Multi-b-value diffusion weighted imaging for preoperative evaluation of risk stratification in early-stage endometrial cancer.

Multi-b-value diffusion weighted imaging for preoperative evaluation of risk stratification in early-stage endometrial cancer.

Zhang, Qi;Yu, Xiaoduo;Lin, Meng;Xie, Lizhi;Zhang, Miaomiao;Ouyang, Han;Zhao, Xinming;
European journal of radiology 2019 Vol. 119 pp. 108637
248
zhang2019multibvalueeuropean

Abstract

To investigate the application of multi-b-value DWI parameters for the assessment of risk stratification in early-stage endometrial cancer (EC).Fifty-three patients with early-stage EC who preoperatively underwent multi-b-value DWI with 13 b values (from 0 to 2000s/mm²) were included in this study. Multi-b-value DWI derived parameters, including apparent diffusion coefficient (ADC), true diffusivity (D), perfusion-related diffusivity (D*) and perfusion fraction (f) were measured independently by two radiologists. In addition, binary logical regression model was used to calculate predicative probability of combined parameters indicating statistical significance in differentiating risk stratification of early-stage endometrial cancer. Receiver operating characteristic analysis was performed for all single and combined parameters.The ADC and D values were significantly lower in intermedium-risk compared with low-risk (P = 0.000 and 0.011), as well as high-risk compared with low-risk of early-stage EC (P = 0.001 and 0.013), while f values only showed significant differences between low-risk and intermedium-risk groups (P = 0.011). Among the single parameters, the ADC values had the highest area under the ROC curve (AUC) in the identification of the low-risk of early-stage EC (AUC=0.892). Moreover, the combination of ADC and f value had the best diagnostic performance with the AUC of 0.912, the sensitivity of 81.1% and the specificity of 87.5%.The multi-b-value DWI parameters provide valuable imaging biomarkers for the assessment of risk stratification in early-stage endometrial cancer. This approach might facilitate the selection of the optimal therapeutic approach and lead to the greater personalization of cancer care.

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