Skip to main content

Advertisement

SpringerLink
Log in

Characterization of solid renal neoplasms using MRI-based quantitative radiomics features

  • Kidneys, Ureters, Bladder, Retroperitoneum
  • Published:
Abdominal Radiology Aims and scope Submit manuscript

Abstract

Purpose

To assess the diagnostic value of magnetic resonance imaging (MRI)-based radiomics features using machine learning (ML) models in characterizing solid renal neoplasms, in comparison/combination with qualitative radiologic evaluation.

Methods

Retrospective analysis of 125 patients (mean age 59 years, 67% males) with solid renal neoplasms that underwent MRI before surgery. Qualitative (signal and enhancement characteristics) and quantitative radiomics analyses (histogram and texture features) were performed on T2-weighted imaging (WI), T1-WI pre- and post-contrast, and DWI. Mann–Whitney U test and receiver-operating characteristic analysis were used in a training set (n = 88) to evaluate diagnostic performance of qualitative and radiomics features for differentiation of renal cell carcinomas (RCCs) from benign lesions, and characterization of RCC subtypes (clear cell RCC [ccRCC] and papillary RCC [pRCC]). Random forest ML models were developed for discrimination between tumor types on the training set, and validated on an independent set (n = 37).

Results

We assessed 104 RCCs (51 ccRCC, 29 pRCC, and 24 other subtypes) and 21 benign lesions in 125 patients. Significant qualitative and quantitative radiomics features (area under the curve [AUC] between 0.62 and 0.90) were included for ML analysis. Models with best diagnostic performance on validation sets showed AUC of 0.73 (confidence interval [CI] 0.5–0.96) for differentiating RCC from benign lesions (using combination of qualitative and radiomics features); AUC of 0.77 (CI 0.62–0.92) for diagnosing ccRCC (using radiomics features), and AUC of 0.74 (CI 0.53–0.95) for diagnosing pRCC (using qualitative features).

Conclusion

ML models incorporating MRI-based radiomics features and qualitative radiologic assessment can help characterize renal masses.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data availability

Data are available upon request to the corresponding author.

Code availability

Software code is available upon request to the corresponding author.

References

  1. Leibovich BC, Lohse CM, Crispen PL, Boorjian SA, Thompson RH, Blute ML, Cheville JC (2010) Histological subtype is an independent predictor of outcome for patients with renal cell carcinoma. J Urol 183 (4):1309-1315. https://doi.org/10.1016/j.juro.2009.12.035

  2. Reuter VE, Presti JC, Jr. (2000) Contemporary approach to the classification of renal epithelial tumors. Semin Oncol 27 (2):124-137

  3. Collins S, McKiernan J, Landman J (2006) Update on the epidemiology and biology of renal cortical neoplasms. J Endourol 20 (12):975-985. https://doi.org/10.1089/end.2006.20.975

  4. Hsieh JJ, Le V, Cao D, Cheng EH, Creighton CJ (2018) Genomic classifications of renal cell carcinoma: a critical step towards the future application of personalized kidney cancer care with pan-omics precision. J Pathol 244 (5):525-537. https://doi.org/10.1002/path.5022

  5. Lebret T, Poulain JE, Molinie V, Herve JM, Denoux Y, Guth A, Scherrer A, Botto H (2007) Percutaneous core biopsy for renal masses: indications, accuracy and results. J Urol 178 (4 Pt 1):1184-1188; discussion 1188. https://doi.org/10.1016/j.juro.2007.05.155

  6. Silverman SG, Gan YU, Mortele KJ, Tuncali K, Cibas ES (2006) Renal masses in the adult patient: the role of percutaneous biopsy. Radiology 240 (1):6-22. https://doi.org/10.1148/radiol.2401050061

  7. Sun MR, Ngo L, Genega EM, Atkins MB, Finn ME, Rofsky NM, Pedrosa I (2009) Renal cell carcinoma: dynamic contrast-enhanced MR imaging for differentiation of tumor subtypes–correlation with pathologic findings. Radiology 250 (3):793-802. https://doi.org/10.1148/radiol.2503080995

