CT 放射组学在肾癌合并肿瘤血栓患者静脉壁侵犯诊断中的应用。

The Application of CT Radiomics in the Diagnosis of Vein Wall Invasion in Patients With Renal Cell Carcinoma Combined With Tumor Thrombus.

机构信息

Department of Urology, Peking University Third Hospital, Haidian District, Beijing, People's Republic of China.

School of Engineering Medicine, Beihang University, Beijing, People's Republic of China.

出版信息

Oncologist. 2024 Feb 2;29(2):151-158. doi: 10.1093/oncolo/oyad243.

DOI:10.1093/oncolo/oyad243

PMID:37672362

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10836321/

Abstract

OBJECTIVE

The objective of this study was to explore the application of radiomics combined with machine learning to establish different models to assist in the diagnosis of venous wall invasion in patients with renal cell carcinoma and venous tumor thrombus and to evaluate the diagnostic efficacy.

MATERIALS AND METHODS

We retrospectively reviewed the data of 169 patients in Peking University Third Hospital from March 2015 to January 21, who was diagnosed as renal mass with venous invasion. According to the intraoperative findings, 111 patients were classified to the venous wall invasion group and 58 cases in the non-invasion group. ITK-snap was used for tumor segmentation and PyRadiomics 3.0.1 package was used for feature extraction. A total of 1598 features could be extracted from each CT image. The patients were divided into training set and testing set by time. The elastic-net regression with 4-fold cross-validation was used as a dimension-reduction method. After feature selection, a support vector machines (SVM) model, a logistic regression (LR) model, and an extra trees (ET) model were established. Then the sensitivity, specificity, accuracy, and the area under the curve (AUC) were calculated to evaluate the diagnostic performance of each model on the testing set.

RESULTS

Patients before September 2019 were divided into the training set, of which 88 patients were in the invasion group and 42 patients were in the non-invasion group. The others were in the testing set, of which 32 patients were in the invasion group and 16 patients were in the non-invasion group. A total of 34 radiomics features were obtained by the elastic-net regression. The SVM model had an AUC value of 0.641 (95% CI, 0.463-0.769), a sensitivity of 1.000, and a specificity of 0.062. The LR model had an AUC value of 0.769 (95% CI, 0.620-0.877), a sensitivity of 0.913, and a specificity of 0.312. The ET model had an AUC value of 0.853 (95% CI, 0.734-0.948), a sensitivity of 0.783, and a specificity of 0.812. Among the 3 models, the ET model had the best diagnostic effect, with a good balance of sensitivity and specificity. And the higher the tumor thrombus grade, the better the diagnostic efficacy of the ET model. In inferior vena cava tumor thrombus, the sensitivity, specificity, accuracy, and AUC of ET model can be improved to 0.889, 0.800, 0.857, 0.878 (95% CI, 0.745-1.000).

CONCLUSION

Machine learning combined with radiomics method can effectively identify whether venous wall was invaded by tumor thrombus and has high diagnostic efficacy with an AUC of 0.853 (95% CI, 0.734-0.948).

摘要

目的

本研究旨在探讨基于放射组学和机器学习的方法建立不同模型以辅助诊断肾癌伴静脉瘤栓患者静脉壁侵犯，并评估其诊断效能。

材料与方法

回顾性分析 2015 年 3 月至 2021 年 1 月北京大学第三医院收治的 169 例术前诊断为肾占位伴静脉侵犯的患者资料，根据术中所见，将 111 例患者分为静脉壁侵犯组，58 例患者分为非侵犯组。采用 ITK-snap 软件进行肿瘤勾画，使用 PyRadiomics 3.0.1 包提取特征。每个 CT 图像可提取 1598 个特征。采用时间分割法将患者分为训练集和测试集，使用弹性网络回归（elastic-net regression）进行降维，经过特征选择后，建立支持向量机（SVM）模型、逻辑回归（LR）模型和极端随机树（ET）模型。在测试集上计算各模型的敏感度、特异度、准确率和曲线下面积（area under the curve，AUC），以评估各模型的诊断性能。

结果

2019 年 9 月前的患者被分为训练集，其中侵犯组 88 例，非侵犯组 42 例；其余患者为测试集，侵犯组 32 例，非侵犯组 16 例。弹性网络回归得到 34 个放射组学特征。SVM 模型的 AUC 值为 0.641（95%CI：0.463-0.769），敏感度为 1.000，特异度为 0.062。LR 模型的 AUC 值为 0.769（95%CI：0.620-0.877），敏感度为 0.913，特异度为 0.312。ET 模型的 AUC 值为 0.853（95%CI：0.734-0.948），敏感度为 0.783，特异度为 0.812。在这 3 个模型中，ET 模型的诊断效果最好，具有较好的敏感度和特异度平衡。肿瘤栓子分级越高，ET 模型的诊断效能越好。在肾静脉下段瘤栓中，ET 模型的敏感度、特异度、准确率和 AUC 可提高至 0.889、0.800、0.857、0.878（95%CI：0.745-1.000）。