ArticleDiagnostics (Basel, Switzerland)2022
Deep Learning-Based Classification of Uterine Cervical and Endometrial Cancer Subtypes from Whole-Slide Histopathology Images.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic performance of artificial intelligence in detecting oral potentially malignant disorders and oral cancer using medical diagnostic imaging: a systematic review and meta-analysis.Frontiers in oral health · 2024Pooled it
- Integrating artificial intelligence and multi-omics data for precision oncology in endometrial cancer: a narrative review.Functional & integrative genomics · 2026Review
- AI for cervical cancer screening on whole slide images: opportunities with open-source simple tools.Pathologica · 2026Article
- Artificial intelligence in endometrial cancer: multimodal deep learning and future perspectives in precision gynecologic oncology - a structured narrative review.Frontiers in oncology · 2026Review
- Artificial Intelligence Tools for Supporting Histopathologic and Molecular Characterization of Gynecological Cancers: A Review.Journal of clinical medicine · 2025Review
- Auto Machine Learning and Convolutional Neural Network in Diabetes Mellitus Research-The Role of Histopathological Images in Designing and Exploring Experimental Models.Biomedicines · 2025Article
- Deep learning-based automated detection of endometrioid endometrial carcinoma in histopathology.Frontiers in oncology · 2025Article
- Prospects and challenges of deep learning in gynecologic malignancies.Frontiers in oncology · 2025Review
- Deep learning models for cervical cancer subtyping using whole slide images.Frontiers in oncology · 2025Article
- Role of artificial intelligence in digital pathology for gynecological cancers.Computational and structural biotechnology journal · 2024Review
- Deep Learning for Grading Endometrial Cancer.The American journal of pathology · 2024Article
- Impact of artificial intelligence on the diagnosis, treatment and prognosis of endometrial cancer.Annals of medicine and surgery (2012) · 2024Review
- Deep Learning for the Pathologic Diagnosis of Hepatocellular Carcinoma, Cholangiocarcinoma, and Metastatic Colorectal Cancer.Cancers · 2023Article
- Investigating novel biomarkers in uterine corpus endometrial carcinoma: in silico analysis and clinical specimens validation via RT-qPCR and immunohistochemistry.American journal of cancer research · 2023Article
- Detection of malignancy in whole slide images of endometrial cancer biopsies using artificial intelligence.PloS one · 2023Article
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4 authors.
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Abstract
Uterine cervical and endometrial cancers have different subtypes with different clinical outcomes. Therefore, cancer subtyping is essential for proper treatment decisions. Furthermore, an endometrial and endocervical origin for an adenocarcinoma should also be distinguished. Although the discrimination can be helped with various immunohistochemical markers, there is no definitive marker. Therefore, we tested the feasibility of deep learning (DL)-based classification for the subtypes of cervical and endometrial cancers and the site of origin of adenocarcinomas from whole slide images (WSIs) of tissue slides. WSIs were split into 360 × 360-pixel image patches at 20× magnification for classification. Then, the average of patch classification results was used for the final classification. The area under the receiver operating characteristic curves (AUROCs) for the cervical and endometrial cancer classifiers were 0.977 and 0.944, respectively. The classifier for the origin of an adenocarcinoma yielded an AUROC of 0.939. These results clearly demonstrated the feasibility of DL-based classifiers for the discrimination of cancers from the cervix and uterus. We expect that the performance of the classifiers will be much enhanced with an accumulation of WSI data. Then, the information from the classifiers can be integrated with other data for more precise discrimination of cervical and endometrial cancers.
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