基于T2WI纹理分析鉴别宫颈鳞癌和腺癌的价值研究
Application of T2-weighted texture analysis to differentiate cervical squamous cell carcinoma from adenocarcinoma
孙静 1余琴 1李静 1林慧子 1夏进东 2伋自翔 3熊波4
作者信息
- 1. 同济大学附属养志康复医院放射科 上海 201600
- 2. 上海交通大学医学院附属松江医院放射科 上海 201600
- 3. 同济大学医学院 上海 200000
- 4. 西门子数字医疗科技(上海)有限公司 上海 200131
- 折叠
摘要
目的 探讨T2WI纹理分析在术前鉴别宫颈鳞癌和宫颈腺癌中的价值.方法 选取 92 例患者的术前T2WI图像,从A·K软件中提取纹理参数共 1117 个,进一步进行纹理分析.通过Mann-Whitney u检验、单变量logistic回归分析和最小冗余最大相关性算法,选择纹理特征来区分宫颈鳞癌和宫颈腺癌.基于随机森林算法构建区分鳞癌和腺癌的预测模型,并通过受试者工作特征(ROC)曲线分析,以评价模型的诊断性能.最后,通过 10 次留组交叉验证法(LGOCV)评估预测模型的稳健性和可重复性.结果 本文中在特征选择后,最终保留了 5 个鉴别宫颈鳞癌和宫颈腺癌的纹理特征用于构建RF模型.预测模型在区分宫颈鳞癌和宫颈腺癌方面具有良好的分类性能:准确率为 81.4%、敏感性为81.33%,特异性为 81.8%.此外,使用 10 次LGOCV算法证明了预测模型的稳健性和可重现性(平均AUC,0.83).结论 磁共振T2WI纹理分析在鉴别宫颈鳞癌和宫颈腺癌方面有一定的鉴别诊断价值.
Abstract
objective To investigate the value of T2WI texture analysis in differentiating cervical squamous cell carcinoma from cervical adenocarcinoma before surgery.Methods The preoperative T2WI images of 92 patients(78 cases of cervical squamous cell carcinoma and 14 of adenocarcinoma)were analyzed retrospectively.A total of 1133 texture parameters were ex-tracted from A·K software for further texture analysis.Textural features were selected to distinguish cervical squamous cell car-cinoma from cervical adenocarcinoma by Mann-Whitney u test,univariate logistic regression analysis,and least redundant maxi-mum correlation algorithm.In addition,a predictive model for distinguishing squamous cell carcinoma from adenocarcinoma was constructed based on a random forest algorithm and analyzed by receiver operating characteristic(ROC)curves to evaluate the diagnostic performance of the model.Finally,the robustness and reproducibility of the prediction model were assessed by the 10-leave-group cross-validation(LGOCV)method.Results In this study,after feature selection,five texture features differentiat-ing cervical squamous cell carcinoma from cervical adenocarcinoma were finally retained for constructing the RF model.The pre-dictive model had good classification performance in distinguishing cervical squamous cell carcinoma from cervical adenocarci-noma,with accuracy of 81.4%,sensitivity of 81.33%,and specificity of 81.8%.In addition,robustness and reproducibility of the prediction model was demonstrated using a 10-fold LGOCV algorithm(mean AUC,0.83).Conclusion MR T2WI texture analysis has certain value in differentiating cervical squamous cell carcinoma from cervical adenocarcinoma.
关键词
磁共振成像/宫颈腺癌/宫颈鳞癌/纹理分析Key words
Magnetic resonance imaging/Cervical adenocarcinoma/Cervical squamous cell carcinoma/Texture analysis引用本文复制引用
基金项目
上海市自然科学基金(23ZR1457400)
出版年
2024
NETL
NSTL
万方数据