Computational augmentation of neoplastic endometrial glands in digital pathology displays
| Autor: | Michael J. Downing, David J. Papke, George L. Mutter, Peter Hufnagl, Sebastian Lohmann |
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| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Biology Endometrium Pathology and Forensic Medicine 03 medical and health sciences 0302 clinical medicine Predictive Value of Tests medicine Humans Process (anatomy) Endometrial intraepithelial neoplasia business.industry Digital pathology Pattern recognition Graph theory medicine.disease Immunohistochemistry Random forest Endometrial Neoplasms 030104 developmental biology medicine.anatomical_structure 030220 oncology & carcinogenesis Endometrial Hyperplasia Graph (abstract data type) Biomarker (medicine) Female Artificial intelligence business Algorithms Carcinoma in Situ |
| Zdroj: | The Journal of pathologyReferences. 253(3) |
| ISSN: | 1096-9896 |
| Popis: | The pathologic diagnosis of neoplasia requires localization and classification of lesional tissue, a process that depends on the recognition of an abnormal spatial distribution of neoplastic elements relative to admixed normal background tissue. In endometrial intraepithelial neoplasia (EIN), a pre-cancer usually managed by hysterectomy, a clonally mutated proliferation of cytologically altered glands ('neoplastic-EIN') aggregates in clusters that also contain background non-neoplastic glands ('background-NL'). Here, we used image analysis to classify individual glands within endometrial tissue fragments as neoplastic-EIN or background-NL, and we used the distribution of predictions to localize foci diagnostic of EIN. Nuclear coordinates were automatically assigned and were used as vertices to generate Delaunay triangulations for each gland. Graph statistical variables were used to develop random forest algorithms to classify glands as neoplastic-EIN or background-NL. Individual glands in an independent validation set were scored by a 'ground truth' biomarker (PAX2 immunohistochemistry). We found that exclusion of small glands led to improvement in classification accuracy. Using an inclusion threshold of 200 nuclei per gland, our final model classification accuracy was 77.5% in the validation set, with a positive predictive value of 0.81. We leveraged this high positive predictive value in a point cloud overlay display to assist end-user identification of EIN foci. This study demonstrates that graph theory approaches applied to small-scale anatomic elements in the endometrium allow biologic classification by machine learning, and that spatial superimposition over large-scale tissue expanses can have practical diagnostic utility. We expect this augmented diagnostic approach to be generalizable to commonly encountered problems in other organ systems. © 2020 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd. |
| Databáze: | OpenAIRE |
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