Su, H. Irene

Treatment-induced ovarian function loss is a significant concern for many young patients with breast cancer. Accurately predicting this risk is crucial for counselling young patients and informing their fertility-related decision-making. However, current risk prediction models for treatment-related ovarian function loss have limitations. To provide a broader representation of patient cohorts and improve feature selection, we combined retrospective data from six datasets within the FoRECAsT (Infertility after Cancer Predictor) databank, including 2679 pre-menopausal women diagnosed with breast cancer. This combined dataset presented notable missingness, prompting us to employ cross imputation using the k-nearest neighbours (KNN) machine learning (ML) algorithm. Employing Lasso regression, we developed an ML model to forecast the risk of treatment-related amenorrhea as a surrogate marker of ovarian function loss at 12 months after starting chemotherapy. Our model identified 20 variables significantly associated with risk of developing amenorrhea. Internal validation resulted in an area under the receiver operating characteristic curve (AUC) of 0.820 (95% CI: 0.817–0.823), while external validation with another dataset demonstrated an AUC of 0.743 (95% CI: 0.666–0.818). A cutoff of 0.20 was chosen to achieve higher sensitivity in validation, as false negatives—patients incorrectly classified as likely to regain menses—could miss timely opportunities for fertility preservation if desired. At this threshold, internal validation yielded sensitivity and precision rates of 91.3% and 61.7%, respectively, while external validation showed 92.9% and 60.0%. Leveraging ML methodologies, we not only devised a model for personalised risk prediction of amenorrhea, demonstrating substantial enhancements over existing models but also showcased a robust framework for maximally harnessing available data sources.

AbstractThe North American Menopause Society held the 2019 Pre-Meeting Symposium on September 25, 2019, in Chicago, Illinois, to review the current state of the science related to the physiology of the perimenopause and to address management of the most prevalent and pressing clinical issues. The perimenopause, as defined by the Stages of Reproductive Aging Workshop + 10, encompasses the menopause transition as well as the first year following menopause, the final menstrual period. This phase in the continuum of women's reproductive lives had been one of the least well understood. Fortunately, contributions from a number of prospective, longitudinal, decades-long studies have provided a better understanding of the perimenopause, whereas posing important new questions related to symptom interaction and linkages between symptoms and long-term health. There is now added clarity to distinguish the effects of reproductive hormonal changes from aging. The variation in symptoms, including vasomotor symptoms, among women over time including differences in experiences by ethnicity and race, provides paradigm shifts in clinical perspective. Refinements in understanding the character, timing, and potential predictive markers for menstrual cycles during the transition have emerged. From the perspective of myriad clinical management challenges, significant progress in recommendations for evaluation and therapeutic approaches has been achieved. Finally, recognizing the menopause transition as an opportunity to initiate positive lifestyle changes to enhance future health was emphasized.

Abstract Objective: More than 5,000 premenopausal women are diagnosed with lung cancer annually in the United States. Limited data exist regarding the risk of treatment-related amenorrhea, a surrogate for infertility and early menopause, after systemic therapies for lung cancer. Methods: Premenopausal women diagnosed with lung cancer under age 50 were surveyed at diagnosis and annually thereafter about their menstrual status as a part of the Mayo Clinic Epidemiology and Genetics of Lung Cancer Research Program. Types of lung cancer-directed treatments were recorded, and frequencies of self-reported menopause at each survey were calculated. Results: A cohort of 182 premenopausal women were included in this study, with average age at lung cancer diagnosis 43 years (SD 6). Among the 85 patients who received chemotherapy, 64% self-reported that they had become menopausal within a year of diagnosis. Platinum salts were universally included in these chemotherapy regimens, and the majority of these women also received taxanes within 1 year of diagnosis. Only 15% of the 94 patients who did not receive systemic therapy within 1 year of diagnosis experienced self-reported menopause. Three patients received targeted therapy alone, two of whom remained premenopausal at the final qualifying survey, completed a median of 3 years after diagnosis. Conclusions: Chemotherapy for lung cancer patients appears to increase risk of early loss of menses in survivors.