ArticleLaboratory investigation; a journal of technical methods and pathology2022
Artificial intelligence modelling in differentiating core biopsies of fibroadenoma from phyllodes tumor.
Article in Laboratory investigation; a journal of technical methods and pathology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06286267 (Development of an Artificial Intelligence-Based System for Precise Diagnosis and Prognosis of Breast Phyllodes Tumors), which is not on this map. Cited by 4 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Development of an Artificial Intelligence-Based System for Precise Diagnosis and Prognosis of Breast Phyllodes Tumors
Who cites it
4 citing papers in PubMed.
- AI-FLEET: Phase I-Multimodal Deep Learning Model for Phyllodes Tumor Classification.Annals of surgical oncology · 2026Article
- Salivary Metabolites in Breast Cancer and Fibroadenomas: Focus on Menopausal Status and BMI.Metabolites · 2024Article
- Use of artificial intelligence in breast surgery: a narrative review.Gland surgery · 2024Review
- A Preliminary Investigation into Search and Matching for Tumor Discrimination in World Health Organization Breast Taxonomy Using Deep Networks.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2024Article
Corrections and comments
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Authors and funding
18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast fibroepithelial lesions (FEL) are biphasic tumors which consist of benign fibroadenomas (FAs) and the rarer phyllodes tumors (PTs). FAs and PTs have overlapping features, but have different clinical management, which makes correct core biopsy diagnosis important. This study used whole-slide images (WSIs) of 187 FA and 100 PT core biopsies, to investigate the potential role of artificial intelligence (AI) in FEL diagnosis. A total of 9228 FA patches and 6443 PT patches was generated from WSIs of the training subset, with each patch being 224 × 224 pixel in size. Our model employed a two-stage architecture comprising a convolutional neural network (CNN) component for feature extraction from the patches, and a recurrent neural network (RNN) component for whole-slide classification using activation values from the global average pooling layer in the CNN model. It achieved an overall slide-level accuracy of 87.5%, with accuracies of 80% and 95% for FA and PT slides respectively. This affirms the potential role of AI in diagnostic discrimination between FA and PT on core biopsies which may be further refined for use in routine practice.
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Registered trials
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