nuclear shapes, texture, orientation, and tumor architecture on digital images of hematoxylin and eosin stained tissue, could help to predict recurrence in INGA patients. Methods A tissue microarrays cohort of 160 retrospectively INGA cases were digitally scanned, and randomly selected as training cohort (D1 = 60), validation cohort (D2 = 100 and D3 = 100, D2 and D3 are different tumor TMA spots from the same patient), accompanied with immunohistochemistry data cohort (D3′ = 100, a duplicate cohort of D3) and negative controls data cohort (D5 = 100, normal adjacent tissues). After nuclear segmentation by watershed-based method, 189 local nuclear features were captured on each TMA core and the top 5 features were selected by Wilcoxon rank sum test within D1. A morphometric-based image classifier (NGAHIC) was composed across the discriminative features and predicted the recurrence in INGA on D2. The intra-tumor heterogeneity was assessed on D3. Manual nuclear atypia grading was conducted on D1 and D2 by two pathologists. The expression of HER2 and Ki67 were detected by immunohistochemistry on D3 and D3′, respectively. The association between manual grading and INGA outcome was analysis. Results Independent validation results showed the NGAHIC achieved an AUC of 0.76 for recurrence prediction. NGAHIC-positive patients had poorer overall survival (P = 0.017) by univariate survival analysis. Multivariate survival analysis, controlling for T-stage, histology stage, invasion depth, demonstrated NGAHIC-positive was a reproducible prognostic factor for poorer disease-specific survival (HR = 17.24, 95% CI 3.93–75.60, P < 0.001). In contrast, human grading was only prognostic for one reader on D2. Moreover, significant correlations were observed between NGAHIC-positive patients and positivity of HER2 and Ki67 labeling index. Conclusions The NGAHIC could provide precision oncology, personalized cancer management. |
topic |
Digital H&E images Predication Negative-node gastric adenocarcinoma Quantitative histomorphometric |
url |
http://link.springer.com/article/10.1186/s12967-019-1839-x |
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