Differentiation of renal angiomyolipoma without visible fat from renal cell carcinoma by machine learning based on whole-tumor computed tomography texture features

Abstract

Background

Morphological findings showed poor accuracy in differentiating angiomyolipoma without visible fat (AMLwvf) from renal cell carcinoma (RCC).

Purpose

To determine the performance of a machine learning classifier in differentiating AMLwvf from different subtypes of RCC based on whole-tumor slices of CT images.

Material and Methods

In this retrospective study, 171 pathologically proven renal masses were collected from a single institution. Texture features were extracted from whole-tumor images in three phases including the pre-contrast (PCP), corticomedullary (CMP), and nephrographic (NP) phases. A support vector machine with the recursive feature elimination method based on fivefold cross-validation (SVM-RFECV) with the synthetic minority oversampling technique (SMOTE) was utilized to establish classifiers for differentiating AMLwvf from all subtypes of RCC (all-RCC), clear cell RCC (ccRCC), and non-ccRCC. The performances of the classifiers based on three-phase and single-phase images were compared with each other and morphological interpretations.

Results

A machine learning classifier achieved the best performance in differentiating AMLwvf from all-RCC, ccRCC, and non-ccRCC. The performance of the best machine learning classifier for differentiating AMLwvf from all-RCC (area under the curve [AUC] = 0.96) and ccRCC (AUC = 0.97) was higher than that for differentiating AMLwvf from non-ccRCC (AUC = 0.89); morphological interpretations achieved lower performance for differentiating AMLwvf from all-RCC (AUC = 0.67), ccRCC (AUC = 0.68), and non-ccRCC (AUC = 0.64).

Conclusion

Machine learning can be a useful non-invasive technique for differentiating AMLwvf from all-RCC, ccRCC, and non-ccRCC, and it can be more accurate than morphological interpretation by radiologists.

Keywords

Renal cell carcinoma angiomyolipoma computed tomography (CT)machine learning

Get full access to this article

View all access options for this article.

References

Hakim

Schieda

Hodgdon

et al. Angiomyolipoma (AML) without visible fat: Ultrasound, CT and MR imaging features with pathological correlation. Eur Radiol 2016; 26:592–600.

Jinzaki

Tanimoto

Narimatsu

et al. Angiomyolipoma: imaging findings in lesions with minimal fat. Radiology 1997; 205:497–502.

Fujii

Komai

Saito

et al. Incidence of benign pathologic lesions at partial nephrectomy for presumed RCC renal masses: Japanese dual-center experience with 176 consecutive patients. Urology 2008; 72:598–602.

Kutikov

Fossett

Ramchandani

et al. Incidence of benign pathologic findings at partial nephrectomy for solitary renal mass presumed to be renal cell carcinoma on preoperative imaging. Urology 2006; 68:737–740.

Zhang

Luo

Liu

et al. Angiomyolipoma with minimal fat: differentiation from papillary renal cell carcinoma by helical CT. Clin Radiol 2013; 68:365–370.

Kim

Park

Shon

et al. Angiomyolipoma with minimal fat: differentiation from renal cell carcinoma at biphasic helical CT. Radiology 2004; 230:677–684.

Kim

Han

et al. Morphologic analysis with computed tomography may help differentiate fat-poor angiomyolipoma from renal cell carcinoma: a retrospective study with 602 patients. Abdom Radiol (NY) 2018; 43:647–654.

Yang

Shen

Chang

et al. Are there useful CT features to differentiate renal cell carcinoma from lipid-poor renal angiomyolipoma? Am J Roentgenol 2013; 201:1017–1028.

Xie

Yang

Yuan

. Lipid-poor renal angiomyolipoma: Differentiation from clear cell renal cell carcinoma using wash-in and washout characteristics on contrast-enhanced computed tomography. Oncol Lett 2016; 11:2327–2331.

10.

Leng

Takahashi

Gomez Cardona

et al. Subjective and objective heterogeneity scores for differentiating small renal masses using contrast-enhanced CT. Abdom Radiol (NY) 2017; 42:1485–1492.

11.

Raman

Chen

Schroeder

et al. CT texture analysis of renal masses: pilot study using random forest classification for prediction of pathology. Acad Radiol 2014; 21:1587–1596.

