| 张正平,侯晓婧,刘梓瑾,米克德,王志涛,孟淑萍,田兴仓,朱力.CT影像组学列线图预测胸腺上皮肿瘤Ki-67表达[J].中国医学影像技术,2024,40(11):1693~1697 |
| CT影像组学列线图预测胸腺上皮肿瘤Ki-67表达 |
| CT radiomics nomogram for predicting Ki-67 expression of thymus epithelial tumors |
| 投稿时间：2024-03-29 修订日期：2024-07-09 |
| DOI：10.13929/j.issn.1003-3289.2024.11.013 |
| 中文关键词: 胸腺肿瘤 Ki-67抗原 体层摄影术,X线计算机 影像组学 列线图 |
| 英文关键词:thymus neoplasms Ki-67 antigen tomography, X-ray computed radiomics nomogram |
| 基金项目:宁夏自然科学基金(2022AAC03581)。 |
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| 中文摘要: |
| 目的 观察CT影像组学列线图预测胸腺上皮肿瘤Ki-67表达的价值。方法 回顾性分析163例胸腺上皮肿瘤患者,包括训练集114例及验证集49例,根据Ki-67指数于集内划分低表达(＜50%)及高表达(≥50%)亚组。采用多因素logistic回归分析筛选胸腺上皮肿瘤Ki-67表达的独立预测因素并以之构建临床-CT模型;基于胸部平扫及静脉期增强CT图提取并筛选最优影像组学特征,分别构建平扫及增强影像组学模型,计算其影像组学评分Radscore平扫及Radscore增强;基于临床-CT模型、Radscore平扫及Radscore增强构建列线图模型。绘制受试者工作特征曲线,计算曲线下面积(AUC),评估各模型预测胸腺上皮肿瘤Ki-67表达的效能。结果 患者性别、病灶强化CT值为胸腺上皮肿瘤Ki-67表达的独立预测因素(P均＜0.05)。临床-CT模型,平扫、增强影像组学模型及列线图模型预测训练集胸腺上皮肿瘤Ki-67表达的AUC分别为0.736、0.814、0.836及0.857,在验证集分别为0.746、0.746、0.750及0.799。结论 CT影像组学列线图可用于预测胸腺上皮肿瘤Ki-67表达。 |
| 英文摘要: |
| Objective To observe the value of CT radiomics nomogram for predicting Ki-67 expression of thymus epithelial tumors. Methods Totally 163 patients with thymus epithelial tumor, including 114 patients in training set and 49 patients in validation set were retrospectively enrolled. The patients were further divided into low expression (＜50%) and high expression (≥50%) subgroups according to Ki-67 index. Multivariate logistic regression analysis was performed to screen independent predicting factors of Ki-67 expression in thymus epithelial tumors, and clinical-CT model was constructed. The optimal radiomics features were extracted and screened based on chest plain and venous phase enhanced CT images, respectively. Then radiomics modelplain and radiomics modelenhanced were constructed, and Radscoreplain and Radscoreenhanced were calculated, respectively. The nomogram model was constructed based on clinical-CT model, Radscoreplain and Radscoreenhanced. Receiver operating characteristic curves were drawn, and the area under the curves (AUC) were calculated to evaluate the efficacy of each model for predicting Ki-67 expression of thymus epithelial tumors. Results Patient's gender and enhanced CT value of lesion were both independent predicting factors of Ki-67 expression in thymus epithelial tumors (both P＜0.05). The AUC of clinical-CT model, radiomics modelplain, radiomics modelenhanced and nomogram model for predicting Ki-67 expression was 0.736, 0.814, 0.836 and 0.857 in training set, which was 0.746, 0.746, 0.750 and 0.799 in validation set, respectively. Conclusion CT radiomics nomogram could be used to predict Ki-67 expression of thymus epithelial tumors. |
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