Bin Yan1,2, Xiufen Liang2, Tingting Zhao1, Caixia Ding3, Ming Zhang1. 1. Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China. 2. Department of Radiology, Shaanxi Provincial Tumor Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China. 3. Department of Pathology, Shaanxi Provincial Tumor Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China.
Abstract
BACKGROUND: The tumor histological grade is closely related to the prognosis of endometrial cancer (EC). The use of the apparent diffusion coefficient (ADC), tumor volume, and MRI-based texture analysis has allowed exciting advances in predicting EC grade before surgery. However, whether this constitutes a simple, convenient, and powerful diagnostic method remains unknown. PURPOSE: To explore the utility of standard deviation (SD) of the ADC (ADCSD) for predicting the tumor grade in patients with EC. MATERIAL AND METHODS: We retrospectively evaluated 138 patients with EC. All patients underwent unenhanced MRI and diffusion-weighted imaging (DWI). The mean ADC value (ADCmean) and SD were obtained using a freehand region of interest traced on the ADC map. Spearman's linear correlation coefficients were calculated to analyze the correlations between the indexes (including ADCSD and the ADCmean) and the Ki-67 index. The Kruskal-Wallis and Mann-Whitney U tests were used to compare differences in the index results among tumor grades. RESULTS: A significant difference in ADCSD was observed among the tumor grades (P=0.000), and the ADCSD value was significantly higher for high-grade EC than for low-grade tumors (289.7 vs. 216.3×10-6mm2 /s, P=0.000). A statistically significant positive correlation was observed between ADCSD and the Ki-67 index (r=0.364, P=0.000). According to the receiver operating characteristic curve, ADCSD ≥240.2×10-6mm2 /s predicted high-grade EC with a sensitivity, specificity, and accuracy of 73.1%, 80.2%, and 77.5%, respectively. CONCLUSION: Based on the intratumor heterogeneity of EC, ADCSD represents a potential method for the preoperative prediction of high-grade EC, although further studies are needed.
BACKGROUND: The tumor histological grade is closely related to the prognosis of endometrial cancer (EC). The use of the apparent diffusion coefficient (ADC), tumor volume, and MRI-based texture analysis has allowed exciting advances in predicting EC grade before surgery. However, whether this constitutes a simple, convenient, and powerful diagnostic method remains unknown. PURPOSE: To explore the utility of standard deviation (SD) of the ADC (ADCSD) for predicting the tumor grade in patients with EC. MATERIAL AND METHODS: We retrospectively evaluated 138 patients with EC. All patients underwent unenhanced MRI and diffusion-weighted imaging (DWI). The mean ADC value (ADCmean) and SD were obtained using a freehand region of interest traced on the ADC map. Spearman's linear correlation coefficients were calculated to analyze the correlations between the indexes (including ADCSD and the ADCmean) and the Ki-67 index. The Kruskal-Wallis and Mann-Whitney U tests were used to compare differences in the index results among tumor grades. RESULTS: A significant difference in ADCSD was observed among the tumor grades (P=0.000), and the ADCSD value was significantly higher for high-grade EC than for low-grade tumors (289.7 vs. 216.3×10-6mm2 /s, P=0.000). A statistically significant positive correlation was observed between ADCSD and the Ki-67 index (r=0.364, P=0.000). According to the receiver operating characteristic curve, ADCSD ≥240.2×10-6mm2 /s predicted high-grade EC with a sensitivity, specificity, and accuracy of 73.1%, 80.2%, and 77.5%, respectively. CONCLUSION: Based on the intratumor heterogeneity of EC, ADCSD represents a potential method for the preoperative prediction of high-grade EC, although further studies are needed.
Entities:
Keywords:
Endometrial neoplasms; apparent diffusion coefficient; cell differentiation; magnetic resonance imaging; standard deviation
Authors: Tao Zheng; Linsha Yang; Juan Du; Yanchao Dong; Shuo Wu; Qinglei Shi; Xiaohan Wang; Lanxiang Liu Journal: Front Oncol Date: 2021-06-21 Impact factor: 6.244