| 马玉萍,朱建国,雍千叶,毛应凡,李海歌.增强CT影像组学联合临床指标预测结直肠癌壁外血管侵犯[J].中国医学影像技术,2024,40(7):1041~1046 |
| 增强CT影像组学联合临床指标预测结直肠癌壁外血管侵犯 |
| Enhanced CT radiomics combined with clinic for predicting extramural venous invasion of colorectal cancer |
| 投稿时间：2023-12-22 修订日期：2024-03-17 |
| DOI：10.13929/j.issn.1003-3289.2024.07.017 |
| 中文关键词: 结直肠肿瘤 体层摄影术,X线计算机 壁外血管侵犯 影像组学 |
| 英文关键词:colorectal neoplasms tomography, X-ray computed extramural venous invasion radiomics |
| 基金项目: |
|
| 摘要点击次数: 790 |
| 全文下载次数: 250 |
| 中文摘要: |
| 目的 观察增强CT影像组学联合临床指标预测结直肠癌壁外血管侵犯(EMVI+)的价值。方法 回顾性分析经术后病理确诊的131例结直肠癌患者资料,按7∶3比例将其分为训练集(n=92,含44例EMVI+、48例EMVI-)与测试集(n=39,含23例EMVI+、16例EMVI-)。基于术前门静脉期CT提取及筛选肿瘤最佳影像组学特征并据以构建影像组学模型;以单因素及多因素logistic回归分析训练集临床、CT及病理学资料,筛选结直肠癌EMVI+的独立预测因素并建立临床模型,基于影像组学模型及临床模型建立联合模型。绘制受试者工作特征曲线,计算曲线下面积(AUC),评估各模型预测结直肠癌EMVI+的效能。以校准曲线及决策曲线分析评估模型校准度及临床实用性。结果 共筛选出4个最佳影像组学特征,以之构建影像组学模型。糖类抗原(CA)19-9及CA72-4均为结直肠癌EMVI+的独立预测因素(OR=1.033、1.285,P均<0.05)。联合模型预测训练集结直肠癌EMVI+的AUC高于影像组学模型和临床模型(AUC=0.908、0.825、0.770,P=0.017、0.003);影像组学、临床及联合模型在测试集的AUC分别为0.751、0.632、0.799,两两之间AUC差异均无统计学意义(P均>0.05)。影像组学模型及联合模型的校准度均较好。训练集以>0.1、测试集以>0.12为阈值时,联合模型的临床净获益较高。结论 增强CT影像组学联合临床能有效预测结直肠癌EMVI。 |
| 英文摘要: |
| Objective To observe the value of enhanced CT radiomics combined with clinical indicators for predicting of extramural venous invasion (EMVI+) of colorectal cancer. Methods Data of 131 patients with colorectal cancer proved by surgery pathology were retrospectively analyzed. The patients were divided into training set (n=92, including 44 cases with EMVI+ and 48 with EMVI-) and test set (n=39, including 23 cases with EMVI+ and 16 with EMVI-) at the ratio of 7∶3. The best radiomics features were extracted based on preoperative portal-venous phase CT to construct a radiomics model. Univariate and multivariate logistic regression analysis were used to analyze the clinical, CT and pathological data of the training set, and the independent predictors of colorectal cancer EMVI were screened to build a clinical model. Finally a combined model was established based on radiomics and clinical model. Receiver operating characteristic curve was drawn, and the area under the curve (AUC) was calculated to evaluate the efficacy of each model for predicting EMVI+ in colorectal cancer. Calibration curve and decision curve analysis were used to evaluate the calibration degree and clinical practicability of the models. Results Four best radiomics features were selected to construct the radiomics model. Carbohydrate antigen (CA) 19-9 and CA 72-4 were both independent predictors of EMVI+ for colorectal cancer (OR=1.033, 1.285, both P<0.05). The AUC of combined model (AUC=0.908) for predicting EMVI+ of colorectal cancer in training set was higher than that of radiomics and clinical models (AUC=0.825, 0.770, P=0.017, 0.003). In test set, the AUC of radiomics, clinical and combined models was 0.751, 0.632 and 0.799, respectively, not being statistical different between each pair (all P>0.05). The radiomics model and combined model both had good calibration degree. Taken >0.1 in training set and >0.12 in test set as the thresholds, the clinical net benefit of combined model was higher. Conclusion Enhanced CT radiomics combined with clinical indicators could effectively predict EMVI+ of colorectal cancer. |
| 查看全文 查看/发表评论 下载PDF阅读器 |
|
|
|
