To determine sensitivity and specificity of the T2 dark spot sign in helping to distinguish endometriomas from other hemorrhagic adnexal lesions. This HIPAA-compliant, institutional review board-approved retrospective study, with informed consent waived, included 56 women (mean age, 38.8 years; range, 18-66 years). With a radiology database search of pelvic magnetic resonance images from December 16, 2002, to July 24, 2012, 74 cystic hemorrhagic adnexal lesions with hyperintense signal on T1-weighted images were identified. Lesions were excluded if they had solid enhancing components. Final diagnosis was established with pathologic analysis for all endometriomas and neoplasms. Hemorrhagic cysts were diagnosed with pathologic analysis (n = 7), follow-up imaging (n = 13), or prior ultrasonography (n = 5). Two radiologists independently reviewed cases and recorded the presence or absence of T2 shading and T2 dark spots. T2 dark spots were defined as discrete, well-defined markedly hypointense foci within the adnexal lesion on T2-weighted images. Sensitivity, specificity, and positive and negative predictive values of the T2 dark spot sign in distinguishing endometriomas from nonendometrioma hemorrhagic lesions were calculated. Sixteen of 45 endometriomas (36%), zero of 25 hemorrhagic cysts, and two of four neoplasms (50%) (all serous cystadenomas) demonstrated T2 dark spots. Forty-two of 45 endometriomas (93%), 12 of 25 hemorrhagic cysts (48%), and four of four neoplasms (100%) demonstrated T2 shading. Sensitivity, specificity, positive predictive value, and negative predictive value of T2 dark spots for differentiating endometriomas from other hemorrhagic cystic ovarian masses were 36% (95% confidence interval [CI]: 19.8, 51.3), 93% (95% CI: 83.9, 100), 89% (95% CI: 63.9, 98.1), and 48% (95% CI: 34.8, 61.8), respectively, and for T2 shading, they were 93% (95% CI: 84.0, 100), 45% (95% CI: 27.8, 61.9), 72% (95% CI: 58.9, 83.0), and 81% (95% CI: 53.7, 95.0), respectively. The T2 dark spot sign has high specificity for chronic hemorrhage and is useful to differentiate endometriomas from hemorrhagic cysts. The T2 shading sign is sensitive but not specific for endometriomas. Online supplemental material is available for this article.

Robust technology infrastructure is needed to enable learning health care systems to improve quality, access, and cost. Such infrastructure relies on the trust and confidence of individuals to share their health data for healthcare and research. Few studies have addressed consumers’ views on electronic data sharing and fewer still have explored the dual purposes of healthcare and research together. The objective of the study is to explore factors that affect consumers’ willingness to share electronic health information for healthcare and research. This study involved a random-digit dial telephone survey of 800 adult Californians conducted in English and Spanish. Logistic regression was performed using backward selection to test for significant (p-value ≤ 0.05) associations of each explanatory variable with the outcome variable. The odds of consent for electronic data sharing for healthcare decreased as Likert scale ratings for EHR impact on privacy worsened, odds ratio (OR) = 0.74, 95% CI [0.60, 0.90]; security, OR = 0.80, 95% CI [0.66, 0.98]; and quality, OR = 0.59, 95% CI [0.46–0.75]. The odds of consent for sharing for research was greater for those who think EHR will improve research quality, OR = 11.26, 95% CI [4.13, 30.73]; those who value research benefit over privacy OR = 2.72, 95% CI [1.55, 4.78]; and those who value control over research benefit OR = 0.49, 95% CI [0.26, 0.94]. Consumers’ choices about electronically sharing health information are affected by their attitudes toward EHRs as well as beliefs about research benefit and individual control. Design of person-centered interventions utilizing electronically collected health information, and policies regarding data sharing should address these values of importance to people. Understanding of these perspectives is critical for leveraging health data to support learning health care systems.