Cervical neoplasia–related factors and decreased prevalence of uterine fibroids among a cohort of African American women

To investigate whether the previously reported inverse association between cervical neoplasia and uterine fibroids is corroborated

Developing a Machine Learning-Based Clinical Decision Support Tool for Uterine Tumor Imaging

Uterine leiomyosarcoma (LMS) is a rare but aggressive malignancy. On imaging, it is difficult to differentiate LMS from, for example, degenerated leiomyoma (LM), a prevalent but benign condition. We curated a data set of 115 axial T2-weighted MRI images from 110 patients (mean [range] age=45 [17-81] years) with UTs that included five different tumor types. These data were randomly split stratifying on tumor volume into training (n=85) and test sets (n=30). An independent second reader (reader 2) provided manual segmentations for all test set images. To automate segmentation, we applied nnU-Net and explored the effect of training set size on performance by randomly generating subsets with 25, 45, 65 and 85 training set images. We evaluated the ability of radiomic features to distinguish between types of UT individually and when combined through feature selection and machine learning. Using the entire training set the mean [95% CI] fibroid DSC was measured as 0.87 [0.59-1.00] and the agreement between the two readers was 0.89 [0.77-1.0] on the test set. When classifying degenerated LM from LMS we achieve a test set F1-score of 0.80. Classifying UTs based on radiomic features we identify classifiers achieving F1-scores of 0.53 [0.45, 0.61] and 0.80 [0.80, 0.80] on the test set for the benign versus malignant, and degenerated LM versus LMS tasks. We show that it is possible to develop an automated method for 3D segmentation of the uterus and UT that is close to human-level performance with fewer than 150 annotated images. For distinguishing UT types, while we train models that merit further investigation with additional data, reliable automatic differentiation of UTs remains a challenge

Short-term quality of life after myomectomy for uterine fibroids from the compare-uf fibroid registry

Background Uterine fibroids may decrease quality of life in a significant proportion of affected women. Myomectomy offers a uterine-sparing treatment option for patients with uterine fibroids that can be performed abdominally, laparoscopically (with or without robotic assistance), and hysteroscopically. Quality of life information using validated measures for different myomectomy routes, especially hysteroscopic myomectomy, is limited. Objective To compare women’s perception of their short-term health-related quality of life measures and reported time to return to usual activities and return to work for different routes of myomectomy. Materials and Methods Comparing Options for Management: Patient-centered Results for Uterine Fibroids (COMPARE-UF) is a prospective nationwide fibroid registry that enrolled premenopausal women seeking treatment for uterine fibroids at 8 clinical sites. For this analysis, we included women undergoing hysteroscopic, abdominal, or laparoscopic myomectomy who completed the postprocedure questionnaire scheduled between 6 and 12 weeks after surgery. Health-related quality of life outcomes, such as pain, anxiety, and return to usual activitie, were assessed for each route. The hysteroscopic myomectomy group had large differences in demographics, fibroid number, and uterine size compared to the other groups; thus, a direct comparison of quality of life measures was performed only for abdominal and laparoscopic approaches after propensity weighting. Propensity weighting was done using 24 variables that included demographics, quality of life baseline measures, and fibroid and uterine measurements. Results A total of 1206 women from 8 COMPARE-UF sites underwent myomectomy (338 hysteroscopic, 519 laparoscopic, and 349 abdominal). All women had substantial improvement in short-term health-related quality of life and symptom severity scores, which was not different among groups. Average symptom severity scores decreased about 30 points in each group. Return to usual activities averaged 0 days (interquartile range, 0–14 days) for hysteroscopic myomectomy, 21 days (interquartile range, 14–28 days) for laparoscopic myomectomy, and 28 days (interquartile range, 14–35 days) for abdominal myomectomy. After propensity adjustment, quality of life outcomes in the laparoscopic and abdominal myomectomy groups were similar except for more anxiety in the laparoscopic myomectomy group and slightly more pain in the abdominal myomectomy group. After propensity weighting, return to usual activities favored laparoscopic compared to abdominal procedures; median time was the same at 21 days, but the highest quartile of women in the abdominal group needed an additional week of recovery (interquartile range,14.0–28.0 for laparoscopic versus 14.0–35.0 for abdominal, P < .01). Time to return to work was also longer in the abdominal arm (median, 22 days; interquartile range, 14–40 days, versus median, 42; interquartile range, 27–56). Conclusion Women who underwent myomectomy had substantial improvement in health-related quality of life, regardless of route of myomectomy. After propensity weighting, abdominal myomectomy was associated with a nearly 2-week longer time to return to work than laparoscopic myomectomy

Black women are more likely than white women to schedule a uterine-sparing treatment for leiomyomas

Background: To evaluate differences in the proportion of uterine fibroid (UF) treatments that are uterine-sparing between Black women and White women and identify factors that could explain disparities. Methods: Women at age 18-54 years who were enrolled from 10 clinical sites in the United States into the Comparing Options for Management: Patient-Centered Results for UFs (COMPARE-UF) treatment registry completed questionnaires before their UF procedure. UF symptoms and quality of life were assessed by questionnaires. Details on UF imaging and treatment (hysterectomy, myomectomy, or uterine artery embolization [UAE]) were collected from each patient's medical record. Random-effects logistic regression was used to assess the association between race and the odds of having a uterine-sparing procedure versus hysterectomy. Subgroup analyses compared each uterine-sparing procedure with hysterectomy. Results: In this cohort of 1141 White women and 1196 Black women, Black women tended to be younger (median 41.0 vs. 42.0 years) and report worse symptoms, pain, and function on every scale compared with White women. Black women were more likely to have had a prior UF treatment compared with White women (22.8% vs. 14.6%). White women had more hysterectomies (43.6% vs. 32.2%) and myomectomies (50.9% vs. 50.2%) versus Black women. Black women had more UAEs (15.1% vs. 4.7%) than White women. After adjusting for clinical site and other variables, Black women had greater odds than White women of having a myomectomy (odds ratio [OR] = 2.41, 95% confidence interval [CI] = 1.63-3.56) or a UAE versus hysterectomy (OR = 4.24, 95% CI = 2.41-7.46). Conclusion: In these participants, Black women were more likely to schedule a uterine-sparing UF treatment and a nonsurgical UF treatment than their White counterparts; this may not be true for all women. Longer comparative effectiveness studies are needed to inform women about the durability of UF treatments. Greater understanding of factors influencing treatment selection is needed as are studies that include women without access to tertiary care centers. Clinical Trial Registration: Clinicaltrials.gov, NCT02260752 (enrollment start: November 2015)

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives