George L Mutter

Computational augmentation of neoplastic endometrial glands in digital pathology displays

AbstractThe 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.

Hypertension and Risk of Endometrial Cancer: A Pooled Analysis in the Epidemiology of Endometrial Cancer Consortium (E2C2)

Abstract Background: The incidence rates of endometrial cancer are increasing, which may partly be explained by the rising prevalence of obesity, an established risk factor for endometrial cancer. Hypertension, another component of metabolic syndrome, is also increasing in prevalence, and emerging evidence suggests that it may be associated with the development of certain cancers. The role of hypertension independent of other components of metabolic syndrome in the etiology of endometrial cancer remains unclear. In this study, we evaluated hypertension as an independent risk factor for endometrial cancer and whether this association is modified by other established risk factors. Methods: We included 15,631 endometrial cancer cases and 42,239 controls matched on age, race, and study-specific factors from 29 studies in the Epidemiology of Endometrial Cancer Consortium. We used multivariable unconditional logistic regression models to estimate ORs and 95% confidence intervals (CI) to evaluate the association between hypertension and endometrial cancer and whether this association differed by study design, race/ethnicity, body mass index, diabetes status, smoking status, or reproductive factors. Results: Hypertension was associated with an increased risk of endometrial cancer (OR, 1.14; 95% CI, 1.09–1.19). There was significant heterogeneity by study design (Phet < 0.01), with a stronger magnitude of association observed among case–control versus cohort studies. Stronger associations were also noted for pre-/perimenopausal women and never users of postmenopausal hormone therapy. Conclusions: Hypertension is associated with endometrial cancer risk independently from known risk factors. Future research should focus on biologic mechanisms underlying this association. Impact: This study provides evidence that hypertension may be an independent risk factor for endometrial cancer.