ArticleJournal of translational medicine2019
Nuclear shape, architecture and orientation features from H&E images are able to predict recurrence in node-negative gastric adenocarcinoma.
Article in Journal of translational medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed, 41 citations in OpenAlex.
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- Biological feature-based machine learning in histopathological images: a systematic review.Journal of pathology informatics · 2026Review
- Prognostic value of nuclear features based on tumor-associated collagen signatures in breast cancer.NPJ breast cancer · 2025Article
- Pathological omics prediction of early and advanced colon cancer based on artificial intelligence model.Discover oncology · 2025Article
- Quantifying and interpreting biologically meaningful spatial signatures within tumor microenvironments.NPJ precision oncology · 2025Review
- Predicting the risk of relapsed or refractory in patients with diffuse large B-cell lymphoma via deep learning.Frontiers in oncology · 2025Article
- Extreme wrinkling of the nuclear lamina is a morphological marker of cancer.NPJ precision oncology · 2024Article
- Identification of histopathological classification and establishment of prognostic indicators of gastric adenocarcinoma based on deep learning algorithm.Medical molecular morphology · 2024Article
- Clinical values of nuclear morphometric analysis in fibroepithelial lesions.Breast cancer research : BCR · 2024Article
- Predicting Breast Cancer Relapse from Histopathological Images with Ensemble Machine Learning Models.Current oncology (Toronto, Ont.) · 2024Article
- A glimpse into the future: Integrating artificial intelligence for precision HER2-positive breast cancer management.iMetaOmics · 2024Review
- AI powered quantification of nuclear morphology in cancers enables prediction of genome instability and prognosis.NPJ precision oncology · 2024Article
- Nondestructive 3D pathology with analysis of nuclear features for prostate cancer risk assessment.The Journal of pathology · 2023Article
- Deep learning for prediction of hepatocellular carcinoma recurrence after resection or liver transplantation: a discovery and validation study.Hepatology international · 2022Article
- Computational pathology for musculoskeletal conditions using machine learning: advances, trends, and challenges.Arthritis research & therapy · 2022Review
- Prediction of HER2-positive breast cancer recurrence and metastasis risk from histopathological images and clinical information via multimodal deep learning.Computational and structural biotechnology journal · 2022Article
- Driverless artificial intelligence framework for the identification of malignant pleural effusion.Translational oncology · 2021Article
- Glandular orientation and shape determined by computational pathology could identify aggressive tumor for early colon carcinoma: a triple-center study.Journal of translational medicine · 2020Article
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Authors and funding
8 authors at 4 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundIdentifying 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.
methodsA 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.
resultsIndependent 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.
conclusionsThe NGAHIC could provide precision oncology, personalized cancer management.
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