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Abstract The prevalence of genital human papillomavirus (HPV) in women with endometriosis has never been reported in a national representative survey. We aimed to investigate the association of endometriosis with the prevalence of HPV. We analyzed the data on 1768 women (representing 43,824,157 women) in the United States aged 20–54 years from the National Health and Nutrition Examination Survey in the prevaccination era (2003–2006). The diagnosis of endometriosis was based on a self-report. The prevalence of any HPV in women with endometriosis did not differ from that in women without endometriosis after controlling for potential confounders such as age, ethnicity, family income, marital status, and the number of deliveries (adjusted prevalence ratio (aPR) 0.84, 95% confidence interval (CI) 0.61–1.15). No significant association was found between the prevalence of high-risk HPV and the diagnosis of endometriosis (aPR 0.71, 95% CI 0.44–1.14). If the participants were not covered by health insurance, the prevalence of any HPV infection in women with endometriosis was higher than in those without endometriosis (aPR 1.44, 95% CI 0.94–2.20). In contrast, in a subgroup who had health insurance, a lower prevalence of any HPV infection was observed in women with endometriosis (aPR 0.71, 95% CI 0.50–1.03), and P for interaction was statistically significant (P = 0.01). There was no association between endometriosis and HPV infection in this study of HPV vaccine-naïve women of reproductive age. The association was not different by the type of HPV. However, access to healthcare may modify the association between endometriosis and HPV infection.

Abstract The detection of Epstein–Barr virus (EBV) in gastric cancer patients is crucial for clinical decision making, as it is related with specific treatment responses and prognoses. Despite its importance, the limited medical resources preclude universal EBV testing. Herein, we propose a deep learning-based EBV prediction method from H&E-stained whole-slide images (WSI). Our model was developed using 319 H&E stained WSI (26 EBV positive; TCGA dataset) from the Cancer Genome Atlas, and 108 WSI (8 EBV positive; ISH dataset) from an independent institution. Our deep learning model, EBVNet consists of two sequential components: a tumor classifier and an EBV classifier. We visualized the learned representation by the classifiers using UMAP. We externally validated the model using 60 additional WSI (7 being EBV positive; HGH dataset). We compared the model’s performance with those of four pathologists. EBVNet achieved an AUPRC of 0.65, whereas the four pathologists yielded a mean AUPRC of 0.41. Moreover, EBVNet achieved an negative predictive value, sensitivity, specificity, precision, and F1-score of 0.98, 0.86, 0.92, 0.60, and 0.71, respectively. Our proposed model is expected to contribute to prescreen patients for confirmatory testing, potentially to save test-related cost and labor.

Abstract We conducted a retrospective cohort study to evaluate the temporal pattern of incidence of chronic conditions after developing breast cancer using a population-based national registry. We selected 84,969 women with newly diagnosed breast cancer between 2002 and 2016 and a 1:10 sample of age-matched non-breast cancer controls (N = 1,057,674). The main study exposure was incident breast cancer, considered as a time-varying exposure. The outcomes were incident cases of leukemia, endometrial cancer, myeloma, cardiomyopathy, osteoporosis, end stage renal disease (ESRD), pulmonary fibrosis, hypothyroidism, type 2 diabetes, hypertension and hyperlipidemia. The development of breast cancer was associated with a significantly increased risk of all outcomes analyzed except for ESRD and hypertension. The fully-adjusted risks of leukemia (HR 3.09; 95% CI 2.11–4.51), cardiomyopathy (HR 2.65; 95% CI 1.90–3.68), endometrial cancer (HR 3.53; 95% CI 2.76–4.53), hypothyroidism (HR 1.29; 95% CI 1.19–1.40), pulmonary fibrosis (HR 1.84; 95% CI 1.12–3.02), and hyperlipidemia (HR 1.24; 95% CI 1.20–1.28) remained significantly elevated after more than 5 years since diagnosis. Optimal care for breast cancer survivors requires close collaboration between oncologists and allied health care professionals to identify and manage the long-term morbidity and mortality associated with these chronic conditions.