Whole-brain structural magnetic resonance imaging-based classification of primary dysmenorrhea in pain-free phase: a machine learning study.

To develop a machine learning model to investigate the discriminative power of whole-brain gray-matter (GM) images derived from primary dysmenorrhea (PDM) women and healthy controls (HCs) during the pain-free phase and further evaluate the predictive ability of contributing features in predicting the variance in menstrual pain intensity. Sixty patients with PDM and 54 matched female HCs were recruited from the local university. All participants underwent the head and pelvic magnetic resonance imaging scans to calculate GM volume and myometrium-apparent diffusion coefficient (ADC) during their periovulatory phase. Questionnaire assessment was also conducted. A support vector machine algorithm was used to develop the classification model. The significance of model performance was determined by the permutation test. Multiple regression analysis was implemented to explore the relationship between discriminative features and intensity of menstrual pain. Demographics and myometrium ADC-based classifications failed to pass the permutation tests. Brain-based classification results demonstrated that 75.44% of subjects were correctly classified, with 83.33% identification of the patients with PDM (P < 0.001). In the regression analysis, demographical indicators and myometrium ADC accounted for a total of 29.37% of the variance in pain intensity. After regressing out these factors, GM features explained 60.33% of the remaining variance. Our results suggested that GM volume can be used to discriminate patients with PDM and HCs during the pain-free phase, and neuroimaging features can further predict the variance in the intensity of menstrual pain, which may provide a potential imaging marker for the assessment of menstrual pain intervention.