Abdominal Radiology
The value of quantitative multiple-phase CT radiomic features analysis in differentiation of clear cell renal cell carcinoma from fat-poor angiomyolipoma
Xiangling Zeng, Jialiang Wu, Lei Sun, Jiawei Chen, Shengsheng Lai, Xin Zhen, Xinhua Wei, Xinqing Jiang, Ruimeng Yang
Published 2019-05-10
Cite as Chin J Radiol, 2019, 53(5): 364-369. DOI: 10.3760/cma.j.issn.1005-1201.2019.05.007
Abstract
ObjectiveTo explore the CT dominant phase and optimal classification model in differenting clear cell renal cell carcinoma (ccRCC) from fat-poor angiomyolipoma (fpAML) through quantitative multiple-phase CT radiomic features analysis.
MethodsClinical and imaging data of 195 cases pathologically confirmed ccRCC (n=131) and fpAML (n=64) were retrospectively studied. All the patients underwent non-contrast enhanced CT scans and dynamic multi-phase (corticomedullary phase, medullary phase and excretion phase) contrast-enhanced CT scans. Regions of interest (ROIs) were manually delineated based on the selected image slices with the maximal diameter of the lesion using ITK-SNAP software, followed by the acquisition of candidate CT radiomic feature sets from each phase with statistically significant differences by using Mann-Whitney U test. Then, using the synthetic minority oversampling technique (SMOTE), 232 classification models which are composed of 29 different feature selection algorithms (top 10 features were chosen by the backward elimination method) and 8 different classifiers were constructed. Employing the 5-fold cross-validation method, the performance of each classification models for each phase was evaluated using accuracy (ACC), sensitivity (SEN), specificity (SPE) and area under receiver operating characteristic curve (AUC), to acquire dominant CT phases and the optimal classification models for distingushing ccRCC and fpAML, along with the key imaging radiomic features.
ResultsIn this study, the mean maximal diameter of ccRCC and fpAML lesions were (3.9±1.4) cm, and (3.5±1.7) cm, respectively, and there was no statistically significant difference in the size of the tumor between two groups (P>0.05). From 102 initial imaging feature sets, the total number of candidate imaging feature sets (P<0.05) were: non-enhanced phase (n=26), corticomedullary phase (n=71), medullary phase (n=68), excretion phase (n=62). Among the 232 classification models through different combination of classifiers and feature selectors, the amount of classification models which achieved the maximum of AUC value (AUCmax) from different CT phases were: non-enhanced phase (n=106, 45.7%), corticomedullary phase (n=94, 40.5%), medullary phase (n=23, 9.9%), excretion phase (n=9, 3.9%). Imaging features from non-enhanced phase and corticomedullary phase yielded higher performance compared with medullary phase and excretion phase, with the corresponding optimal prediction models were SVM-fisher_score (AUC: 0.897, ACC: 83%, SEN: 84%, SPE:80%) and Logistic Regression-RFS (AUC: 0.891, ACC: 83%, SEN: 81%, SPE:89%), respectively.
ConclusionsThe quantitative imaging features from non-enhanced and corticomedullary phase have better performance among proposed classification models than that from medullary phase and excretion phase. Furthermore, it is feasible to acquire proper combination of feature selection and classifiers to achieve high performance in identifying ccRCC and fpAML.
Key words:
Kidney neoplasms; Tomography,X-ray computed; Radiomics
Contributor Information
Xiangling Zeng
Department of Radiology, Guangzhou First People′s Hospital, Guangzhou Medical University, Guangzhou 510180, China
Jialiang Wu
Department of Radiology, Guangzhou First People′s Hospital, Guangzhou Medical University, Guangzhou 510180, China
Lei Sun
Department of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
Jiawei Chen
Department of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
Shengsheng Lai
Department of Medical Instrument, Guangdong Food and Drug Vocational College, Guangzhou 510520, China
Xin Zhen
Department of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
Xinhua Wei
Department of Radiology, Guangzhou First People′s Hospital, South China University of Technology, Affiliated Second Hospital, Guangzhou 510180, China
Xinqing Jiang
Department of Radiology, Guangzhou First People′s Hospital, South China University of Technology, Affiliated Second Hospital, Guangzhou 510180, China
Ruimeng Yang
Department of Radiology, Guangzhou First People′s Hospital, South China University of Technology, Affiliated Second Hospital, Guangzhou 510180, China