Association between Physical Activity and Menstrual Cycle Disorders in Young Athletes.

Our study aims to evaluate clinical predictors of menstrual cycle disorders in female athletes who compete in running disciplines. This is a prospective observational study. Women were recruited between January and May 2022. Fifty-three patients were enrolled and completed a questionnaire about menstrual cycle, physical activity, and food habit characteristics. Of the women in our population, 39.6% had menstrual irregularities and reported a significantly higher number of kilometers run per week (67 vs. 35, p:0.02). The number of kilometers run per week was associated with menstrual irregularities (for 10 km, OR 1.35; IC95% 1.05-1.73; p: 0.02) after adjusting for BMI, age, level of sport and caloric intake. The variable of "km run per week" appeared as a diagnostic indicator of irregular menstrual cycle with statistical significance (AUC ROC curve 0.71, IC95% 0.54-0.86, p-value=0.01) and the cut-off of 65 km run per week is a good indicator of the presence of irregular menstrual cycle (sensitivity (SE) and specificity (SP) of 55% and 81.48%). Menstrual cycle disorders are very frequent in female athletes, and the variable of km run per week may play a role in screening endurance athletes at high risk for these disorders.

Machine learning and radiomics for segmentation and classification of adnexal masses on ultrasound.

Ultrasound-based models exist to support the classification of adnexal masses but are subjective and rely upon ultrasound expertise. We aimed to develop an end-to-end machine learning (ML) model capable of automating the classification of adnexal masses. In this retrospective study, transvaginal ultrasound scan images with linked diagnoses (ultrasound subjective assessment or histology) were extracted and segmented from Imperial College Healthcare, UK (ICH development dataset; n = 577 masses; 1444 images) and Morgagni-Pierantoni Hospital, Italy (MPH external dataset; n = 184 masses; 476 images). A segmentation and classification model was developed using convolutional neural networks and traditional radiomics features. Dice surface coefficient (DICE) was used to measure segmentation performance and area under the ROC curve (AUC), F1-score and recall for classification performance. The ICH and MPH datasets had a median age of 45 (IQR 35-60) and 48 (IQR 38-57) years old and consisted of 23.1% and 31.5% malignant cases, respectively. The best segmentation model achieved a DICE score of 0.85 ± 0.01, 0.88 ± 0.01 and 0.85 ± 0.01 in the ICH training, ICH validation and MPH test sets. The best classification model achieved a recall of 1.00 and F1-score of 0.88 (AUC:0.93), 0.94 (AUC:0.89) and 0.83 (AUC:0.90) in the ICH training, ICH validation and MPH test sets, respectively. We have developed an end-to-end radiomics-based model capable of adnexal mass segmentation and classification, with a comparable predictive performance (AUC 0.90) to the published performance of expert subjective assessment (gold standard), and current risk models. Further prospective evaluation of the classification performance of this ML model against existing methods is required.