Skip to main content
Log in

Tumor margin irregularity degree is an important preoperative predictor of adverse pathology for clinical T1/2 renal cell carcinoma and the construction of predictive model

  • Original Article
  • Published:
World Journal of Urology Aims and scope Submit manuscript

Abstract

Purpose

To explore the critical role of the tumor margin irregularity degree (TMID) of renal tumors in predicting adverse pathology of patients with clinical T1/2 (cT1/2) renal cell carcinoma (RCC).

Methods

A total of 821 patients with cT1/2 RCC undergoing nephrectomy in the Second Hospital of Tianjin Medical University between January 2017 and December 2020 were reviewed. The tumor margin irregularity (TMI) was classified into renal mass with locally raised protrusion and smooth margin called ‘lobular’, sharply and unsmooth nodular margin called ‘spiculation’, blurred margins between tumor and renal parenchyma or a completely irregular and non-elliptical shape. The ratio between the number of irregular cross-sections (X) and the number of total cross-sections from top to bottom occupied (Y) was defined as TMID (X/Y). The logistic regression was performed to determine the independent predictors of adverse pathology, and the Kaplan–Meier curve and log-rank test were used to analyze the survival outcomes.

Results

Among 821 cT1/2 RCC patients, 245 (29.8%) had adverse pathology. The results of the univariate and multivariate logistic regressions showed that the age, tumor size, hemoglobin, and TMID were the independent predictors of adverse pathology. Incorporation of TMID could increase the discrimination of the predictive model with the area under curve (AUC) of ROC curves increasing from 0.725 to 0.808. Patients with adverse pathology or higher TMID both had significantly shorter recurrence-free survival (RFS).

Conclusion

The nomogram model incorporated with TMID for predicting adverse pathology could increase its discrimination, calibration, and clinical application values, compared with the models without TMID.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

Data availability

The datasets used and/or analyzed during the current study are available on the Supplementary file 5.

Abbreviations

TMID:

Tumor margin irregularity degree

RCC:

Renal cell carcinoma

RFS:

Recurrence-free survival

PN:

Partial nephrectomy

RN:

Radical nephrectomy

CSS:

Cancer-specific survival

AS:

Active surveillance

sRCC:

Small renal cell carcinoma

CT:

Computerized tomography

MRI:

Magnetic resonance imaging

CID:

Contour irregularity degree

pRCC:

Papillary renal cell carcinoma

BMI:

Body mass index

IQR:

Interquartile range

VIF:

Variance inflation factor

ROC:

Receiver operating characteristic

DCA:

Decision curve analysis

AUC:

Area under curve

NLR:

Neutrophil-to-lymphocyte ratio

AGR:

Albumin-to-globulin ratio

References

  1. Sung H, Ferlay J, Siegel RL et al (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 71(3):209–249. https://doi.org/10.3322/caac.21660

    Article  PubMed  Google Scholar 

  2. Bukavina L, Bensalah K, Bray F et al (2022) Epidemiology of renal cell carcinoma: 2022 update. Eur Urol. https://doi.org/10.1016/j.eururo.2022.08.019

    Article  PubMed  Google Scholar 

  3. Ljungberg B, Albiges L, Abu-Ghanem Y et al (2022) European association of urology guidelines on renal cell carcinoma: the 2022 update. Eur Urol 82(4):399–410. https://doi.org/10.1016/j.eururo.2022.03.006

    Article  PubMed  Google Scholar 

  4. Dabestani S, Marconi L, Kuusk T et al (2018) Follow-up after curative treatment of localised renal cell carcinoma. World J Urol 36(12):1953–1959. https://doi.org/10.1007/s00345-018-2338-z

    Article  PubMed  Google Scholar 

  5. Ball MW, Gorin MA, Bhayani SB et al (2015) Preoperative predictors of malignancy and unfavorable pathology for clinical T1a tumors treated with partial nephrectomy: a multi-institutional analysis. Urol Oncol 33(3):112.e9–14. https://doi.org/10.1016/j.urolonc.2014.11.003

    Article  PubMed  Google Scholar 

  6. Tosoian JJ, Feldman AS, Abbott MR et al (2020) Biopsy cell cycle proliferation score predicts adverse surgical pathology in localized renal cell carcinoma. Eur Urol 78(5):657–660. https://doi.org/10.1016/j.eururo.2020.08.032

