BME MS Thesis Defense: Brynne MacWilliams, “Breast Thermal Patterning in Response to Reproductive Hormones and Exercise”

Master’s Thesis Defense 

Tuesday, May 6, 2025 

50P 4911 

12:00-1:00pm 

“Breast Thermal Patterning in Response to Reproductive Hormones and Exercise” 

Brynne MacWilliams 

Abstract: Breast thermography has potential as a non-invasive monitoring device for breast health, but its clinical utility in reproductive and exercise physiology remains limited by a lack of normative data on how hormones and physical activity modulate breast surface temperature. In this study, we recruited five healthy, regularly menstruating women (ages 21–23) and monitored them over one complete menstrual cycle (30 days). Daily first-morning urine samples were tested for indicators of estrogen (estrone-3-glucuronide (E3G)), and progesterone (pregnanediol-3-glucuronide (PdG)), luteinizing hormone (LH), and follicle-stimulating hormone (FSH). Resting breast temperature was measured biweekly using infrared thermography and skin-mounted thermistors. Once weekly, participants completed a 20-minute exercise protocol at 80–90% of age-predicted maximal heart rate, with post-exercise breast temperature recorded over time.  

Exercise induced a clear, statistically significant change in breast surface temperature from baseline (unpaired t = 3.64, p = 0.0003; paired t = 2.75, p = 0.0066). Repeated-measures ANOVA revealed cyclic variation in resting temperature across menstrual phases (F(3,511) = 10.29, p < 1×10−6) and significant inter-individual differences (F(4,511) = 29.57, p < 1×10−6). A global multiple linear regression of breast temperature on LH, E3G, PdG and FSH yielded an R2 = 0.117 with PdG emerging as the strongest single predictor (β = 0.144). Sensor-specific regressions showed modest regional sensitivity, with some locations achieving R2 = 0.150 for PdG. Mixed-effects modeling (random intercepts for ParticipantID) rendered all hormone coefficients non-significant (p > 0.98), underscoring the importance of personalized baselines. Pearson correlation confirmed PdG’s thermogenic role (r = 0.234). Models predicting post-exercise temperature change from hormone levels performed poorly (all R2 < 0), suggesting that these hormones do not explain behavior of thermal changes in the breast following exercise. These findings suggest that progesterone-related and exercise-induced thermal patterns produce distinct breast thermal signatures but also highlight substantial inter- and intra-individual variability. This supports the need for personalized calibration in clinical thermography and points toward future integration of machine-learning approaches to interpret spatial–temporal temperature patterns for reproductive health monitoring. 

For a zoom link, please email kharrison@wpi.edu