| 尹进学,卢斌贵,杨佩瑜,钟熹,陈志军,桂思,洪璇阳,李颖慧,孙紫情,李建生.基于T2WI 3D纹理分析评估宫颈癌组织学分级[J].中国医学影像技术,2021,37(1):86~90 |
| 基于T2WI 3D纹理分析评估宫颈癌组织学分级 |
| Three-dimensional texture analysis based on T2WI for evaluation on histological grade of cervical cancer |
| 投稿时间：2019-12-24 修订日期：2020-07-19 |
| DOI：10.13929/j.issn.1003-3289.2021.01.020 |
| 中文关键词: 子宫颈肿瘤 磁共振成像 纹理分析 组织学分级 |
| 英文关键词:uterine cervical neoplasms magnetic resonance imaging texture analysis histological grade |
| 基金项目: |
| 作者 | 单位 | E-mail | | 尹进学 | 广州医科大学附属肿瘤医院放射科, 广东 广州 510095 | | | 卢斌贵 | 广州医科大学附属肿瘤医院放射科, 广东 广州 510095 | | | 杨佩瑜 | 广州医科大学附属肿瘤医院放射科, 广东 广州 510095 | | | 钟熹 | 广州医科大学附属肿瘤医院放射科, 广东 广州 510095 | | | 陈志军 | 广州医科大学附属肿瘤医院放射科, 广东 广州 510095 | | | 桂思 | 广州医科大学附属肿瘤医院放射科, 广东 广州 510095 | | | 洪璇阳 | 广州医科大学附属肿瘤医院放射科, 广东 广州 510095 | | | 李颖慧 | 广州医科大学附属肿瘤医院放射科, 广东 广州 510095 | | | 孙紫情 | 广州医科大学附属肿瘤医院放射科, 广东 广州 510095 | | | 李建生 | 广州医科大学附属肿瘤医院放射科, 广东 广州 510095 | 284007239@qq.com |
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| 中文摘要: |
| 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；比较3组间纹理参数差异，以组间差异有统计学意义的纹理参数构建Logistic回归模型，评价其评估宫颈癌组织学分级的效能。结果 低、中、高分化组间，区域灰度不均匀度（GLNUz）、区长度不均匀度（ZLNU）、能量（GLCM-Energy）、强度（Busyness）、游程灰度级不均匀度（GLNUr）、游程长度不均匀度（RLNU）、体积（Volume-vx）及容积（Volume-ml）8个参数差异有统计学意义（P均<0.05）。低分化组与高分化组间8个纹理参数差异均有统计学意义（P均<0.05），中分化与高分化组间Energy、GLNUz、ZLNU差异有统计学意义（P均<0.05）。低、中、高分化组间差异有统计学意义的8个纹理参数均与组织学分级相关（|r|=0.491~0.567）。低分化与高分化组间8个差异有统计学意义纹理参数鉴别二者的AUC为0.711~0.774，以其构建的Logistic回归模型的AUC为0.875，敏感度87.50%，特异度77.78%。中分化与高分化组间3个差异有统计学意义的纹理参数的AUC为0.685~0.717，以此构建的Logistic回归模型的AUC为0.753，敏感度78.75%，特异度72.92%。结论 基于T2WI的3D纹理分析对术前预测宫颈癌组织学分级有一定价值，其模型诊断效能更高。 |
| 英文摘要: |
| 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. |
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