Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma
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To evaluate the diagnostic performance of machine-learning based quantitative texture analysis of CT images to differentiate small (≤ 4 cm) angiomyolipoma without visible fat (AMLwvf) from renal cell carcinoma (RCC).
This single-institutional retrospective study included 58 patients with pathologically proven small renal mass (17 in AMLwvf and 41 in RCC groups). Texture features were extracted from the largest possible tumorous regions of interest (ROIs) by manual segmentation in preoperative three-phase CT images. Interobserver reliability and the Mann-Whitney U test were applied to select features preliminarily. Then support vector machine with recursive feature elimination (SVM-RFE) and synthetic minority oversampling technique (SMOTE) were adopted to establish discriminative classifiers, and the performance of classifiers was assessed.
Of the 42 extracted features, 16 candidate features showed significant intergroup differences (P < 0.05) and had good interobserver agreement. An optimal feature subset including 11 features was further selected by the SVM-RFE method. The SVM-RFE+SMOTE classifier achieved the best performance in discriminating between small AMLwvf and RCC, with the highest accuracy, sensitivity, specificity and AUC of 93.9 %, 87.8 %, 100 % and 0.955, respectively.
Machine learning analysis of CT texture features can facilitate the accurate differentiation of small AMLwvf from RCC.
• Although conventional CT is useful for diagnosis of SRMs, it has limitations.
• Machine-learning based CT texture analysis facilitate differentiation of small AMLwvf from RCC.
• The highest accuracy of SVM-RFE+SMOTE classifier reached 93.9 %.
• Texture analysis combined with machine-learning methods might spare unnecessary surgery for AMLwvf.
KeywordsAngiomyolipoma Renal cell carcinoma Computed tomography Texture analysis Machine learning
Angiomyolipoma without visible fat
Area under the curve
Field of view
Grey-level co-occurrence matrix
Picture archiving and communication system
Radial basis function
Renal cell carcinoma
Recursive feature elimination
Receiver operating characteristic
Region of interest
Synthetic minority oversampling technique
Small renal mass
Support vector machine
The authors state that this work has not received any funding.
Compliance with ethical standards
The scientific guarantor of this publication is Zhichao Feng, M.D.
Conflict of interest
The authors of this manuscript declare a relationship with the following company: GE Healthcare.
Peng Cao is a senior scientist for GE Healthcare (Shanghai, China) and provided the software and necessary training for this study. He has no intention to apply for a patent based on this paper or invent any product, and did not provide any financial support. GE Healthcare did not play any additional role in the study design, data collection and analysis, or preparation of the manuscript. There are no other author disclosures, and the other authors (Zhichao Feng, Pengfei Rong, Qingyu Zhou, Wenwei Zhu, Zhimin Yan, Qianyun Liu and Wei Wang) have no conflicts of interest.
Statistics and biometry
Pengfei Rong and Wei Wang kindly provided statistical advice for this manuscript.
Written informed consent was waived by the Institutional Review Board.
Institutional Review Board approval was obtained.
• diagnostic or prognostic study
• performed at one institution
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