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Prospective performance of clear cell likelihood scores (ccLS) in renal masses evaluated with multiparametric magnetic resonance imaging

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Abstract

Objectives

Solid renal masses have unknown malignant potential with commonly utilized imaging. Biopsy can offer a diagnosis of cancer but has a high non-diagnostic rate and complications. Reported use of multiparametric magnetic resonance imaging (mpMRI) to diagnose aggressive histology (i.e., clear cell renal cell carcinoma (ccRCC)) via a clear cell likelihood score (ccLS) was based on retrospective review of cT1a tumors. We aim to retrospectively assess the diagnostic performance of ccLS prospectively assigned to renal masses of all stages evaluated with mpMRI prior to histopathologic evaluation.

Methods

In this retrospective cohort study from June 2016 to November 2019, 434 patients with 454 renal masses from 2 institutions with heterogenous patient populations underwent mpMRI with prospective ccLS assignment and had pathologic diagnosis. ccLS performance was assessed by contingency table analysis. The association between ccLS and ccRCC was assessed with logistic regression.

Results

Mean age and tumor size were 60 ± 13 years and 5.4 ± 3.8 cm. Characteristics were similar between institutions except for patient age and race (both p < 0.001) and lesion laterality and histology (both p = 0.04). The PPV of ccLS increased with each increment in ccLS (ccLS1 5% [3/55], ccLS2 6% [3/47], ccLS3 35% [20/57], ccLS4 78% [85/109], ccLS5 93% [173/186]). Pooled analysis for ccRCC diagnosis revealed sensitivity 91% (258/284), PPV 87% (258/295) for ccLS ≥ 4, and specificity 56% (96/170), NPV 94% (96/102) for ccLS ≤ 2. Diagnostic performance was similar between institutions.

Conclusions

We confirm the optimal diagnostic performance of mpMRI to identify ccRCC in all clinical stages. High PPV and NPV of ccLS can help inform clinical management decision-making.

Key Points

• The positive predictive value of the clear cell likelihood score (ccLS) for detecting clear cell renal cell carcinoma was 5% (ccLS1), 6% (ccLS2), 35% (ccLS3), 78% (ccLS4), and 93% (ccLS5). Sensitivity of ccLS ≥ 4 and specificity of ccLS ≤ 2 were 91% and 56%, respectively.

• When controlling for confounding variables, ccLS is an independent risk factor for identifying clear cell renal cell carcinoma.

• Utilization of the ccLS can help guide clinical care, including the decision for renal mass biopsy, reducing the morbidity and risk to patients.

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Abbreviations

ADC:

Apparent diffusion coefficient

AS:

Active surveillance

ccLS:

Clear cell likelihood score

ccRCC:

Clear cell renal cell carcinoma

chrRCC:

Chromophobe RCC

mpMRI:

Multiparametric magnetic resonance imaging

pRCC:

Papillary RCC

RMB:

Renal mass biopsy

References

  1. Smith-Bindman R, Miglioretti DL, Larson EB (2008) Rising use of diagnostic medical imaging in a large integrated health system. Health Aff (Millwood) 27(6):1491–1502

    Article  Google Scholar 

  2. Znaor A, Lortet-Tieulent J, Laversanne M, Jemal A, Bray F (2015) International variations and trends in renal cell carcinoma incidence and mortality. Eur Urol 67(3):519–530

    Article  PubMed  Google Scholar 

  3. Mason RJ, Abdolell M, Trottier G et al (2011) Growth kinetics of renal masses: analysis of a prospective cohort of patients undergoing active surveillance. Eur Urol 59(5):863–867

    Article  PubMed  Google Scholar 

  4. Campbell SC, Novick AC, Belldegrun A et al (2009) Guideline for management of the clinical T1 renal mass. J Urol 182(4):1271–1279

    Article  PubMed  Google Scholar 

  5. Escudier B, Porta C, Schmidinger M et al (2016) Renal cell carcinoma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol 27(suppl 5):v58–v68

    Article  CAS  PubMed  Google Scholar 

  6. Mehrazin R, Smaldone MC, Kutikov A et al (2014) Growth kinetics and short-term outcomes of cT1b and cT2 renal masses under active surveillance. J Urol 192(3):659–664

    Article  PubMed  PubMed Central  Google Scholar 

  7. Leibovich BC, Lohse CM, Cheville JC et al (2018) Predicting oncologic outcomes in renal cell carcinoma after surgery. Eur Urol 73(5):772–780

    Article  PubMed  Google Scholar 

  8. Delahunt B, Cheville JC, Martignoni G et al (2013) The International Society of Urological Pathology (ISUP) grading system for renal cell carcinoma and other prognostic parameters. Am J Surg Pathol 37(10):1490–1504

    Article  PubMed  Google Scholar 

  9. 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 

  10. Kutikov A, Smaldone MC, Uzzo RG, Haifler M, Bratslavsky G, Leibovich BC (2016) Renal mass biopsy: always, sometimes, or never? Eur Urol 70(3):403–406

