Development and External Validation of Radiomics Approach for Nuclear Grading in Clear Cell Renal Cell Carcinoma.
Zhou, H
Mao, H
Dong, D
Fang, M
Gu, D
Liu, X
Xu, M
Yang, S
Zou, J
Yin, R
Zheng, H
Tian, J
Pan, C
Fang, X
Yang, S
Zou, J
Yin, R
Zheng, H
Tian, J
Pan, C
Fang, X
- Publisher:
- Springer Science and Business Media LLC
- Publication Type:
- Journal Article
- Citation:
- Annals of surgical oncology, 2020, 27, (10), pp. 4057-4065
- Issue Date:
- 2020-10
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Zhou2020_Article_DevelopmentAndExternalValidati.pdf | 1.31 MB |
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Background and purpose
Nuclear grades of clear cell renal cell carcinoma (ccRCC) are usually confirmed by invasive methods. Radiomics is a quantitative tool that uses non-invasive medical imaging for tumor diagnosis and prognosis. In this study, a radiomics approach was proposed to analyze the association between preoperative computed tomography (CT) images and nuclear grades of ccRCC.Methods
Our dataset included 320 ccRCC patients from two centers and was divided into a training set (n = 124), an internal test set (n = 123), and an external test set (n = 73). A radiomic feature set was extracted from unenhanced, corticomedullary phase, and nephrographic phase CT images. The maximizing independent classification information criteria function and recursive feature elimination with cross-validation were used to select effective features. Random forests were used to build a final model for predicting nuclear grades, and area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of radiomic features and models.Results
The radiomic features from the three CT phases could effectively distinguished the four nuclear grades. A combined model, merging radiomic features and clinical characteristics, obtained good predictive performances in the internal test set (AUC 0.77, 0.75, 0.79, and 0.85 for the four grades, respectively), and performance was further confirmed in the external test set, with AUCs of 0.75, 0.68, and 0.73 (no fourth-level data).Conclusion
The combination of CT radiomic features and clinical characteristics could discriminate the nuclear grades in ccRCC, which may help in assisting treatment decision making.Please use this identifier to cite or link to this item: