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MR texture analysis in differentiation of small and very small renal cell carcinoma subtypes

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

Abstract

Purpose

To explore the diagnostic efficacy of MR-based texture analysis in differentiation of small (≤ 4 cm) and very small (≤ 2 cm) renal cell carcinoma subtypes.

Methods

One hundred and eight patients with pT1a (≤ 4 cm) renal cell carcinoma and pretreatment MRI were enrolled in this retrospective study. Histogram and gray-level co-occurrence matrix (GLCM) parameters were extracted from whole-tumor images. Among subtypes, patient age, tumor size, histological grading and texture parameters were compared. Diagnostic model using combination of texture parameters was constructed using logistic regression and validated using fivefold cross-validation. AUC with 95% CI, accuracy, sensitivity and specificity for subtype differentiation are reported. Further we explored the distinguishing ability of texture parameters and diagnostic model in very small (≤ 2 cm) RCC subgroups.

Results

Significant texture parameters among RCC subtypes were identified. For small (≤ 4 cm) renal cell carcinoma subtyping, combining models based on texture parameters achieved good AUCs for differentiating ccRCC vs. non-ccRCC, chRCC vs. non-chRCC and ccRCC vs. chRCC (0.79, 0.74 and 0.81). Further, in subgroups of very small (≤ 2 cm) RCCs, diagnostic models had better differentiating performances, achieving AUCs of 0.88, 0.99, 0.96 in differentiating ccRCC vs. non-ccRCC, chRCC vs. non-chRCC and ccRCC vs. chRCC.

Conclusion

MR texture analysis may help to differentiate small (≤ 4 cm) and very small (≤ 2 cm) RCC subtypes. This non-invasive method can potentially provide additional information for localized RCC treatment and surveillance strategy.

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References

  1. Hsieh JJ, Purdue MP, Signoretti S et al (2017) Renal cell carcinoma. Nat Rev Dis Primers 3:17009

    Article  PubMed  PubMed Central  Google Scholar 

  2. Antonio F, Nofisat I, Bill B et al (2016) Management of Small Renal Masses: American Society of Clinical Oncology Clinical Practice Guideline. J Clin Oncol 35:668-680

    Google Scholar 

  3. Campbell S, Uzzo RG, Allaf ME, et al (2017) Renal mass and localized renal cancer: AUA guideline. J Urol 198(3): 520-529

    Article  PubMed  Google Scholar 

  4. Ryan DW, Hajime T, Steven CC, Erick MR (2018) AUA Renal Mass and Localized Renal Cancer Guidelines: Imaging Implications. RadioGraphics 38:2021–2033

    Article  Google Scholar 

  5. Moch H, Cubilla AL, Humphrey PA, Reuter VE, Ulbright TM (2016) The 2016 WHO classification of tumours of the urinary system and male genital organs-part a: renal, penile, and testicular tumours. Eur Urol 70:93-105

    Article  PubMed  Google Scholar 

  6. Jung JP, Chan KK (2017) Small (< 4 cm) Renal Tumors With Predominantly Low Signal Intensity on T2-Weighted Images: Differentiation of Minimal-Fat Angiomyolipoma From Renal Cell Carcinoma. Am J Roentgenol 208:124-130

    Article  Google Scholar 

  7. Kohei S, Naoki T (2018) CT and MR imaging for solid renal mass characterization. Eur J Radiol 99:40-54.

    Article  Google Scholar 

  8. Young JR, Coy H, Kim HJ et al (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. Am J Roentgenol 208:812-819

    Article  Google Scholar 

  9. Miles KA, Ganeshan B, Hayball MP (2013) CT texture analysis using the filtration-histogram method: what do the measurements mean? Cancer Imaging 13:400-406

    Article  PubMed  PubMed Central  Google Scholar 

  10. Castellano G, Bonilha L, Li LM, Cendes F (2004) Texture analysis of medical images. Clin Radiol 59:1061-1069

    Article  CAS  PubMed  Google Scholar 

  11. Ankur G, Abdul R, Devasenathipathy K et al (2019) Role of MR texture analysis in histological subtyping and grading of renal cell carcinoma: a preliminary study. Abdom Radiol, 44(10):3336-3349

    Article  Google Scholar 

  12. Wei W, KaiMing Cao, ShengMing J et al (2020) Differentiation of renal cell carcinoma subtypes through MRI-based radiomics analysis. Eur Radio 30:5738-5747

    Article  Google Scholar 

  13. Uyen NH, S. Mojdeh M, Osorio M et al (2018) Assessment of multiphasic contrast-enhanced MR textures in differentiating small renal mass subtypes. Abdom Radiol 43:3400-3409

    Article  Google Scholar 

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

    Article  PubMed  Google Scholar 

  15. Stephan U, Lucian B, Annemarie B et al (2020) Radiomics of computed tomography and magnetic resonance imaging in renal cell carcinoma—a systematic review and meta-analysis. Eur Radio 30:3558-3566

    Article  Google Scholar 

  16. Cui E, Li Z, Ma C et al (2020) Predicting the ISUP grade of clear cell renal cell carcinoma with multiparametric MR and multiphase CT radiomics. Eur Radiol. 30(5):2912-2921

    Article  PubMed  Google Scholar 

  17. Yao Z, Shuai W, Yan C, Huiqian D (2021) Deep learning with a convolutional neural network model to differentiate renal parenchymal tumors: a preliminary study. Abdom Radiol 46:3260-3268

    Article  Google Scholar 

  18. Xi IL, Zhao Y, Wang R, et al. (2020) Deep learning to distinguish benign from malignant renal lesions based on routine MR imaging. Clin Cancer Res 26(8):1944-1952

    Article  PubMed  Google Scholar 

  19. Meghan GL (2020) Radiomics and Artificial Intelligence for Renal Mass Characterization. Radiol Clin North Am 58(5):995-1008

    Article  Google Scholar 

Download references

Acknowledgements

Thanks to Dr. Jue Fan (Singeron Biotechnologies) for the consultation of the statistics.

Funding

This study is supported by Beijing Hope Run Special Fund of Cancer Foundation of China (LC2019B07), CAMS Innovation Fund for Medical Sciences (CIFMS) 2021-I2M-CT-B-059 and National Natural Science Foundation of China (81201701).

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Authors and Affiliations

  1. Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China

    Yichen Wang, Xinxin Zhang, Jin Zhang, Lianyu Zhang, Jie Zhang & Yan Chen

Authors
  1. Yichen Wang
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  2. Xinxin Zhang
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  3. Jin Zhang
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  4. Lianyu Zhang
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  5. Jie Zhang
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  6. Yan Chen
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Correspondence to Yan Chen.

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Wang, Y., Zhang, X., Zhang, J. et al. MR texture analysis in differentiation of small and very small renal cell carcinoma subtypes. Abdom Radiol 48, 1044–1050 (2023). https://doi.org/10.1007/s00261-022-03794-w

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  • DOI: https://doi.org/10.1007/s00261-022-03794-w

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