    Article  PubMed  Google Scholar 

  11. Richard PO, Lavallee LT, Pouliot F et al (2018) Is routine renal tumor biopsy associated with lower rates of benign histology following nephrectomy for small renal masses? J Urol 200(4):731–736

    Article  PubMed  Google Scholar 

  12. Patel HD, Johnson MH, Pierorazio PM et al (2016) Diagnostic accuracy and risks of biopsy in the diagnosis of a renal mass suspicious for localized renal cell carcinoma: systematic review of the literature. J Urol 195(5):1340–1347

    Article  PubMed  PubMed Central  Google Scholar 

  13. Tomaszewski JJ, Uzzo RG, Smaldone MC (2014) Heterogeneity and renal mass biopsy: a review of its role and reliability. Cancer Biol Med 11(3):162–172

    PubMed  PubMed Central  Google Scholar 

  14. Allen BC, Tirman P, Jennings Clingan M, Manny J, Del Gaizo AJ, Leyendecker JR (2014) Characterizing solid renal neoplasms with MRI in adults. Abdom Imaging 39(2):358–387

    Article  PubMed  Google Scholar 

  15. Hotker AM, Mazaheri Y, Wibmer A et al (2017) Differentiation of clear cell renal cell carcinoma from other renal cortical tumors by use of a quantitative multiparametric MRI approach. AJR Am J Roentgenol 208(3):W85–W91

    Article  PubMed  PubMed Central  Google Scholar 

  16. Cornelis F, Tricaud E, Lasserre AS et al (2014) Routinely performed multiparametric magnetic resonance imaging helps to differentiate common subtypes of renal tumours. Eur Radiol 24(5):1068–1080

    Article  CAS  PubMed  Google Scholar 

  17. Canvasser NE, Kay FU, Xi Y et al (2017) Diagnostic accuracy of multiparametric magnetic resonance imaging to identify clear cell renal cell carcinoma in cT1a renal masses. J Urol 198(4):780–786

    Article  PubMed  PubMed Central  Google Scholar 

  18. Johnson BA, Kim S, Steinberg RL, de Leon AD, Pedrosa I, Cadeddu JA (2019) Diagnostic performance of prospectively assigned clear cell likelihood scores (ccLS) in small renal masses at multiparametric magnetic resonance imaging. Urol Oncol 37(12):941–946

    Article  PubMed  PubMed Central  Google Scholar 

  19. Silverman SG, Pedrosa I, Ellis JH et al (2019) Bosniak classification of cystic renal masses, version 2019: an update proposal and needs assessment. Radiology 292(2):475–488

    Article  PubMed  PubMed Central  Google Scholar 

  20. Kay FU, Pedrosa I (2017) Imaging of solid renal masses. Radiol Clin North Am 55(2):243–258

    Article  PubMed  Google Scholar 

  21. Kay FU, Canvasser NE, Xi Y et al (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

    Article  PubMed  PubMed Central  Google Scholar 

  22. Diaz de Leon A, Davenport MS, Silverman SG, Schieda N, Cadeddu JA, Pedrosa I (2019) Role of virtual biopsy in the management of renal masses. AJR Am J Roentgenol:1–10

  23. Sun MR, Ngo L, Genega EM et al (2009) Renal cell carcinoma: dynamic contrast-enhanced MR imaging for differentiation of tumor subtypes--correlation with pathologic findings. Radiology 250(3):793–802

    Article  PubMed  Google Scholar 

  24. Sasiwimonphan K, Takahashi N, Leibovich BC, Carter RE, Atwell TD, Kawashima A (2012) Small (<4 cm) renal mass: differentiation of angiomyolipoma without visible fat from renal cell carcinoma utilizing MR imaging. Radiology 263(1):160–168

    Article  PubMed  Google Scholar 

  25. 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(1):93–105

    Article  PubMed  Google Scholar 

  26. Gudbjartsson T, Hardarson S, Petursdottir V, Thoroddsen A, Magnusson J, Einarsson GV (2005) Renal oncocytoma: a clinicopathological analysis of 45 consecutive cases. BJU Int 96(9):1275–1279

    Article  PubMed  Google Scholar 

  27. Bindayi A, Hamilton ZA, McDonald ML et al (2018) Neoadjuvant therapy for localized and locally advanced renal cell carcinoma. Urol Oncol 36(1):31–37

    Article  CAS  PubMed  Google Scholar 

  28. Frank I, Blute ML, Cheville JC, Lohse CM, Weaver AL, Zincke H (2003) Solid renal tumors: an analysis of pathological features related to tumor size. J Urol 170(6):2217–2220

    Article  PubMed  Google Scholar 

  29. Beck SD, Patel MI, Snyder ME et al (2004) Effect of papillary and chromophobe cell type on disease-free survival after nephrectomy for renal cell carcinoma. Ann Surg Oncol 11(1):71–77

    Article  PubMed  Google Scholar 

  30. Patard JJ, Leray E, Rioux-Leclercq N et al (2005) Prognostic value of histologic subtypes in renal cell carcinoma: a multicenter experience. J Clin Oncol 23(12):2763–2771

