基于T2WI 3D纹理分析评估宫颈癌组织学分级

目的 探讨基于T2WI的3D纹理分析评估宫颈癌组织学分级的价值.方法 回顾性分析经病理证实的175例宫颈癌患者,其中高分化41例(高分化组),中分化76例(中分化组),低分化58例(低分化组),术前均接受常规MR平扫及增强扫查.采用ITK-SNAP软件勾画感兴趣体积(VOI),以LIFEx软件计算获取41个纹理参数;比...

Three-dimensional texture analysis based on T2WI for evaluation on histological grade of cervical cancer

Objective To explore the value of 3D texture analysis (TA) based on T2WI for predicting histological grade of cervical cancer. Methods Data of 175 patients of cervical cancer confirmed by pathology were retrospectively analyzed, including 41 cases of high differentiation (high differentiation group), 76 of middle differentiation (middle differentiation group) and 58 cases of low differentiation (low differentiation group) cervical cancer. All patients underwent conventional plain and enhanced MR scanning before operation. The volume of interest (VOI) was delineated by using ITK-SNAP software, and 41 texture parameters were calculated and obtained with LIFEx software. Then the texture parameters were compared among 3 groups. Taken texture parameters statistically different among groups, Logistic regression models for predicting histological grade of cervical cancer before surgical operations were established, and their effectiveness were analyzed. Results Statistical significant differences of 8 parameters (GLNUz, ZLNU, GLCM-Energy, Busyness, GLNUr, RLNU, Volume-vx and Volume-ml) were found among 3 groups (all P<0.05). There were statistically significant differences of 8 texture parameters between low differentiation group and high differentiation group (all P<0.05), while tatistical significant differences of Energy, GLNUz and ZLNU were detected between middle differentiation group and high differentiation group (all P<0.05). Eight texture parameters being statistically different among low, middle and high differentiation groups were all correlated with histological grade (|r|=0.491-0.567). AUC value of 8 statistically different texture parameters between low and high differentiation groups were 0.711-0.774, of Logistic regression model based on these parameters was 0.875, with sensitivity of 87.50% and specificity of 77.78%. AUC of 3 texture parameters being statistically different between middle and high differentiation groups were 0.685-0.717, of Logistic regression model was 0.753, with sensitivity of 78.75% and specificity of 72.92%, respectively. Conclusion 3D TA based on T2WI had certain value in predicting histological grade of cervical cancer before operation, and the Logistic regression models were more effective than TA.