Radiomics and quantitative multi-parametric MRI for predicting uterine fibroid growth

Uterine fibroids (UFs) are benign tumors that vary in clinical presentation from asymptomatic to causing debilitating symptoms. UF management is limited by our inability to predict UF growth rate and future morbidity. This study aimed to develop a predictive model to identify UFs with increased growth rate and possible resultant morbidity. We retrospectively analyzed 44 expertly-outlined UFs from 21 patients who underwent two multi-parametric MR imaging exams as part of a prospective study over an average of 16 months. We identified 100 initial features by extracting quantitative MRI, morphological and textural radiomics features from DCE, T2, and ADC sequences. Principal component analysis reduced dimensionality, with the smallest number of components explaining over 97.5% of variance selected. Employing a leave-one-fibroid-out scheme, a linear discriminant analysis classifier utilized these components to output a growth risk score. The classifier incorporated the first six principal components and achieved an area under the ROC curve of 0.80 (95% CI [0.69; 0.91]), effectively distinguishing UFs growing faster than the median growth rate of 0.93 cm3/year/fibroid from slower-growing ones within the cohort. Time-to-event analysis, dividing the cohort based on the median growth risk score, yielded a hazard ratio of 0.33 [0.15; 0.76], demonstrating potential clinical utility. In conclusion, this pilot study developed a promising predictive model utilizing quantitative MRI features and principal component analysis to identify UFs with increased growth rate. Furthermore, the model's discrimination ability supports its potential clinical utility in developing tailored patient and fibroid specific management once validated on a larger cohort.