Drukker Karen, Medved Milica, Harmath Carla B, Giger Maryellen L, Madueke-Laveaux Obianuju S
University of Chicago, Department of Radiology, Chicago, Illinois, United States.
University of Chicago, Department of Obstetrics and Gynecology, Chicago, Illinois, United States.
J Med Imaging (Bellingham). 2024 Sep;11(5):054501. doi: 10.1117/1.JMI.11.5.054501. Epub 2024 Sep 12.
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.
We aim to develop a predictive model to identify UFs with increased growth rates and possible resultant morbidity.
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.
The classifier incorporated the first three principal components and achieved an area under the receiver operating characteristic curve of 0.80 (95% confidence interval [0.69; 0.91]), effectively distinguishing UFs growing faster than the median growth rate of 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.
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 discrimination ability supports its potential clinical utility in developing tailored patient and fibroid-specific management once validated on a larger cohort.
子宫肌瘤（UFs）会对女性健康构成严重风险。UFs是良性肿瘤，临床表现从无症状到引发使人衰弱的症状不等。由于我们无法预测UFs的生长速度和未来发病率，UFs的治疗受到限制。
我们旨在开发一种预测模型，以识别生长速度加快且可能导致发病的UFs。
我们对20例患者的44个经过专业勾勒的UFs进行了回顾性分析，这些患者作为一项前瞻性研究的一部分，平均在16个月内接受了两次多参数磁共振成像检查。我们通过从DCE、T2和表观扩散系数序列中提取定量磁共振成像（MRI）特征以及形态学和纹理放射组学特征，确定了44个初始特征。主成分分析降低了维度，选择了能够解释超过97.5%方差的最少数量的成分。采用留一肌瘤法，线性判别分析分类器利用这些成分输出生长风险评分。
该分类器纳入了前三个主成分，在受试者工作特征曲线下的面积为0.80（95%置信区间[0.69；0.91]），有效地将队列中生长速度快于中位数生长速度的UFs与生长较慢的UFs区分开来。生存分析根据中位数生长风险评分对队列进行划分，得出风险比为0.33[0.15；0.76]，证明了其潜在的临床应用价值。
我们开发了一种有前景的预测模型，利用定量MRI特征和主成分分析来识别生长速度加快的UFs。此外，该模型的辨别能力支持其在更大队列中得到验证后，在制定针对患者和肌瘤的个性化治疗方案方面具有潜在的临床应用价值。
