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A CT-based radiomics nomogram for differentiation of renal angiomyolipoma without visible fat from homogeneous clear cell renal cell carcinoma

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To develop and validate a radiomics nomogram for preoperative differentiating renal angiomyolipoma without visible fat (AML.wovf) from homogeneous clear cell renal cell carcinoma (hm-ccRCC).

Methods

Ninety-nine patients with AML.wovf (n = 36) and hm-ccRCC (n = 63) were divided into a training set (n = 80) and a validation set (n = 19). Radiomics features were extracted from corticomedullary phase and nephrographic phase CT images. A radiomics signature was constructed and a radiomics score (Rad-score) was calculated. Demographics and CT findings were assessed to build a clinical factors model. Combined with the Rad-score and independent clinical factors, a radiomics nomogram was constructed. Nomogram performance was assessed with respect to calibration, discrimination, and clinical usefulness.

Results

Fourteen features were used to build the radiomics signature. The radiomics signature showed good discrimination in the training set (AUC [area under the curve], 0.879; 95%; confidence interval [CI], 0.793–0.966) and the validation set (AUC, 0.846; 95% CI, 0.643–1.000). The radiomics nomogram showed good calibration and discrimination in the training set (AUC, 0.896; 95% CI, 0.810–0.983) and the validation set (AUC, 0.949; 95% CI, 0.856–1.000) and showed better discrimination capability (p < 0.05) compared with the clinical factor model (AUC, 0.788; 95% CI, 0.683–0.893) in the training set. Decision curve analysis demonstrated the nomogram outperformed the clinical factors model and radiomics signature in terms of clinical usefulness.

Conclusions

The CT-based radiomics nomogram, a noninvasive preoperative prediction tool that incorporates the Rad-score and clinical factors, shows favorable predictive efficacy for differentiating AML.wovf from hm-ccRCC, which might assist clinicians in tailoring precise therapy.

Key Points

• Differential diagnosis between AML.wovf and hm-ccRCC is rather difficult by conventional imaging modalities.

• A radiomics nomogram integrated with the radiomics signature, demographics, and CT findings facilitates differentiation of AML.wovf from hm-ccRCC with improved diagnostic efficacy.

• The CT-based radiomics nomogram might spare unnecessary surgery for AML.wovf.

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Abbreviations

3-D:

Three-dimensional

AML:

Angiomyolipoma

AML.wovf:

AML without visible fat

ANOVA:

Analysis of variance

AUC:

Area under the curve

BMI:

Body mass index

ccRCC:

Clear cell renal cell carcinoma

CI:

Confidence interval

CMP:

Corticomedullary phase

DCA:

Decision curve analysis

EP:

Excretory phase

GLCM:

Gray level co-occurrence matrix

GLRLM:

Gray level run length matrix

GLSZM:

Gray level size zone matrix

Hm-ccRCC:

Homogeneous ccRCC

ICC:

Inter-/intra- class correlation coefficient

LASSO:

Least absolute shrinkage and selection operator

Nomo-score:

Nomogram score

NP:

Nephrographic phase

OR:

Odds ratio

PACS:

Picture archiving and communication system

PEC:

Perivascular epithelioid cell

Rad-score:

Radiomics score

ROC:

Receiver operator characteristic

ROI:

Region of interest

SVM:

Support vector machine

References

  1. Lim RS, Flood TA, McInnes MDF, Lavallee LT, Schieda N (2018) Renal angiomyolipoma without visible fat: can we make the diagnosis using CT and MRI? Eur Radiol 28:542–553

    Article  Google Scholar 

  2. Farrell C, Noyes SL, Tourojman M, Lane BR (2015) Renal angiomyolipoma: preoperative identification of atypical fat-poor AML. Curr Urol Rep 16:12

    Article  Google Scholar 

  3. Schieda N, Kielar AZ, Al Dandan O, McInnes MD, Flood TA (2015) Ten uncommon and unusual variants of renal angiomyolipoma (AML): radiologic-pathologic correlation. Clin Radiol 70:206–220

    Article  CAS  Google Scholar 

  4. Vos N, Oyen R (2018) Renal angiomyolipoma: the good, the bad, and the ugly. J Belg Soc Radiol 102:41

    Article  Google Scholar 

  5. Thiravit S, Teerasamit W, Thiravit P (2018) The different faces of renal angiomyolipomas on radiologic imaging: a pictorial review. Br J Radiol 91:20170533

    Article  Google Scholar 

  6. Flum AS, Hamoui N, Said MA et al (2016) Update on the diagnosis and management of renal angiomyolipoma. J Urol 195:834–846

    Article  Google Scholar 

  7. Hakim SW, Schieda N, Hodgdon T, McInnes MD, Dilauro M, Flood TA (2016) Angiomyolipoma (AML) without visible fat: ultrasound, CT and MR imaging features with pathological correlation. Eur Radiol 26:592–600

    Article  Google Scholar 

  8. Li ZC, Zhai G, Zhang J et al (2018) Differentiation of clear cell and non-clear cell renal cell carcinomas by all-relevant radiomics features from multiphase CT: a VHL mutation perspective. Eur Radiol. https://doi.org/10.1007/s00330-018-5872-6

  9. Shinagare AB, Krajewski KM, Braschi-Amirfarzan M, Ramaiya NH (2017) Advanced renal cell carcinoma: role of the radiologist in the era of precision medicine. Radiology 284:333–351

    Article  Google Scholar 

  10. Park BK (2017) Renal angiomyolipoma: radiologic classification and imaging features according to the amount of fat. AJR Am J Roentgenol 209:826–835