  8. Young JR, Coy H, Kim HJ, Douek M, Lo P, Pantuck AJ, Raman SS (2017) Performance of Relative Enhancement on Multiphasic MRI for the Differentiation of Clear Cell Renal Cell Carcinoma (RCC) From Papillary and Chromophobe RCC Subtypes and Oncocytoma. AJR Am J Roentgenol 208 (4):812-819. https://doi.org/10.2214/ajr.16.17152

  9. Durinck S, Stawiski EW, Pavia-Jimenez A, Modrusan Z, Kapur P, Jaiswal BS, Zhang N, Toffessi-Tcheuyap V, Nguyen TT, Pahuja KB, Chen YJ, Saleem S, Chaudhuri S, Heldens S, Jackson M, Pena-Llopis S, Guillory J, Toy K, Ha C, Harris CJ, Holloman E, Hill HM, Stinson J, Rivers CS, Janakiraman V, Wang W, Kinch LN, Grishin NV, Haverty PM, Chow B, Gehring JS, Reeder J, Pau G, Wu TD, Margulis V, Lotan Y, Sagalowsky A, Pedrosa I, de Sauvage FJ, Brugarolas J, Seshagiri S (2015) Spectrum of diverse genomic alterations define non-clear cell renal carcinoma subtypes. Nat Genet 47 (1):13-21. https://doi.org/10.1038/ng.3146

  10. Gerlinger M, Rowan AJ, Horswell S, Math M, Larkin J, Endesfelder D, Gronroos E, Martinez P, Matthews N, Stewart A, Tarpey P, Varela I, Phillimore B, Begum S, McDonald NQ, Butler A, Jones D, Raine K, Latimer C, Santos CR, Nohadani M, Eklund AC, Spencer-Dene B, Clark G, Pickering L, Stamp G, Gore M, Szallasi Z, Downward J, Futreal PA, Swanton C (2012) Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 366 (10):883-892. https://doi.org/10.1056/nejmoa1113205

  11. de Leon AD, Kapur P, Pedrosa I (2019) Radiomics in Kidney Cancer: MR Imaging. Magn Reson Imaging Clin N Am 27 (1):1-13. https://doi.org/10.1016/j.mric.2018.08.005

  12. Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: Images Are More than Pictures, They Are Data. Radiology 278 (2):563-577. https://doi.org/10.1148/radiol.2015151169

  13. Lewis S, Hectors S, Taouli B (2020) Radiomics of hepatocellular carcinoma. Abdom Radiol (NY). https://doi.org/10.1007/s00261-019-02378-5

  14. Yang F, Ford JC, Dogan N, Padgett KR, Breto AL, Abramowitz MC, Dal Pra A, Pollack A, Stoyanova R (2018) Magnetic resonance imaging (MRI)-based radiomics for prostate cancer radiotherapy. Transl Androl Urol 7 (3):445-458. https://doi.org/10.21037/tau.2018.06.05

  15. Just N (2014) Improving tumour heterogeneity MRI assessment with histograms. Br J Cancer 111 (12):2205-2213. https://doi.org/10.1038/bjc.2014.512

  16. Haralick R, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Transactions on systems, man, and cybernetics 11 (6):610-621

  17. Liu Z, Wang S, Dong D, Wei J, Fang C, Zhou X, Sun K, Li L, Li B, Wang M, Tian J (2019) The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges. Theranostics 9 (5):1303-1322. https://doi.org/10.7150/thno.30309

  18. Jordan MI, Mitchell TM (2015) Machine learning: Trends, perspectives, and prospects. Science 349 (6245):255-260. https://doi.org/10.1126/science.aaa8415

  19. Buchlak QD, Esmaili N, Leveque JC, Farrokhi F, Bennett C, Piccardi M, Sethi RK (2019) Machine learning applications to clinical decision support in neurosurgery: an artificial intelligence augmented systematic review. Neurosurg Rev. https://doi.org/10.1007/s10143-019-01163-8

  20. Deo RC (2015) Machine Learning in Medicine. Circulation 132 (20):1920-1930. https://doi.org/10.1161/circulationaha.115.001593

