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European Radiology
Published in: European Radiology 4/2018

01-04-2018 | Urogenital

Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma

Authors: Zhichao Feng, Pengfei Rong, Peng Cao, Qingyu Zhou, Wenwei Zhu, Zhimin Yan, Qianyun Liu, Wei Wang

Published in: European Radiology | Issue 4/2018

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Abstract

Objective

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).

Methods

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.

Results

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.

Conclusion

Machine learning analysis of CT texture features can facilitate the accurate differentiation of small AMLwvf from RCC.

Key Points

• 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.
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Metadata
Title
Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma
Authors
Zhichao Feng
Pengfei Rong
Peng Cao
Qingyu Zhou
Wenwei Zhu
Zhimin Yan
Qianyun Liu
Wei Wang
Publication date
01-04-2018
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 4/2018
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-017-5118-z

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