Establishment of prognosis related model of localized clear cell renal cell carcinoma

ZHANG Xuanyu; SU Zhonghua; BU Ren'ge

doi:10.7619/jcmp.20201810

Journal of Clinical Medicine in Practice > 2021 > 25(7): 107-110, 114. > DOI: 10.7619/jcmp.20201810

ZHANG Xuanyu, SU Zhonghua, BU Ren'ge. Establishment of prognosis related model of localized clear cell renal cell carcinoma[J]. Journal of Clinical Medicine in Practice, 2021, 25(7): 107-110, 114. DOI: 10.7619/jcmp.20201810

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Establishment of prognosis related model of localized clear cell renal cell carcinoma

1.
Department of Urinary Surgery, Shengjing Hospital Affiliated to China Medical University, Shenyang, Liaoning, 110004
2.
Department of Gynecology, Shengjing Hospital Affiliated to China Medical University, Shenyang, Liaoning, 110004

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Received Date: December 27, 2020
Available Online: April 20, 2021
Published Date: April 14, 2021

Graphical Abstract

Abstract

Abstract

Objective To establish and evaluate postoperative prognosis related predictive model for patients with localized clear cell renal cell carcinoma (ccRCC).
Methods The clinical materials of 526 ccRCC patients with radical nephrectomy or partial nephrectomy were retrospectively analyzed. Univariate and multivariate Cox regression analysis were used to establish Nomograms. The value of the model was evaluated by calibration plots, decision curve analysis (DCA) and Harrell's consistency index (CI).
Results Univariate analysis showed that age, clinical symptoms, history of hypertension, hyperlipidemia, D-dimer, albumin, anemia, preoperative creatinine, pathological grading and tumor size were independent risk factors for disease-free survival (DFS) (P < 0.05). The final predictive model included age, symptoms, anemia, D-dimer and tumor size. The CI of Nomogram was 0.78 (95% CI, 0.71 to 0.85). The calibration plots at 3 and 5 years after operation showed that the model performed well, and DCA showed that the model had clinical benefits.
Conclusion The predictive model constructed in this study can predict the prognosis of patients with localized ccRCC, and it can provide reference for postoperative follow-up and personalized disease management of related patients.
- clear cell renal cell carcinoma,
- Nomogram,
- calibration plots,
- decision curve analysis

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References (22)

