ArticleCancers2023
AI-Powered Segmentation of Invasive Carcinoma Regions in Breast Cancer Immunohistochemical Whole-Slide Images.
Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 13 citations in OpenAlex.
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- The ATG14: multi-layer autophagy control and an emerging therapeutic target in cancer.Apoptosis : an international journal on programmed cell death · 2026Review
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- Functional Roles and Mechanistic Insights of YEATS Domain Proteins in Digestive System Tumors.Digestive diseases and sciences · 2025Review
- The role of HOXA1 in cancer and targeted therapy.Medical oncology (Northwood, London, England) · 2025Review
- Carvedilol sensitizes paclitaxel-resistant gastric cancer AGS cells to paclitaxel: influences on apoptotic regulators, Notch, PI3K/AKT, ERK1/2 signaling pathways, and miR-34a expression.Medical oncology (Northwood, London, England) · 2025Article
- Influence of tumour location on the survival outcomes of upper tract urothelial carcinoma treated with radical nephroureterectomy.World journal of urology · 2025Observational
- Extracellular vesicles derived from bone marrow mesenchymal stem cells ameliorate chronic liver damage via microRNA-136-5p.Molecular and cellular biochemistry · 2025Article
- Automated interpretation of PD-L1 CPS based on multi-AI models integration strategy in gastric cancer.Frontiers in immunology · 2025Article
- Review
- The InterVision Framework: An Enhanced Fine-Tuning Deep Learning Strategy for Auto-Segmentation in Head and Neck.Journal of personalized medicine · 2024Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
aimsThe automation of quantitative evaluation for breast immunohistochemistry (IHC) plays a crucial role in reducing the workload of pathologists and enhancing the objectivity of diagnoses. However, current methods face challenges in achieving fully automated immunohistochemistry quantification due to the complexity of segmenting the tumor area into distinct ductal carcinoma in situ (DCIS) and invasive carcinoma (IC) regions. Moreover, the quantitative analysis of immunohistochemistry requires a specific focus on invasive carcinoma regions. METHODS AND
resultsIn this study, we propose an innovative approach to automatically identify invasive carcinoma regions in breast cancer immunohistochemistry whole-slide images (WSIs). Our method leverages a neural network that combines multi-scale morphological features with boundary features, enabling precise segmentation of invasive carcinoma regions without the need for additional H&E and P63 staining slides. In addition, we introduced an advanced semi-supervised learning algorithm, allowing efficient training of the model using unlabeled data. To evaluate the effectiveness of our approach, we constructed a dataset consisting of 618 IHC-stained WSIs from 170 cases, including four types of staining (ER, PR, HER2, and Ki-67). Notably, the model demonstrated an impressive intersection over union (IoU) score exceeding 80% on the test set. Furthermore, to ascertain the practical utility of our model in IHC quantitative evaluation, we constructed a fully automated Ki-67 scoring system based on the model's predictions. Comparative experiments convincingly demonstrated that our system exhibited high consistency with the scores given by experienced pathologists.
conclusionsOur developed model excels in accurately distinguishing between DCIS and invasive carcinoma regions in breast cancer immunohistochemistry WSIs. This method paves the way for a clinically available, fully automated immunohistochemistry quantitative scoring system.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.