12.

Schieda

Thornhill

Al-Subhi

et al. Diagnosis of sarcomatoid renal cell carcinoma with CT: evaluation by qualitative imaging features and texture analysis. Am J Roentgenol 2015; 204:1013–1023.

13.

Scalera

Khalid

et al. Texture analysis as a radiomic marker for differentiating renal tumors. Abdom Radiol (NY) 2017; 42:2470–2478.

14.

Hodgdon

McInnes

Schieda

et al. Can quantitative CT texture analysis be used to differentiate fat-poor renal angiomyolipoma from renal cell carcinoma on unenhanced CT Images? Radiology 2015; 276:787–796.

15.

Yan

Liu

Wang

et al. Angiomyolipoma with minimal fat: differentiation from clear cell renal cell carcinoma and papillary renal cell carcinoma by texture analysis on CT images. Acad Radiol 2015; 22:1115–1121.

16.

Feng

Rong

Cao

et al. Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma. Eur Radiol 2018; 28:1625–1633.

17.

Kozarski

Ganeshan

et al. Assessment of tumor heterogeneity by CT texture analysis: can the largest cross-sectional area be used as an alternative to whole tumor analysis? Eur J Radiol 2013; 82:342–348.

18.

Ortiz-Ramon

Larroza

Arana

et al. A radiomics evaluation of 2D and 3D MRI texture features to classify brain metastases from lung cancer and melanoma. Conf Proc IEEE Eng Med Biol Soc 2017; 2017:493–496.

19.

Yushkevich

Piven

Hazlett

et al. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 2006; 31:1116–1128.

20.

Zwanenburg

Leger

Vallières

et al. Image biomarker standardisation initiative-feature definitions. arXiv preprint arXiv 2016;161207003.

21.

van Griethuysen

JJM

Fedorov

Parmar

et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res 2017; 77:e104–e107.

22.

Chawla

Bowyer

Hall

et al. SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research 2002; 16:321–357.

23.

Zhang

Oikonomou

Wong

et al. Radiomics-based prognosis analysis for non-small cell lung cancer. Sci Rep 2017; 7:46349.

24.

Kursa

Rudnicki

. Feature selection with the Boruta package. J Stat Softw 2010; 36:1–13.

25.

Guyon

Weston

Barnhill

et al. Gene selection for cancer classification using support vector machines. Machine Learning 2002; 46:389–422.

26.

Jeong

Park

et al. Unenhanced CT and MRI parameters that can be used to reliably predict fat-invisible angiomyolipoma. Am J Roentgenol 2016; 206:340–347.

27.

Takahashi

Leng

Kitajima

et al. Small (<4 cm) renal masses: differentiation of angiomyolipoma without visible fat from renal cell carcinoma using unenhanced and contrast-enhanced CT. Am J Roentgenol 2015; 205:1194–1202.

28.

Jinzaki

Silverman

Akita

et al. Renal angiomyolipoma: a radiological classification and update on recent developments in diagnosis and management. Abdom Imaging 2014; 39:588–604.

29.

Lee

Hong

Jung

et al. Differentiation of fat-poor angiomyolipoma from clear cell renal cell carcinoma in contrast-enhanced MDCT images using quantitative feature classification. Med Phys 2017; 44:3604–3614.

30.

Beresova

Larroza

Arana

et al. 2D and 3D texture analysis to differentiate brain metastases on MR images: proceed with caution. MAGMA 2018; 31:285–294.

31.

Lim

Flood

McInnes

MDF

et al. Renal angiomyolipoma without visible fat: Can we make the diagnosis using CT and MRI? Eur Radiol 2018; 28:542–553.

32.

Farrell

Noyes

Tourojman

et al. Renal angiomyolipoma: preoperative identification of atypical fat-poor AML. Curr Urol Rep 2015; 16:12.

33.

Kim

Yun

Jung

et al. Usefulness of the ice-cream cone pattern in computed tomography for prediction of angiomyolipoma in patients with a small renal mass. Korean J Urol 2013; 54:504–509.

34.

Lee-Felker

Felker

Tan

et al. Qualitative and quantitative MDCT features for differentiating clear cell renal cell carcinoma from other solid renal cortical masses. Am J Roentgenol 2014; 203:W516–W524.