References

[1]	FERLAY J, COLOMBET M, SOERJOMATARAM I, et al. Cancer incidence and mortality patterns in Europe: Estimates for 40 countries and 25 major cancers in 2018[J]. Eur J Cancer, 2018, 103: 356-387. doi: 10.1016/j.ejca.2018.07.005
[2]	KEEGAN K A, SCHUPP C W, CHAMIE K, et al. Histopathology of surgically treated renal cell carcinoma: survival differences by subtype and stage[J]. J Urol, 2012, 188(2): 391-397. doi: 10.1016/j.juro.2012.04.006
[3]	TSUI K H, SHVARTS O, SMITH R B, et al. Prognostic indicators for renal cell carcinoma: a multivariate analysis of 643 patients using the revised 1997 TNM staging criteria[J]. J Urol, 2000, 163(4): 1090-1095, 1295. doi: 10.1016/S0022-5347(05)67699-9
[4]	ZHAO H L, CAO Y T, WANG Y, et al. Dynamic prognostic model for kidney renal clear cell carcinoma (KIRC) patients by combining clinical and genetic information[J]. Sci Rep, 2018, 8(1): 17613. doi: 10.1038/s41598-018-35981-5
[5]	SORBELLINI M, KATTAN M W, SNYDER M E, et al. A postoperative prognostic nomogram predicting recurrence for patients with conventional clear cell renal cell carcinoma[J]. J Urol, 2005, 173(1): 48-51. doi: 10.1097/01.ju.0000148261.19532.2c
[6]	ZISMAN A, PANTUCK A J, DOREY F, et al. Improved prognostication of renal cell carcinoma using an integrated staging system[J]. J Clin Oncol, 2001, 19(6): 1649-1657. doi: 10.1200/JCO.2001.19.6.1649
[7]	FRANK I, BLUTE M L, CHEVILLE J C, et al. An outcome prediction model for patients with clear cell renal cell carcinoma treated with radical nephrectomy based on tumor stage, size, grade and necrosis: the SSIGN score[J]. J Urol, 2002, 168(6): 2395-2400. doi: 10.1016/S0022-5347(05)64153-5
[8]	ZHANG X, BU R G, LIU Z Q, et al. Development and validation of a model for predicting intravesical recurrence in organ-confined upper urinary tract urothelial carcinoma patients after radical nephroureterectomy: a retrospective study in one center with long-term follow-up[J]. Pathol Oncol Res, 2020, 26(3): 1741-1748. doi: 10.1007/s12253-019-00748-4
[9]	VICKERS A J, CRONIN A M, ELKIN E B, et al. Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers[J]. BMC Med Inform Decis Mak, 2008, 8: 53-58. doi: 10.1186/1472-6947-8-53
[10]	SOBIN L H, WITTEKIND C. TNM classification of malignant tumours[M]. Hoboken, NJ, USA: John Wiley & Sons, Inc, 2003: 23-25.
[11]	KARAKIEWICZ P I, BRIGANTI A, CHUN F K, et al. Multi-institutional validation of a new renal cancer-specific survival nomogram[J]. J Clin Oncol, 2007, 25(11): 1316-1322. doi: 10.1200/JCO.2006.06.1218
[12]	TSIMAFEYEU I V, DEMIDOV L V, MADZHUGA A V, et al. Hypercoagulability as a prognostic factor for survival in patients with metastatic renal cell carcinoma[J]. J Exp Clin Cancer Res, 2009, 28: 30-38. doi: 10.1186/1756-9966-28-30
[13]	ERDEM S, AMASYALI A S, AYTAC O, et al. Increased preoperative levels of plasma fibrinogen and D dimer in patients with renal cell carcinoma is associated with poor survival and adverse tumor characteristics[J]. Urol Oncol, 2014, 32(7): 1031-1040. doi: 10.1016/j.urolonc.2014.03.013
[14]	KIANG T K, TENG X W, SURENDRADOSS J, et al. Glutathione depletion by valproic acid in sandwich-cultured rat hepatocytes: Role of biotransformation and temporal relationship with onset of toxicity[J]. Toxicol Appl Pharmacol, 2011, 252(3): 318-324. doi: 10.1016/j.taap.2011.03.004
[15]	LAURSEN I, BRIAND P, LYKKESFELDT A E. Serum albumin as a modulator on growth of the human breast cancer cell line, MCF-7[J]. Anticancer Res, 1990, 10(2A): 343-351. http://www.ncbi.nlm.nih.gov/pubmed/2346307
[16]	KAYSEN G A. Serum albumin concentration in dialysis patients: why does it remain resistant to therapy[J]. Kidney Int Suppl, 2003(87): S92-S98. http://www.ncbi.nlm.nih.gov/pubmed/14531780?dopt=Abstract
[17]	MORGAN T M, TANG D, STRATTON K L, et al. Preoperative nutritional status is an important predictor of survival in patients undergoing surgery for renal cell carcinoma[J]. Eur Urol, 2011, 59(6): 923-928. doi: 10.1016/j.eururo.2011.01.034
[18]	HE X B, GUO S J, CHEN D, et al. Preoperative albumin to globulin ratio (AGR) as prognostic factor in renal cell carcinoma[J]. J Cancer, 2017, 8(2): 258-265. doi: 10.7150/jca.16525
[19]	FOX P, HUDSON M, BROWN C, et al. Markers of systemic inflammation predict survival in patients with advanced renal cell cancer[J]. Br J Cancer, 2013, 109(1): 147-153. doi: 10.1038/bjc.2013.300
[20]	MCMILLAN D C. Systemic inflammation, nutritional status and survival in patients with cancer[J]. Curr Opin Clin Nutr Metab Care, 2009, 12(3): 223-226. doi: 10.1097/MCO.0b013e32832a7902
[21]	HU H, YAO X, XIE X, et al. Prognostic value of preoperative NLR, dNLR, PLR and CRP in surgical renal cell carcinoma patients[J]. World J Urol, 2017, 35(2): 261-270. doi: 10.1007/s00345-016-1864-9
[22]	YAYCIOGLU O, ESKICORAPCI S, KARABULUT E, et al. A preoperative prognostic model predicting recurrence-free survival for patients with kidney cancer[J]. Jpn J Clin Oncol, 2013, 43(1): 63-68. doi: 10.1093/jjco/hys192

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Establishment of prognosis related model of localized clear cell renal cell carcinoma