  21. Kruppa J, Liu Y, Biau G, Kohler M, Konig IR, Malley JD, Ziegler A (2014) Probability estimation with machine learning methods for dichotomous and multicategory outcome: theory. Biom J 56 (4):534-563. https://doi.org/10.1002/bimj.201300068

  22. Miotto R, Wang F, Wang S, Jiang X, Dudley JT (2018) Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform 19 (6):1236-1246. https://doi.org/10.1093/bib/bbx044

  23. Shinagare AB, Vikram R, Jaffe C, Akin O, Kirby J, Huang E, Freymann J, Sainani NI, Sadow CA, Bathala TK, Rubin DL, Oto A, Heller MT, Surabhi VR, Katabathina V, Silverman SG (2015) Radiogenomics of clear cell renal cell carcinoma: preliminary findings of The Cancer Genome Atlas-Renal Cell Carcinoma (TCGA-RCC) Imaging Research Group. Abdom Imaging 40 (6):1684-1692. https://doi.org/10.1007/s00261-015-0386-z

  24. Kay FU, Canvasser NE, Xi Y, Pinho DF, Costa DN, Diaz de Leon A, Khatri G, Leyendecker JR, Yokoo T, Lay AH, Kavoussi N, Koseoglu E, Cadeddu JA, Pedrosa I (2018) Diagnostic Performance and Interreader Agreement of a Standardized MR Imaging Approach in the Prediction of Small Renal Mass Histology. Radiology 287 (2):543-553. https://doi.org/10.1148/radiol.2018171557

  25. Hoang UN, Mojdeh Mirmomen S, Meirelles O, Yao J, Merino M, Metwalli A, Marston Linehan W, Malayeri AA (2018) Assessment of multiphasic contrast-enhanced MR textures in differentiating small renal mass subtypes. Abdom Radiol (NY). https://doi.org/10.1007/s00261-018-1625-x

  26. Mehta P, Wang CH, Day AGR, Richardson C, Bukov M, Fisher CK, Schwab DJ (2019) A high-bias, low-variance introduction to Machine Learning for physicists. Phys Rep 810:1-124. https://doi.org/10.1016/j.physrep.2019.03.001

  27. Hirsch MS, Signoretti S, Dal Cin P (2015) Adult Renal Cell Carcinoma: A Review of Established Entities from Morphology to Molecular Genetics. Surg Pathol Clin 8 (4):587-621. https://doi.org/10.1016/j.path.2015.09.003

  28. Goyal A, Razik A, Kandasamy D, Seth A, Das P, Ganeshan B, Sharma R (2019) Role of MR texture analysis in histological subtyping and grading of renal cell carcinoma: a preliminary study. Abdom Radiol (NY). https://doi.org/10.1007/s00261-019-02122-z

  29. Yu H, Scalera J, Khalid M, Touret AS, Bloch N, Li B, Qureshi MM, Soto JA, Anderson SW (2017) Texture analysis as a radiomic marker for differentiating renal tumors. Abdom Radiol (NY) 42 (10):2470-2478. https://doi.org/10.1007/s00261-017-1144-1

  30. Bonekamp D, Kohl S, Wiesenfarth M, Schelb P, Radtke JP, Gotz M, Kickingereder P, Yaqubi K, Hitthaler B, Gahlert N, Kuder TA, Deister F, Freitag M, Hohenfellner M, Hadaschik BA, Schlemmer HP, Maier-Hein KH (2018) Radiomic Machine Learning for Characterization of Prostate Lesions with MRI: Comparison to ADC Values. Radiology 289 (1):128-137. https://doi.org/10.1148/radiol.2018173064

  31. Sun XY, Feng QX, Xu X, Zhang J, Zhu FP, Yang YH, Zhang YD (2020) Radiologic-Radiomic Machine Learning Models for Differentiation of Benign and Malignant Solid Renal Masses: Comparison With Expert-Level Radiologists. AJR Am J Roentgenol 214 (1):W44-W54. https://doi.org/10.2214/ajr.19.21617