    Article  PubMed  Google Scholar 

  7. Richard PO, Jewett MA, Bhatt JR et al (2015) Renal tumor biopsy for small renal masses: a single-center 13-year experience. Eur Urol 68(6):1007–1013. https://doi.org/10.1016/j.eururo.2015.04.004

    Article  PubMed  Google Scholar 

  8. Patel N, Cranston D, Akhtar MZ et al (2012) Active surveillance of small renal masses offers short-term oncological efficacy equivalent to radical and partial nephrectomy. BJU Int 110(9):1270–1275. https://doi.org/10.1111/j.1464-410X.2012.11130.x

    Article  PubMed  Google Scholar 

  9. Jewett MA, Mattar K, Basiuk J et al (2011) Active surveillance of small renal masses: progression patterns of early stage kidney cancer. Eur Urol 60(1):39–44. https://doi.org/10.1016/j.eururo.2011.03.030

    Article  PubMed  Google Scholar 

  10. Li G, Xiao T, Wang K et al (2021) Histopathological validation of safe margin for nephron-sparing surgery based on individual tumor growth pattern. World J Surg Oncol 19(1):255. https://doi.org/10.1186/s12957-021-02375-3

    Article  PubMed  PubMed Central  Google Scholar 

  11. 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. https://doi.org/10.1148/radiol.2018171557

    Article  PubMed  Google Scholar 

  12. Leslie S, Gill IS, de Castro Abreu AL et al (2014) Renal tumor contact surface area: a novel parameter for predicting complexity and outcomes of partial nephrectomy. Eur Urol 66(5):884–893. https://doi.org/10.1016/j.eururo.2014.03.010

    Article  PubMed  Google Scholar 

  13. Jamshidi N, Jonasch E, Zapala M et al (2015) The radiogenomic risk score: construction of a prognostic quantitative, noninvasive image-based molecular assay for renal cell carcinoma. Radiology 277(1):114–123. https://doi.org/10.1148/radiol.2015150800

    Article  PubMed  Google Scholar 

  14. Dai C, Huang J, Li Y et al (2021) Tumor contour irregularity on preoperative imaging: a practical and useful prognostic parameter for papillary renal cell carcinoma. Eur Radiol 31(6):3745–3753. https://doi.org/10.1007/s00330-020-07456-7

    Article  PubMed  Google Scholar 

  15. Liu H, Tang K, Chen Z et al (2021) Comparison and development of preoperative systemic inflammation markers-based models for the prediction of unfavorable pathology in newly diagnosed clinical T1 renal cell carcinoma. Pathol Res Pract 225:153563. https://doi.org/10.1016/j.prp.2021.153563

    Article  CAS  PubMed  Google Scholar 

  16. Fuhrman SA, Lasky LC, Limas C (1982) Prognostic significance of morphologic parameters in renal cell carcinoma. Am J Surg Pathol 6(7):655–663. https://doi.org/10.1097/00000478-198210000-00007

    Article  CAS  PubMed  Google Scholar 

  17. Kutikov A, Smaldone MC, Egleston BL et al (2011) Anatomic features of enhancing renal masses predict malignant and high-grade pathology: a preoperative nomogram using the RENAL Nephrometry score. Eur Urol 60(2):241–248. https://doi.org/10.1016/j.eururo.2011.03.029

    Article  PubMed  PubMed Central  Google Scholar 

  18. Capitanio U, Montorsi F (2016) Renal cancer. Lancet 387(10021):894–906. https://doi.org/10.1016/S0140-6736(15)00046-X

    Article  PubMed  Google Scholar 

  19. Finelli A, Ismaila N, Bro B et al (2017) Management of small renal masses: american society of clinical oncology clinical practice guideline. J Clin Oncol 35(6):668–680. https://doi.org/10.1200/JCO.2016.69.9645

    Article  PubMed  Google Scholar 

  20. Yang C, Shuch B, Serrano M et al (2019) Adverse histopathologic characteristics in small clear cell renal cell carcinomas have negative impact on prognosis: a study of 631 cases with clinical follow-up. Am J Surg Pathol 43(10):1413–1420. https://doi.org/10.1097/PAS.0000000000001333

    Article  PubMed  Google Scholar 

  21. Maurice MJ, Zhu H, Kim SP et al (2016) Increased use of partial nephrectomy to treat high-risk disease. BJU Int 117(6B):E75-86. https://doi.org/10.1111/bju.13262