    Article  PubMed  Google Scholar 

  31. Campbell SC, Novick AC, Herts B et al (1997) Prospective evaluation of fine needle aspiration of small, solid renal masses: accuracy and morbidity. Urology 50(1):25–29

    Article  CAS  PubMed  Google Scholar 

  32. Leveridge MJ, Finelli A, Kachura JR et al (2011) Outcomes of small renal mass needle core biopsy, nondiagnostic percutaneous biopsy, and the role of repeat biopsy. Eur Urol 60(3):578–584

    Article  PubMed  Google Scholar 

  33. Wang R, Wolf JS, Wood DP, Higgins EJ, Hafez KS (2009) Accuracy of percutaneous core biopsy in management of small renal masses. Urology 73(3):586–590

    Article  PubMed  Google Scholar 

  34. Friedman P, Sayah M, Egharevba A et al (2015) Evaluation of the concordance of histologic subtype and prognostic indicators between renal cell carcinoma biopsies and their subsequent resections, in USCAP 104th Annual Meeting. Mod Pathol: Boston, MA pp 202–271

  35. Ball MW, Bezerra SM, Gorin MA et al (2015) Grade heterogeneity in small renal masses: potential implications for renal mass biopsy. J Urol 193(1):36–40

    Article  PubMed  Google Scholar 

  36. He QQ, Wang HZ, Kenyon J et al (2015) Accuracy of percutaneous core biopsy in the diagnosis of small renal masses (<= 4.0 cm): a meta-analysis. Int Braz J Urol 41(1):15–25

    Article  PubMed  PubMed Central  Google Scholar 

  37. Johnson DC, Vukina J, Smith AB et al (2015) Preoperatively misclassified, surgically removed benign renal masses: a systematic review of surgical series and United States population level burden estimate. J Urol 193(1):30–35

    Article  PubMed  Google Scholar 

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Funding

This study has received funding by the NIH grants #U01CA207091, #P50CA196516 and #5RO1CA154475.

Author information

Authors and Affiliations

  1. Department of Urology, University of Texas Southwestern, Dallas, TX, USA

    Ryan L. Steinberg, Brett A. Johnson, Rashed Ghandour, Payal Kapur, Jeffrey A. Cadeddu & Ivan Pedrosa

  2. Department of Radiology, University of Texas Southwestern, NE6.216, 5323 Harry Hines Blvd, MC 9085, Dallas, TX, 75390, USA

    Robert G. Rasmussen, Alberto Diaz De Leon, Yin Xi, Takeshi Yokoo, Jeffrey A. Cadeddu & Ivan Pedrosa

  3. Advanced Imaging Research Center, University of Texas Southwestern, Dallas, TX, USA

    Takeshi Yokoo & Ivan Pedrosa

  4. University of Texas Southwestern Medical School, Dallas, TX, USA

    Sandy Kim

  5. Department of Pathology, University of Texas Southwestern, Dallas, TX, USA

    Payal Kapur

Authors
  1. Ryan L. Steinberg
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  2. Robert G. Rasmussen
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  3. Brett A. Johnson
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  4. Rashed Ghandour
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  5. Alberto Diaz De Leon
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  7. Takeshi Yokoo
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  8. Sandy Kim
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  9. Payal Kapur
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  10. Jeffrey A. Cadeddu
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  11. Ivan Pedrosa
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Corresponding author

Correspondence to Ivan Pedrosa.

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Guarantor

The scientific guarantor of this publication is Ivan Pedrosa.

Conflict of interest

Ivan Pedrosa served in an Advisory Scientific Board for Bayer Healthcare.

Institutional Research Agreement, Philips Healthcare.

Institutional Research Agreement, Siemens Healthineers.

Institutional Research Agreement, GE Healthcare.

Statistics and biometry

One of the authors (YX) has significant statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

Some study subjects or cohorts have been previously reported in Johnson BA, Kim S, Steinberg RL, Diaz de Leon A, Pedrosa I, Cadeddu JA, “Diagnostic performance of prospectively assigned clear cell Likelihood scores (ccLS) in small renal masses at multiparametric magnetic resonance imaging,” Urologic Oncology: Seminars and Original Investigations, 2019.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at two institutions

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Ryan L. Steinberg and Robert G. Rasmussen designate co-first authorship

Electronic supplementary material

Supplemental Figure 1

(A) Distribution of lesions with and without pathology (path) by clear cell likelihood score (ccLS). (B) Distribution of lesions with and without pathology by ranges of tumor size and evidence invasive features on MRI: < 4 cm (i.e., T1a), 4–7 cm (i.e., T1b), > 7 cm or T3–4 (i.e., T2–4). (DOCX 138 kb)

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Steinberg, R.L., Rasmussen, R.G., Johnson, B.A. et al. Prospective performance of clear cell likelihood scores (ccLS) in renal masses evaluated with multiparametric magnetic resonance imaging. Eur Radiol 31, 314–324 (2021). https://doi.org/10.1007/s00330-020-07093-0

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  • DOI: https://doi.org/10.1007/s00330-020-07093-0

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