  32. Gupta K, Miller JD, Li JZ, Russell MW, Charbonneau C (2008) Epidemiologic and socioeconomic burden of metastatic renal cell carcinoma (mRCC): a literature review. Cancer Treat Rev 34 (3):193-205. https://doi.org/10.1016/j.ctrv.2007.12.001

  33. 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 (2):333-351. https://doi.org/10.1148/radiol.2017160343

  34. Uthoff J, Stephens MJ, Newell JD, Jr., Hoffman EA, Larson J, Koehn N, De Stefano FA, Lusk CM, Wenzlaff AS, Watza D, Neslund-Dudas C, Carr LL, Lynch DA, Schwartz AG, Sieren JC (2019) Machine learning approach for distinguishing malignant and benign lung nodules utilizing standardized perinodular parenchymal features from CT. Med Phys 46 (7):3207-3216. https://doi.org/10.1002/mp.13592

  35. Abajian A, Murali N, Savic LJ, Laage-Gaupp FM, Nezami N, Duncan JS, Schlachter T, Lin M, Geschwind JF, Chapiro J (2018) Predicting Treatment Response to Intra-arterial Therapies for Hepatocellular Carcinoma with the Use of Supervised Machine Learning-An Artificial Intelligence Concept. J Vasc Interv Radiol 29 (6):850-857 e851. https://doi.org/10.1016/j.jvir.2018.01.769

  36. Yu J, Zhang X, Liu H, Zhang R, Yu X, Cheng Z, Han Z, Liu F, Hao G, Mu MJ, Liang P (2020) Percutaneous Microwave Ablation versus Laparoscopic Partial Nephrectomy for cT1a Renal Cell Carcinoma: A Propensity-matched Cohort Study of 1955 Patients. Radiology 294 (3):698-706. https://doi.org/10.1148/radiol.2020190919

  37. Orlhac F, Frouin F, Nioche C, Ayache N, Buvat I (2019) Validation of A Method to Compensate Multicenter Effects Affecting CT Radiomics. Radiology 291 (1):53-59. https://doi.org/10.1148/radiol.2019182023

  38. Rios Velazquez E, Aerts HJ, Gu Y, Goldgof DB, De Ruysscher D, Dekker A, Korn R, Gillies RJ, Lambin P (2012) A semiautomatic CT-based ensemble segmentation of lung tumors: comparison with oncologists’ delineations and with the surgical specimen. Radiother Oncol 105 (2):167-173. https://doi.org/10.1016/j.radonc.2012.09.023

Download references

Funding

None.

Author information

Authors and Affiliations

  1. BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Daniela Said, Stefanie J. Hectors, Daniel Stocker, Octavia Bane, Sara Lewis & Bachir Taouli

  2. Department of Radiology, Universidad de los Andes, Santiago, Chile

    Daniela Said

  3. Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Stefanie J. Hectors, Eric Wilck, Ally Rosen, Sara Lewis & Bachir Taouli

  4. Department of Radiology, Weill Cornell Medicine, New York, NY, USA

    Stefanie J. Hectors

  5. Department of Radiology, Long Island School of Medicine, NYU-Winthrop Hospital, Mineola, NY, USA

    Ally Rosen

  6. Institute of Interventional and Diagnostic Radiology, University Hospital Zurich and University of Zurich, Zurich, Switzerland

    Daniel Stocker

  7. Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Alp Tuna Beksaç & Ketan Badani

Authors
  1. Daniela Said
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stefanie J. Hectors
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Eric Wilck
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ally Rosen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Daniel Stocker
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Octavia Bane
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Alp Tuna Beksaç
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Sara Lewis
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Ketan Badani
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Bachir Taouli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bachir Taouli.

Ethics declarations

Conflict of interest

None of the authors declare conflicts of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 151 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Said, D., Hectors, S.J., Wilck, E. et al. Characterization of solid renal neoplasms using MRI-based quantitative radiomics features. Abdom Radiol 45, 2840–2850 (2020). https://doi.org/10.1007/s00261-020-02540-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00261-020-02540-4

Keywords

Search

Navigation