ArticleModern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc2020
A machine learning algorithm for simulating immunohistochemistry: development of SOX10 virtual IHC and evaluation on primarily melanocytic neoplasms.
Article in Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Virtual immunohistochemistry by conditional generative adversarial networks.Scientific reports · 2026Article
- Artificial Intelligence for Lentigo Maligna: Automated Margin Assessment via Sox-10-Based Melanocyte Density Mapping.Dermatopathology (Basel, Switzerland) · 2025Article
- Deep learning finds convergent melanocytic morphology despite noisy archival slides.Cell reports methods · 2025Article
- An initial game-theoretic assessment of enhanced tissue preparation and imaging protocols for improved deep learning inference of spatial transcriptomics from tissue morphology.Briefings in bioinformatics · 2024Article
- Utilizing deep learning model for assessing melanocytic density in resection margins of lentigo maligna.Diagnostic pathology · 2024Article
- Intraoperative margin assessment for basal cell carcinoma with deep learning and histologic tumor mapping to surgical site.NPJ precision oncology · 2024Article
- Training immunophenotyping deep learning models with the same-section ground truth cell label derivation method improves virtual staining accuracy.Frontiers in immunology · 2024Article
- Deep learning model shows pathologist-level detection of sentinel node metastasis of melanoma and intra-nodal nevi on whole slide images.Frontiers in medicine · 2024Article
- Digital pathology systems enabling quality patient care.Genes, chromosomes & cancer · 2023Review
- A snapshot of artificial intelligence research 2019-2021: is it replacing or assisting physicians?Journal of the American Medical Informatics Association : JAMIA · 2023Review
- Recent Advances in Melanoma Diagnosis and Prognosis Using Machine Learning Methods.Current oncology reports · 2023Review
- Restaining-based annotation for cancer histology segmentation to overcome annotation-related limitations among pathologists.Patterns (New York, N.Y.) · 2023Article
- Computer-Assisted Annotation of Digital H&E/SOX10 Dual Stains Generates High-Performing Convolutional Neural Network for Calculating Tumor Burden in H&E-Stained Cutaneous Melanoma.International journal of environmental research and public health · 2022Article
- Global Research Trends of Artificial Intelligence on Histopathological Images: A 20-Year Bibliometric Analysis.International journal of environmental research and public health · 2022Article
- HEROHE Challenge: Predicting HER2 Status in Breast Cancer from Hematoxylin-Eosin Whole-Slide Imaging.Journal of imaging · 2022Article
- From Immunohistochemistry to New Digital Ecosystems: A State-of-the-Art Biomarker Review for Precision Breast Cancer Medicine.Cancers · 2022Review
- Deep learning features encode interpretable morphologies within histological images.Scientific reports · 2022Article
- AI-enabledCell reports methods · 2022Article
- A deep learning model for molecular label transfer that enables cancer cell identification from histopathology images.NPJ precision oncology · 2022Article
- How the variability between computer-assisted analysis procedures evaluating immune markers can influence patients' outcome prediction.Histochemistry and cell biology · 2021Article
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3 authors.
Funding
Abstract
Immunohistochemistry (IHC) is a diagnostic technique used throughout pathology. A machine learning algorithm that could predict individual cell immunophenotype based on hematoxylin and eosin (H&E) staining would save money, time, and reduce tissue consumed. Prior approaches have lacked the spatial accuracy needed for cell-specific analytical tasks. Here IHC performed on destained H&E slides is used to create a neural network that is potentially capable of predicting individual cell immunophenotype. Twelve slides were stained with H&E and scanned to create digital whole slide images. The H&E slides were then destained, and stained with SOX10 IHC. The SOX10 IHC slides were scanned, and corresponding H&E and IHC digital images were registered. Color-thresholding and machine learning techniques were applied to the registered H&E and IHC images to segment 3,396,668 SOX10-negative cells and 306,166 SOX10-positive cells. The resulting segmentation was used to annotate the original H&E images, and a convolutional neural network was trained to predict SOX10 nuclear staining. Sixteen thousand three hundred and nine image patches were used to train the virtual IHC (vIHC) neural network, and 1,813 image patches were used to quantitatively evaluate it. The resulting vIHC neural network achieved an area under the curve of 0.9422 in a receiver operator characteristics analysis when sorting individual nuclei. The vIHC network was applied to additional images from clinical practice, and was evaluated qualitatively by a board-certified dermatopathologist. Further work is needed to make the process more efficient and accurate for clinical use. This proof-of-concept demonstrates the feasibility of creating neural network-driven vIHC assays.
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