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

作者：Karen Drukker, Milica Medved, Carla Harmath, Maryellen L. Giger, Obianuju Sandra Madueke-Laveaux · 发表于：Journal of medical imaging · 年份：2024 · DOI：10.1117/1.jmi.11.5.054501 · 被引用次数：5 · 研究领域：Uterine Myomas and Treatments、Endometrial and Cervical Cancer Treatments、Ovarian cancer diagnosis and treatment

Significance: Uterine fibroids (UFs) can pose a serious health risk to women. 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. Aim: We aim to develop a predictive model to identify UFs with increased growth rates and possible resultant morbidity. Approach: We retrospectively analyzed 44 expertly outlined UFs from 20 patients who underwent two multi-parametric MR imaging exams as part of a prospective study over an average of 16 months. We identified 44 initial features by extracting quantitative magnetic resonance imaging (MRI) features plus morphological and textural radiomics features from DCE, T2, and apparent diffusion coefficient sequences. Principal component analysis reduced dimensionality, with the smallest number of components explaining over 97.5% of the variance selected. Employing a leave-one-fibroid-out scheme, a linear discriminant analysis classifier utilized these components to output a growth risk score. Results: 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. Conclusion: We developed a promising predictive model utilizing quantitative MRI features and principal component analysis to identify UFs with increased growth rates. Furthermore, the model's...