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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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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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Abbreviations

ACC:

Accuracy

AMLwvf:

Angiomyolipoma without visible fat

AUC:

Area under the curve

CMP:

Corticomedullary phase

FOV:

Field of view

GLCM:

Grey-level co-occurrence matrix

ICC:

Interobserver agreement

NP:

Nephrographic phase

PACS:

Picture archiving and communication system

RBF:

Radial basis function

RCC:

Renal cell carcinoma

RFE:

Recursive feature elimination

ROC:

Receiver operating characteristic

ROI:

Region of interest

SMOTE:

Synthetic minority oversampling technique

SRM:

Small renal mass

SVM:

Support vector machine

UP:

Unenhanced phase

References

  1. Kutikov A, Fossett LK, Ramchandani P et al (2006) Incidence of benign pathologic findings at partial nephrectomy for solitary renal mass presumed to be renal cell carcinoma on preoperative imaging. Urology 68:737–740

    Article  PubMed  Google Scholar 

  2. Fujii Y, Komai Y, Saito K et al (2008) Incidence of benign pathologic lesions at partial nephrectomy for presumed RCC renal masses: Japanese dual-center experience with 176 consecutive patients. Urology 72:598–602

    Article  PubMed  Google Scholar 

  3. Flum AS, Hamoui N, Said MA et al (2016) Update on the Diagnosis and Management of Renal Angiomyolipoma. J Urol 195:834–846

    Article  PubMed  Google Scholar 

  4. Umbreit EC, Shimko MS, Childs MA et al (2012) Metastatic potential of a renal mass according to original tumour size at presentation. Bju Int 109:190–194 discussion 194

    Article  PubMed  Google Scholar 

  5. Kim JK, Park SY, Shon JH et al (2004) Angiomyolipoma with minimal fat: differentiation from renal cell carcinoma at biphasic helical CT. Radiology 230:677–684

    Article  PubMed  Google Scholar 

  6. Zhang YY, Luo S, Liu Y et al (2013) Angiomyolipoma with minimal fat: differentiation from papillary renal cell carcinoma by helical CT. Clin Radiol 68:365–370

    Article  PubMed  Google Scholar 

  7. Hakim SW, Schieda N, Hodgdon T et al (2016) Angiomyolipoma (AML) without visible fat: Ultrasound, CT and MR imaging features with pathological correlation. Eur Radiol 26:592–600

    Article  PubMed  Google Scholar 

  8. Yang CW, Shen SH, Chang YH et al (2013) Are there useful CT features to differentiate renal cell carcinoma from lipid-poor renal angiomyolipoma? AJR Am J Roentgenol 201:1017–1028

    Article  PubMed  Google Scholar 

  9. Xie P, Yang Z, Yuan Z (2016) Lipid-poor renal angiomyolipoma: Differentiation from clear cell renal cell carcinoma using wash-in and washout characteristics on contrast-enhanced computed tomography. Oncol Lett 11:2327–2331

    Article  PubMed  PubMed Central  Google Scholar 

  10. Davnall F, Yip CS, Ljungqvist G et al (2012) Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights Imaging 3:573–589

    Article  PubMed  PubMed Central  Google Scholar 

  11. Hodgdon T, Mcinnes MD, Schieda N et al (2015) Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images? Radiology 276:787–796

    Article  PubMed  Google Scholar 

  12. Yan L, Liu Z, Wang G et al (2015) Angiomyolipoma with minimal fat: differentiation from clear cell renal cell carcinoma and papillary renal cell carcinoma by texture analysis on CT images. Acad Radiol 22:1115–1121

    Article  PubMed  Google Scholar 

  13. Kumar V, Gu Y, Basu S et al (2012) Radiomics: the process and the challenges. Magn Reson Imaging 30:1234–1248

    Article  PubMed  PubMed Central  Google Scholar 

  14. Parmar C, Grossmann P, Bussink J et al (2015) Machine Learning methods for Quantitative Radiomic Biomarkers. Sci Rep 5:13087

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Zhang X, Yan LF, Hu YC et al (2017) Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features. Oncotarget 8:47816–47830

    PubMed  PubMed Central  Google Scholar 

  16. Nketiah G, Elschot M, Kim E et al (2017) T2-weighted MRI-derived textural features reflect prostate cancer aggressiveness: preliminary results. Eur Radiol 27:3050–3059

    Article  PubMed  Google Scholar 

  17. Wibmer A, Hricak H, Gondo T et al (2015) Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores. Eur Radiol 25:2840–2850

    Article  PubMed  PubMed Central  Google Scholar 

  18. Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Guyon I, Weston J, Barnhill S et al (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46:389–422

    Article  Google Scholar 

  20. Fehr D, Veeraraghavan H, Wibmer A et al (2015) Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images. Proc Natl Acad Sci 112:E6265–E6273

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Wang J, Wu C J, Bao M L et al (2017) Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer. Eur Radiol. https:/doi.org/10.1007/s00330-017-4800-5

  22. Zhang X, Xu X, Tian Q, et al (2017) Radiomics assessment of bladder cancer grade using texture features from diffusion-weighted imaging. J Magn Reson Imaging​ 46: 1281-1288

