Affiliation:
1. Department of Urology, Peking University Third Hospital , Haidian District, Beijing , People’s Republic of China
2. School of Engineering Medicine, Beihang University , Beijing , People’s Republic of China
3. Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of the People’s Republic of China , Beijing , People’s Republic of China
4. Beijing Engineering Research Center of Cardiovascular Wisdom Diagnosis and Treatment , Beijing , People’s Republic of China
Abstract
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).
Funder
National Natural Science Foundation of China
Publisher
Oxford University Press (OUP)
Reference26 articles.
1. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries;Bray,2018
2. The epidemiology and risk factors for renal cancer;Qayyum,2013
3. The Mayo Clinic experience with surgical management, complications and outcome for patients with renal cell carcinoma and venous tumour thrombus;Blute,2004
4. A systematic review and meta-analysis of clinicopathologic factors linked to oncologic outcomes for renal cell carcinoma with tumor thrombus treated by radical nephrectomy with thrombectomy;Gu,2018
5. The surgical management and prognosis of renal cell cancer with IVC tumor thrombus: 15-years of experience using a multi-specialty approach at a single UK referral center;Ali,2013