Related Experiment Video For Endometrial carcinoma (EC)
Updated: Jun 1, 2025
Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
Published on: December 15, 2014
Differentiation of early-stage endometrial carcinoma from benign endometrial lesions: a comparative study of six
Qiu Bi1, Yuchen Deng1, Na Xu2
1Department of MRI, the First People's Hospital of Yunnan Province, the Affiliated Hospital of Kunming University of Science and Technology, Kunming, China.
Background:
Accurate differentiation between benign and malignant endometrial lesions holds substantial clinical importance. This study aimed to evaluate the efficacy of various diffusion models in the preoperative diagnosis of early-stage endometrial carcinoma (EC).
Methods:
A total of 72 consecutive patients with benign or malignant endometrial lesions from the First People's Hospital of Yunnan Province were prospectively enrolled between April 2021 and July 2023. Fourteen diffusion parameters derived from monoexponential diffusion-weighted imaging (DWI), diffusion kurtosis imaging (DKI), intravoxel incoherent motion (IVIM), stretched exponential model (SEM), continuous-time random walk (CTRW), and fractional order calculus (FROC) models were calculated and compared. Independent predictors of early-stage EC were identified using logistic regression analysis. The performance of the diffusion parameters, both individually and in combination with effective clinical indicators, for differentiating benign and malignant endometrial lesions was evaluated.
Results:
This study consisted of 17 patients with benign endometrial lesions and 55 patients with EC. Significant differences in age and menopausal status were observed between the benign and malignant endometrial groups (P=0.015 and P=0.011, respectively). With the exception of the pseudodiffusion coefficient (D*) and perfusion fraction (f), all other parameters exhibited significant differences between the benign and malignant groups (P<0.05). Mean kurtosis (MK), true diffusion coefficient (D), and temporal diffusion heterogeneity index (αCTRW) were identified as independent predictors of early-stage EC, achieving an area under the curve (AUC) of 0.903 [95% confidence interval (CI): 0.824-0.982], surpassing that of any individual diffusion parameter. The combination of these independent predictors with menopausal status yielded the highest AUC (0.922, 95% CI: 0.845-0.999), accuracy (93.1%), and sensitivity (100.0%).
Conclusions:
MK, D, and αCTRW have the potential to serve as independent predictors in predicting early-stage EC, and the performance can be enhanced when combined with menopausal status.