  23. Chawla NV, Bowyer KW, Hall LO et al (2011) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321–357

    Google Scholar 

  24. Zhang Y, Oikonomou A, Wong A et al (2017) Radiomics-based Prognosis Analysis for Non-Small Cell Lung Cancer. Sci Rep 7:46349

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Yu H, Scalera J, Khalid M et al (2017) Texture analysis as a radiomic marker for differentiating renal tumors. Abdom Radiol (NY). https:/doi.org/10.1007/s00261-017-1144-1

  26. Lee HS, Hong H, Jung DC et al (2017) Differentiation of fat-poor angiomyolipoma from clear cell renal cell carcinoma in contrast-enhanced MDCT images using quantitative feature classification. Med Phys 44:3604–3614

    Article  CAS  PubMed  Google Scholar 

  27. Lin X, Yang F, Zhou L et al (2012) A support vector machine-recursive feature elimination feature selection method based on artificial contrast variables and mutual information. J Chromatogr B Analyt Technol Biomed Life Sci 910:149–155

    Article  CAS  PubMed  Google Scholar 

  28. Jinzaki M, Tanimoto A, Narimatsu Y et al (1997) Angiomyolipoma: imaging findings in lesions with minimal fat. Radiology 205:497–502

    Article  CAS  PubMed  Google Scholar 

  29. Tsukada J, Jinzaki M, Yao M et al (2013) Epithelioid angiomyolipoma of the kidney: radiological imaging. Int J Urol 20:1105–1111

    Article  PubMed  Google Scholar 

  30. Ishigami K, Pakalniskis MG, Leite LV et al (2015) Characterization of renal cell carcinoma, oncocytoma, and lipid-poor angiomyolipoma by unenhanced, nephrographic, and delayed phase contrast-enhanced computed tomography. Clin Imaging 39:76–84

    Article  PubMed  Google Scholar 

  31. Silverman SG, Mortele KJ, Tuncali K et al (2007) Hyperattenuating renal masses: etiologies, pathogenesis, and imaging evaluation. Radiographics 27:1131–1143

    Article  PubMed  Google Scholar 

  32. Liu S, Zheng H, Zhang Y et al (2017) Whole-volume apparent diffusion coefficient-based entropy parameters for assessment of gastric cancer aggressiveness. J Magn Reson Imaging. https:/doi.org/10.1002/jmri.25752

  33. Choi ER, Lee HY, Jeong JY et al (2016) Quantitative image variables reflect the intratumoral pathologic heterogeneity of lung adenocarcinoma. Oncotarget 7:67302–67313

    PubMed  PubMed Central  Google Scholar 

  34. Sidhu HS, Benigno S, Ganeshan B et al (2017) Textural analysis of multiparametric MRI detects transition zone prostate cancer. Eur Radiol 27:2348–2358

    Article  PubMed  Google Scholar 

  35. Hanahan D, Weinberg RA (2011) Hallmarks of cancer: the next generation. Cell 144:646–674

    Article  CAS  PubMed  Google Scholar 

  36. Johnson PT, Horton KM, Fishman EK (2010) How not to miss or mischaracterize a renal cell carcinoma: protocols, pearls, and pitfalls. AJR Am J Roentgenol 194:W307–W315

    Article  PubMed  Google Scholar 

  37. Takahashi N, Leng S, Kitajima K et al (2015) Small (< 4 cm) Renal Masses: Differentiation of Angiomyolipoma Without Visible Fat From Renal Cell Carcinoma Using Unenhanced and Contrast-Enhanced CT. AJR Am J Roentgenol 205:1194–1202

    Article  PubMed  Google Scholar 

  38. Xu X, Liu Y, Zhang X et al (2017) Preoperative prediction of muscular invasiveness of bladder cancer with radiomic features on conventional MRI and its high-order derivative maps. Abdom Radiol (NY) 42:1896–1905

    Article  Google Scholar 

  39. Yip SSF, Parmar C, Blezek D et al (2017) Application of the 3D slicer chest imaging platform segmentation algorithm for large lung nodule delineation. Plos One 12:e0178944

    Article  PubMed  PubMed Central  Google Scholar 

  40. Ng F, Kozarski R, Ganeshan B et al (2013) 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 82:342–348

    Article  PubMed  Google Scholar 

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Funding

The authors state that this work has not received any funding.

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Authors and Affiliations

  1. Department of Radiology, The Third Xiangya Hospital, Central South University, Changsha, Hunan, 410013, China

    Zhichao Feng, Pengfei Rong, Qingyu Zhou, Wenwei Zhu, Zhimin Yan, Qianyun Liu & Wei Wang

  2. GE Healthcare, Shanghai, 210000, China

    Peng Cao

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  1. Zhichao Feng
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  2. Pengfei Rong
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  3. Peng Cao
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  4. Qingyu Zhou
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  8. Wei Wang
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Corresponding author

Correspondence to Wei Wang.

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Guarantor

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.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

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Feng, Z., Rong, P., Cao, P. 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 28, 1625–1633 (2018). https://doi.org/10.1007/s00330-017-5118-z

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