    Article  Google Scholar 

  11. 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  Google Scholar 

  12. Lu Q, Li CX, Huang BJ, Xue LY, Wang WP (2015) Triphasic and epithelioid minimal fat renal angiomyolipoma and clear cell renal cell carcinoma: qualitative and quantitative CEUS characteristics and distinguishing features. Abdom Imaging 40:333–342

    Article  Google Scholar 

  13. Lu Q, Wang W, Huang B, Li C, Li C (2012) Minimal fat renal angiomyolipoma: the initial study with contrast-enhanced ultrasonography. Ultrasound Med Biol 38:1896–1901

    Article  Google Scholar 

  14. Chen LS, Zhu ZQ, Wang ZT et al (2018) Chemical shift magnetic resonance imaging for distinguishing minimal-fat renal angiomyolipoma from renal cell carcinoma: a meta-analysis. Eur Radiol 28:1854–1861

    Article  Google Scholar 

  15. Schieda N, Hodgdon T, El-Khodary M, Flood TA, McInnes MD (2014) Unenhanced CT for the diagnosis of minimal-fat renal angiomyolipoma. AJR Am J Roentgenol 203:1236–1241

    Article  Google Scholar 

  16. Chaudhry HS, Davenport MS, Nieman CM, Ho LM, Neville AM (2012) Histogram analysis of small solid renal masses: differentiating minimal fat angiomyolipoma from renal cell carcinoma. AJR Am J Roentgenol 198:377–383

    Article  Google Scholar 

  17. Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577

    Article  Google Scholar 

  18. Shu J, Tang Y, Cui J et al (2018) Clear cell renal cell carcinoma: CT-based radiomics features for the prediction of Fuhrman grade. Eur J Radiol 109:8–12

    Article  Google Scholar 

  19. Ding J, Xing Z, Jiang Z et al (2018) CT-based radiomic model predicts high grade of clear cell renal cell carcinoma. Eur J Radiol 103:51–56

    Article  Google Scholar 

  20. Kocak B, Yardimci AH, Bektas CT et al (2018) Textural differences between renal cell carcinoma subtypes: machine learning-based quantitative computed tomography texture analysis with independent external validation. Eur J Radiol 107:149–157

    Article  Google Scholar 

  21. Varghese BA, Chen F, Hwang DH et al (2018) Differentiation of predominantly solid enhancing lipid-poor renal cell masses by use of contrast-enhanced CT: evaluating the role of texture in tumour subtyping. AJR Am J Roentgenol 211:W288–W296

    Article  Google Scholar 

  22. Kunapuli G, Varghese BA, Ganapathy P et al (2018) A decision-support tool for renal mass classification. J Digit Imaging 31:929–939

    Article  Google Scholar 

  23. Feng Z, Rong P, Cao P et al (2018) 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

    Article  Google Scholar 

  24. 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  Google Scholar 

  25. Haider MA, Vosough A, Khalvati F, Kiss A, Ganeshan B, Bjarnason GA (2017) CT texture analysis: a potential tool for prediction of survival in patients with metastatic clear cell carcinoma treated with sunitinib. Cancer Imaging 17:4

    Article  Google Scholar 

  26. Collins GS, Reitsma JB, Altman DG, Moons KG (2015) Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ 350:g7594

    Article  Google Scholar 

  27. 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  Google Scholar 

  28. Hodgdon T, McInnes MD, Schieda N, Flood TA, Lamb L, Thornhill RE (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  Google Scholar 

  29. Lee HS, Hong H, Jung DC, Park S, Kim J (2017) Differentiation of fat-poor angiomyolipoma from clear cell renal cell carcinoma in contrast-enhanced MDCT images using quantitative feature classification. Med Phys 44:3604–3014

    Article  CAS  Google Scholar 

  30. Ng F, Kozarski R, Ganeshan B, Goh V (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  Google Scholar 

Download references

Funding

This study has received funding by the National Natural Science Foundation of China (81701688 and 81601527); the Natural Science Foundation of Shandong Province (ZR2017BH096 and ZR2017MH036); the Key Research and Development Project of Shandong Province (2018GSF118078); and the Postdoctoral Science Foundation of China (2018M642617). None of these funding sources had any role in study design, the collection, analysis and interpretation of data, the writing of the report, or the decision to submit the paper for publication.

Author information

Author notes

  1. Pei Nie and Guangjie Yang contributed equally to this work.

Authors and Affiliations

  1. Radiology Department, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China

    Pei Nie & Dapeng Hao

  2. PET-CT Center, The Affiliated Hospital of Qingdao University, No. 59, Haier Road, Qingdao, 266061, Shandong, China

    Guangjie Yang, Zhenguang Wang, Lei Yan, Wenjie Miao, Yujun Zhao & Aidi Gong

  3. Pathology Department, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China

    Jie Wu

  4. Huiying Medical Technology Co., Ltd, Beijing, China

    Jingjing Cui & Yan Jia

  5. Urology Department, The Affiliated Hospital of Qingdao University, No. 16, Jiangsu Road, Qingdao, 266005, Shandong, China

    Haitao Niu

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  1. Pei Nie
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Corresponding authors

Correspondence to Zhenguang Wang or Haitao Niu.

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Guarantor

The scientific guarantor of this publication is Zhenguang Wang.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors (Guangjie Yang) has significant statistical expertise.

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Written informed consent was waived by the Institutional Review Board.

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Institutional Review Board approval was obtained.

Methodology

• retrospective

• case-control study

• performed at one institution

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Nie, P., Yang, G., Wang, Z. et al. A CT-based radiomics nomogram for differentiation of renal angiomyolipoma without visible fat from homogeneous clear cell renal cell carcinoma. Eur Radiol 30, 1274–1284 (2020). https://doi.org/10.1007/s00330-019-06427-x

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  • DOI: https://doi.org/10.1007/s00330-019-06427-x

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