ArticleComputer methods and programs in biomedicine2020
Automated gleason grading on prostate biopsy slides by statistical representations of homology profile.
Article in Computer methods and programs in biomedicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Slide-free FIBI histology for improved quantitative vascular network analysis in breast cancer: Integrating vessel geometry and topology metrics.Journal of pathology informatics · 2026Article
- Computational pathology: A survey review and the way forward.Journal of pathology informatics · 2024Review
- An Artificial Intelligent System for Prostate Cancer Diagnosis in Whole Slide Images.Journal of medical systems · 2024Article
- Advancing Precision Medicine: Algebraic Topology and Differential Geometry in Radiology and Computational Pathology.Laboratory investigation; a journal of technical methods and pathology · 2024Review
- A quantitative evaluation method utilizing the homology concept to assess the state of chromatin within the nucleus of lung cancer.Scientific reports · 2023Article
- Development of "Mathematical Technology for Cytopathology," an Image Analysis Algorithm for Pancreatic Cancer.Diagnostics (Basel, Switzerland) · 2022Article
- Histopathological Tissue Segmentation of Lung Cancer with Bilinear CNN and Soft Attention.BioMed research international · 2022Article
- [Automated grading of glioma based on density and atypia analysis in whole slide images].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2021Article
- GestAltNet: aggregation and attention to improve deep learning of gestational age from placental whole-slide images.Laboratory investigation; a journal of technical methods and pathology · 2021Article
- Homology-Based Image Processing for Automatic Classification of Histopathological Images of Lung Tissue.Cancers · 2021Article
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Abstract
BACKGROUND AND
objectiveGleason grading system is currently the clinical gold standard for determining prostate cancer aggressiveness. Prostate cancer is typically classified into one of 5 different categories with 1 representing the most indolent disease and 5 reflecting the most aggressive disease. Grades 3 and 4 are the most common and difficult patterns to be discriminated in clinical practice. Even though the degree of gland differentiation is the strongest determinant of Gleason grade, manual grading is subjective and is hampered by substantial inter-reader disagreement, especially with regard to intermediate grade groups.
methodsTo capture the topological characteristics and the degree of connectivity between nuclei around the gland, the concept of Homology Profile (HP) for prostate cancer grading is presented in this paper. HP is an algebraic tool, whereby, certain algebraic invariants are computed based on the structure of a topological space. We utilized the Statistical Representation of Homology Profile (SRHP) features to quantify the extent of glandular differentiation. The quantitative characteristics which represent the image patch are fed into a supervised classifier model for discrimination of grade patterns 3 and 4.
resultsOn the basis of the novel homology profile, we evaluated 43 digitized images of prostate biopsy slides annotated for regions corresponding to Grades 3 and 4. The quantitative patch-level evaluation results showed that our approach achieved an Area Under Curve (AUC) of 0.96 and an accuracy of 0.89 in terms of discriminating Grade 3 and 4 patches. Our approach was found to be superior to comparative methods including handcrafted cellular features, Stacked Sparse Autoencoder (SSAE) algorithm and end-to-end supervised learning method (DLGg). Also, slide-level quantitative and qualitative evaluation results reflect the ability of our approach in discriminating Gleason Grade 3 from 4 patterns on H&E tissue images.
conclusionsWe presented a novel Statistical Representation of Homology Profile (SRHP) approach for automated Gleason grading on prostate biopsy slides. The most discriminating topological descriptions of cancerous regions for grade 3 and 4 in prostate cancer were identified. Moreover, these characteristics of homology profile are interpretable, visually meaningful and highly consistent with the rubric employed by pathologists for the task of Gleason grading.
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