ArticleFrontiers in molecular biosciences2020
Predict Ki-67 Positive Cells in H&E-Stained Images Using Deep Learning Independently From IHC-Stained Images.
Article in Frontiers in molecular biosciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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24 citing papers in PubMed, 1 synthesis or guideline pooled it, 54 citations in OpenAlex.
- Ki-67 assessment of pancreatic neuroendocrine neoplasms: Systematic review and meta-analysis of manual vs. digital pathology scoring.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2022Pooled it
- Tumor-Aware Unsupervised Digital HER2 Immunohistochemistry via Dual Contrastive Learning.Biosensors · 2026Article
- Spatial ecology of breast cancer reveals co-evolution of proliferative and dormant niches.Genome medicine · 2026Article
- Mucin phenotype-based deep learning framework for intestinal metaplasia-carcinogenesis progression prediction.NPJ precision oncology · 2025Article
- Cross-Modality Learning for Predicting Immunohistochemistry Biomarkers from Hematoxylin and Eosin-Stained Whole Slide Images.The American journal of pathology · 2025Article
- Optical super-resolution histology of formalin-fixed paraffin-embedded tissue samples: challenges and opportunities.Nature communications · 2025Review
- Color Normalization in Breast Cancer Immunohistochemistry Images Based on Sparse Stain Separation and Self-Sparse Fuzzy Clustering.Diagnostics (Basel, Switzerland) · 2025Article
- Prognostic role of Ki-67 in colorectal carcinoma: Development and evaluation of machine learning prediction models.World journal of clinical oncology · 2025Article
- Comparing non-machine learning vs. machine learning methods for Ki67 scoring in gastrointestinal neuroendocrine tumors.Scientific reports · 2025Article
- Digitally derived Ki-67 proliferation index for GastroEnteroPancreatic neuroendocrine neoplasms.Pathology oncology research : POR · 2025Article
- Deep learning based analysis of G3BP1 protein expression to predict the prognosis of nasopharyngeal carcinoma.PloS one · 2025Article
- Leveraging deep learning for identification and segmentation of "CAF-1/p60-positive" nuclei in oral squamous cell carcinoma tissue samples.Journal of pathology informatics · 2024Article
- Transformation from hematoxylin-and-eosin staining to Ki-67 immunohistochemistry digital staining images using deep learning: experimental validation on the labeling index.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Training immunophenotyping deep learning models with the same-section ground truth cell label derivation method improves virtual staining accuracy.Frontiers in immunology · 2024Article
- Restaining-based annotation for cancer histology segmentation to overcome annotation-related limitations among pathologists.Patterns (New York, N.Y.) · 2023Article
- The promise and challenge of spatial omics in dissecting tumour microenvironment and the role of AI.Frontiers in oncology · 2023Review
- Predicting IHC staining classes of NF1 using features in the hematoxylin channel.Journal of pathology informatics · 2023Article
- Deep Learning-Based Prediction of Molecular Tumor Biomarkers from H&E: A Practical Review.Journal of personalized medicine · 2022Review
- Label-Free Virtual HER2 Immunohistochemical Staining of Breast Tissue using Deep Learning.BME frontiers · 2022Article
- MC-ViT: Multi-path cross-scale vision transformer for thymoma histopathology whole slide image typing.Frontiers in oncology · 2022Article
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Authors and funding
11 authors at 5 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
objectiveTo obtain molecular information in slides directly from H&E staining slides, which apparently display morphological information, to show that some differences in molecular level have already encoded in morphology.
methodsIn this paper, we selected Ki-67-expression as the representative of molecular information. We proposed a method that can predict Ki-67 positive cells directly from H&E stained slides by a deep convolutional network model. To train this model, we constructed a dataset containing Ki-67 negative or positive cell images and background images. These images were all extracted from H&E stained WSIs and the Ki-67 expression was acquired from the corresponding IHC stained WSIs. The trained model was evaluated both on classification performance and the ability to quantify Ki-67 expression in H&E stained images.
resultsThe model achieved an average accuracy of 0.9371 in discrimination of Ki-67 negative cell images, positive cell images and background images. As for evaluation of quantification performance, the correlation coefficient between the quantification results of H&E stained images predicted by our model and that of IHC stained images obtained by color channel filtering is 0.80. CONCLUSION AND SIGNIFICANCE: Our study indicates that the deep learning model has a good performance both on prediction of Ki-67 positive cells and quantification of Ki-67 expression in cancer samples stained by H&E. More generally, this study shows that deep learning is a powerful tool in exploring the relationship between morphological information and molecular information. AVAILABILITY AND IMPLEMENTATION: The main program is available at https://github.com/liuyiqing2018/predict_Ki-67_from_HE.
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