Differentiating Uterine Sarcoma From Atypical Leiomyoma on Preoperative Magnetic Resonance Imaging Using Logistic Regression Classifier: Added Value of Diffusion-Weighted Imaging-Based Quantitative Parameters
Hokun Kim, Sung Eun Rha, Yu Ri Shin, Eu Hyun Kim, Soo Youn Park, Su Lim Lee, Ahwon Lee, Mee‐Ran Kim
Abstract
Objective: To evaluate the added value of diffusion-weighted imaging (DWI)-based quantitative parameters to distinguish uterine sarcomas from atypical leiomyomas on preoperative magnetic resonance imaging (MRI).Materials and Methods: A total of 138 patients (age, 43.7 10.3 years) with uterine sarcoma (n = 44) and atypical leiomyoma (n = 94) were retrospectively collected from four institutions.The cohort was randomly divided into training (84/138, 60.0%) and validation (54/138, 40.0%) sets.Two independent readers evaluated six qualitative MRI features and two DWI-based quantitative parameters for each index tumor.Multivariable logistic regression was used to identify the relevant qualitative MRI features.Diagnostic classifiers based on qualitative MRI features alone and in combination with DWI-based quantitative parameters were developed using a logistic regression algorithm.The diagnostic performance of the classifiers was evaluated using a cross-table analysis and calculation of the area under the receiver operating characteristic curve (AUC).Results: Mean apparent diffusion coefficient value of uterine sarcoma was lower than that of atypical leiomyoma (mean standard deviation, 0.94 0.30 10 -3 mm 2 /s vs. 1.23 0.25 10 -3 mm 2 /s; P < 0.001), and the relative contrast ratio was higher in the uterine sarcoma (8.16 2.94 vs. 4.19 2.66; P < 0.001).Selected qualitative MRI features included ill-defined margin (adjusted odds ratio [aOR], 17.9; 95% confidence interval [CI], 1.41-503, P = 0.040), intratumoral hemorrhage (aOR, 27.3; 95% CI, 3.74-596, P = 0.006), and absence of T2 dark area (aOR, 83.5; 95% CI, 12.4-1916, P < 0.001).The classifier that combined qualitative MRI features and DWI-based quantitative parameters showed significantly better performance than without DWI-based parameters in the validation set (AUC, 0.92 vs. 0.78; P < 0.001). Conclusion:The addition of DWI-based quantitative parameters to qualitative MRI features improved the diagnostic performance of the logistic regression classifier in differentiating uterine sarcomas from atypical leiomyomas on preoperative MRI.