RESUMEN
PURPOSE: We examined associations between patient and treatment characteristics with longitudinally collected patient-reported outcome (PRO) measures to provide a data-informed description of the experiences of women undergoing treatment for endometrial cancer. METHODS: We administered National Institutes of Health Patient Reported Outcomes Measurement Information System (PROMIS) questionnaires at the preoperative visit and at 6 and 12 months after surgery. Anxiety, depression, fatigue, sleep disturbance, pain, physical function, and ability to participate in social roles were assessed. Analysis of variance (ANOVA) and linear mixed models were used to examine associations between patient characteristics and PRO measures at baseline and through time. RESULTS: Of 187 women enrolled, 174 (93%) and 103 (69%) completed the 6- and 12-month questionnaires, respectively. Anxiety was substantially elevated at baseline (half of one population-level standard deviation) and returned to general population mean levels at 6 and 12 months. Younger age, Medicaid/None/Self-pay insurance, prevalent diabetes, and current smoking were associated with higher symptom burden on multiple PRO measures across the three time points. Women with aggressive histology, higher disease stage, or those with adjuvant treatment had worse fatigue at 6 months, which normalized by 12 months. CONCLUSIONS: We observed a high symptom burden at endometrial cancer diagnosis, with most PRO measures returning to general population means by 1 year. Information on risk factor-PRO associations can be used during the clinical visit to inform supportive service referral. IMPLICATIONS FOR CANCER SURVIVORS: These findings can inform clinicians' discussions with endometrial cancer survivors regarding expected symptom trajectory following diagnosis and treatment.
RESUMEN
Recent advances in imaging technologies now allow for real-time tracking of fast-moving immune cells as they search for targets such as pathogens and tumor cells through complex three-dimensional tissues. Cytotoxic T cells are specialized immune cells that continually scan tissues for such targets to engage and kill, and have emerged as the principle mediators of breakthrough immunotherapies against cancers. Modeling the way these T cells move is of great value in furthering our understanding of their collective search efficiency. T-cell motility is characterized by heterogeneity at two levels: (a) Individual cells display different distributions of translational speeds and turning angles, and (b) each cell can during a given track, its motility, switch between local search and directional motion. Despite a likely considerable influence on a motile population's search performance, statistical models that accurately capture both such heterogeneities in a distinguishing manner are lacking. Here, we model three-dimensional T-cell trajectories through a spherical representation of their incremental steps and compare model outputs to real-world motility data from primary T cells navigating physiological environments. T cells in a population are clustered based on their directional persistence and characteristic "step lengths" therein capturing between-cell heterogeneity. The motility dynamics of cells within each cluster are individually modeled through hidden Markov model to capture within-cell transitions between local and more extensive search patterns. We explore the importance of explicitly capturing altered motility patterns when cells lie in close proximity to one another, through a non-homogenous hidden Markov model.