ArticleModern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc2022
Deep learning-based histotype diagnosis of ovarian carcinoma whole-slide pathology images.
Article in Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic accuracy of ovarian cancer using convolutional neural network: a systematic review and meta-analysis.BMC medical informatics and decision making · 2026Pooled it
- A new era in computational pathology: A survey on foundation and vision-language models.Journal of pathology informatics · 2026Review
- From unlabeled to labeled: self-supervised deep learning in computational pathology.BioData mining · 2026Article
- A novel model for assessing lymph node metastasis and the immune microenvironment in breast cancer by integrating digital pathology images and transcriptomics.Discover oncology · 2026Article
- Boosting pathology foundation models via few-shot prompt-tuning for rare cancer subtyping.Nature communications · 2026Article
- A generalizable pathology foundation model using a unified knowledge distillation pretraining framework.Nature biomedical engineering · 2026Article
- Distinction between primary and metastatic mucinous ovarian carcinoma from histopathology images using deep learning.NPJ digital medicine · 2026Article
- Improving Ovarian Cancer Subtyping with Computer Vision Models on Tiled Histopathological Images.Journal of imaging informatics in medicine · 2026Article
- Advancements in artificial intelligence for cancer diagnosis and prognosis prediction: current applications and emerging opportunities.Frontiers in cell and developmental biology · 2026Review
- Histopathology images-based deep learning prediction of prognosis in primary mucinous ovarian carcinoma.Frontiers in oncology · 2026Article
- A multimodal knowledge-enhanced whole-slide pathology foundation model.Nature communications · 2025Article
- SMMILe enables accurate spatial quantification in digital pathology using multiple-instance learning.Nature cancer · 2025Article
- Artificial Intelligence Tools for Supporting Histopathologic and Molecular Characterization of Gynecological Cancers: A Review.Journal of clinical medicine · 2025Review
- A Deep Learning Framework for Classification of Neuroendocrine Neoplasm Whole Slide Images.Cancers · 2025Article
- Article
- Quantitative and qualitative metrics of tumor stroma in predicting ovarian cancer outcomes and expansion of its study with AI-based tools.Molecular therapy. Oncology · 2025Review
- Review
- Ultrasound-based radiomics for predicting the five major histological subtypes of epithelial ovarian cancer.BMC medical imaging · 2025Article
- OVision A raspberry Pi powered portable low cost medical device framework for cancer diagnosis.Scientific reports · 2025Article
- Evaluation of the prognostic potential of histopathological subtyping in high-grade serous ovarian carcinoma.Virchows Archiv : an international journal of pathology · 2025Article
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12 authors.
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Abstract
Ovarian carcinoma has the highest mortality of all female reproductive cancers and current treatment has become histotype-specific. Pathologists diagnose five common histotypes by microscopic examination, however, histotype determination is not straightforward, with only moderate interobserver agreement between general pathologists (Cohen's kappa 0.54-0.67). We hypothesized that machine learning (ML)-based image classification models may be able to recognize ovarian carcinoma histotype sufficiently well that they could aid pathologists in diagnosis. We trained four different artificial intelligence (AI) algorithms based on deep convolutional neural networks to automatically classify hematoxylin and eosin-stained whole slide images. Performance was assessed through cross-validation on the training set (948 slides corresponding to 485 patients), and on an independent test set of 60 patients from another institution. The best-performing model achieved a diagnostic concordance of 81.38% (Cohen's kappa of 0.7378) in our training set, and 80.97% concordance (Cohen's kappa 0.7547) on the external dataset. Eight cases misclassified by ML in the external set were reviewed by two subspecialty pathologists blinded to the diagnoses, molecular and immunophenotype data, and ML-based predictions. Interestingly, in 4 of 8 cases from the external dataset, the expert review pathologists rendered diagnoses, based on blind review of the whole section slides classified by AI, that were in agreement with AI rather than the integrated reference diagnosis. The performance characteristics of our classifiers indicate potential for improved diagnostic performance if used as an adjunct to conventional histopathology.
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