    Article  PubMed  Google Scholar 

  22. Richard PO, Lavallée 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. https://doi.org/10.1016/j.juro.2018.04.015

    Article  PubMed  Google Scholar 

  23. Lane BR, Babineau D, Kattan MW et al (2007) A preoperative prognostic nomogram for solid enhancing renal tumors 7 cm or less amenable to partial nephrectomy. J Urol 178(2):429–434. https://doi.org/10.1016/j.juro.2007.03.106

    Article  PubMed  Google Scholar 

  24. Limkin EJ, Reuzé S, Carré A et al (2019) The complexity of tumor shape, spiculatedness, correlates with tumor radiomic shape features. Sci Rep 9(1):4329. https://doi.org/10.1038/s41598-019-40437-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Bhandari A, Ibrahim M, Sharma C et al (2021) CT-based radiomics for differentiating renal tumours: a systematic review. Abdom Radiol (NY) 46(5):2052–2063. https://doi.org/10.1007/s00261-020-02832-9

    Article  PubMed  Google Scholar 

  26. Edge SB, Compton CC (2010) The American Joint Committee on Cancer: the 7th edition of the AJCC cancer staging manual and the future of TNM. Ann Surg Oncol 17(6):1471–1474. https://doi.org/10.1245/s10434-010-0985-4

    Article  PubMed  Google Scholar 

  27. Xu P, Zhang S, Cao B et al (2022) Predictive value of renal tumor contour irregularity score in pathological T3a upstaging of clinical T1 renal cell carcinoma: a multi-institutional study. Urol Oncol 40(5):199.e1-199.e8. https://doi.org/10.1016/j.urolonc.2022.02.005

    Article  PubMed  Google Scholar 

  28. Riley RD, Ensor J, Snell K et al (2020) Calculating the sample size required for developing a clinical prediction model. BMJ 368:m441. https://doi.org/10.1136/bmj.m441

    Article  PubMed  Google Scholar 

Download references

Funding

This study was supported by the Tianjin Municipal Natural Science Foundation (grant no.21JCYBJC01690).

Author information

Authors and Affiliations

Authors

Contributions

KRW: protocol/project development, data collection or management, data analysis, manuscript writing; GXW: protocol/project development, data collection or management, data analysis; YRL: data collection or management, data analysis, manuscript writing; LD: data collection or management, data analysis; YJN and GL: protocol/project development, data analysis, manuscript editing.

Corresponding authors

Correspondence to Yuanjie Niu or Gang Li.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Ethical approval

This study was approved by the Institutional Review Board of Tianjin Medical University. All procedures followed were in accordance with the Helsinki Declaration of 1964 and later versions.

Consent for publication

Informed consent was obtained from all individual participants enrolled in the study.

Additional information

Publisher's Note

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

Supplementary Information

Below is the link to the electronic supplementary material.

345_2023_4698_MOESM1_ESM.tif

Supplementary file1 Supplementary Figure 1. Flowchart of clinical T1/2 RCC patients treated with nephrectomy included in the study. RCC, renal cell carcinoma (TIF 932 KB)

345_2023_4698_MOESM2_ESM.tif

Supplementary file2 Supplementary Figure 2. Irregular tumor margin of RCC in contrast-enhanced CT (A) A mass with smooth margin and prominent nodules from part of it; (B) A mass with unsmooth margin and sharply small nodular from part of it; (C) A mass with blurred margin; (D) A mass with completely irregular and non-elliptical shape. RCC, renal cell carcinoma (TIF 2372 KB)

345_2023_4698_MOESM3_ESM.tif

Supplementary file3 Supplementary Figure 3. Kaplan-Meier curve of RFS for cT1/2 RCC patients stratified by (A) adverse pathology, (B) TMID, (C) age, (D) tumor size, (E) hemoglobin and (F) ‘N’ score. RCC, renal cell carcinoma; TMID, tumor margin irregularity degree; RFS, recurrence-free survival; AUC, area under curve (TIF 854 KB)

Supplementary file4 (DOCX 33 KB)

Supplementary file5 (CSV 76 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, K., Wang, G., Liu, Y. et al. Tumor margin irregularity degree is an important preoperative predictor of adverse pathology for clinical T1/2 renal cell carcinoma and the construction of predictive model. World J Urol 42, 64 (2024). https://doi.org/10.1007/s00345-023-04698-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00345-023-04698-0

Keywords

Navigation