ArticleSensors (Basel, Switzerland)2022
Characterization of Nuclear Pleomorphism and Tubules in Histopathological Images of Breast Cancer.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Biological feature-based machine learning in histopathological images: a systematic review.Journal of pathology informatics · 2026Review
- HiGATE: hierarchical graph attention for multi-scale tissue encoder in computational pathology.Frontiers in oncology · 2026Article
- Embedding-driven dual-branch approach for accurate breast tumor cellularity classification.Scientific reports · 2025Article
- A Short Breast Imaging Reporting and Data System-Based Description for Classification of Breast Mass Grade.Life (Basel, Switzerland) · 2024Article
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4 authors.
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Abstract
Breast cancer (BC) diagnosis is made by a pathologist who analyzes a portion of the breast tissue under the microscope and performs a histological evaluation. This evaluation aims to determine the grade of cellular differentiation and the aggressiveness of the tumor by the Nottingham Grade Classification System (NGS). Nowadays, digital pathology is an innovative tool for pathologists in diagnosis and acquiring new learning. However, a recurring problem in health services is the excessive workload in all medical services. For this reason, it is required to develop computational tools that assist histological evaluation. This work proposes a methodology for the quantitative analysis of BC tissue that follows NGS. The proposed methodology is based on digital image processing techniques through which the BC tissue can be characterized automatically. Moreover, the proposed nuclei characterization was helpful for grade differentiation in carcinoma images of the BC tissue reaching an 0.84 accuracy. In addition, a metric was proposed to assess the likelihood of a structure in the tissue corresponding to a tubule by considering spatial and geometrical characteristics between lumina and its surrounding nuclei, reaching an accuracy of 0.83. Tests were performed from different databases and under various magnification and staining contrast conditions, showing that the methodology is reliable for histological breast tissue analysis.
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