Bi Qiu, Deng Yuchen, Xu Na, Wu Shan, Zhang Hongjiang, Huang Yichen, Zhang Shuni, Wang Shaoyu, Wu Yunzhu, Wu Kunhua, Zhang Jie
Department of MRI, the First People's Hospital of Yunnan Province, the Affiliated Hospital of Kunming University of Science and Technology, Kunming, China.
Department of Radiology, Municipal People's Hospital of Chuxiong, Chuxiong, China.
Quant Imaging Med Surg. 2025 Jan 2;15(1):121-134. doi: 10.21037/qims-24-896. Epub 2024 Nov 13.
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).
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.
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 (α) 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%).
MK, D, and α have the potential to serve as independent predictors in predicting early-stage EC, and the performance can be enhanced when combined with menopausal status.
准确鉴别良性和恶性子宫内膜病变具有重要的临床意义。本研究旨在评估各种扩散模型在早期子宫内膜癌(EC)术前诊断中的效能。
2021年4月至2023年7月,前瞻性纳入云南省第一人民医院72例连续的良性或恶性子宫内膜病变患者。计算并比较了从单指数扩散加权成像(DWI)、扩散峰度成像(DKI)、体素内不相干运动(IVIM)、拉伸指数模型(SEM)、连续时间随机游走(CTRW)和分数阶微积分(FROC)模型得出的14个扩散参数。采用逻辑回归分析确定早期EC的独立预测因素。评估了扩散参数单独及与有效临床指标联合用于鉴别良性和恶性子宫内膜病变的性能。
本研究包括17例良性子宫内膜病变患者和55例EC患者。良性和恶性子宫内膜组在年龄和绝经状态方面存在显著差异(分别为P = 0.015和P = 0.011)。除伪扩散系数(D*)和灌注分数(f)外,所有其他参数在良性和恶性组之间均表现出显著差异(P < 0.05)。平均峰度(MK)、真实扩散系数(D)和时间扩散异质性指数(α)被确定为早期EC的独立预测因素,曲线下面积(AUC)为0.903 [95%置信区间(CI):0.824 - 0.982],超过任何单个扩散参数。这些独立预测因素与绝经状态联合产生了最高的AUC(0.922,95% CI:0.845 - 0.999)、准确性(93.1%)和敏感性(100.0%)。
MK、D和α有可能作为预测早期EC的独立预测因素,与绝经状态联合时性能可得到提高。