Web-based nomogram and risk stratification system constructed for predicting the overall survival of older adults with primary kidney cancer after surgical resection.
Elderly patients
Kidney cancer
Nomogram
Overall survival
Risk stratification system
SEER database
Surgery
Journal
Journal of cancer research and clinical oncology
ISSN: 1432-1335
Titre abrégé: J Cancer Res Clin Oncol
Pays: Germany
ID NLM: 7902060
Informations de publication
Date de publication:
Oct 2023
Oct 2023
Historique:
received:
22
05
2023
accepted:
29
06
2023
medline:
31
8
2023
pubmed:
6
7
2023
entrez:
6
7
2023
Statut:
ppublish
Résumé
Kidney cancer (KC) is one of the most common malignant tumors in adults which particularly affects the survival of elderly patients. We aimed to construct a nomogram to predict overall survival (OS) in elderly KC patients after surgery. Information on all primary KC patients aged more than 65 years and treated with surgery between 2010 and 2015 was downloaded from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression analysis was used to identify the independent prognostic factors. Consistency index (C-index), receiver operating characteristic curve (ROC), the area under curve (AUC), and calibration curve were used to assess the accuracy and validity of the nomogram. Comparison of the clinical benefits of nomogram and the TNM staging system is done by decision curve analysis (DCA) and time-dependent ROC. A total of 15,989 elderly KC patients undergoing surgery were included. All patients were randomly divided into training set (N = 11,193, 70%) and validation set (N = 4796, 30%). The nomogram produced C-indexes of 0.771 (95% CI 0.751-0.791) and 0.792 (95% CI 0.763-0.821) in the training and validation sets, respectively, indicating that the nomogram has excellent predictive accuracy. The ROC, AUC, and calibration curves also showed the same excellent results. In addition, DCA and time-dependent ROC showed that the nomogram outperformed the TNM staging system with better net clinical benefits and predictive efficacy. Independent influencing factors for postoperative OS in elderly KC patients were sex, age, histological type, tumor size, grade, surgery, marriage, radiotherapy, and T-, N-, and M-stage. The web-based nomogram and risk stratification system could assist surgeons and patients in clinical decision-making.
Sections du résumé
BACKGROUND
BACKGROUND
Kidney cancer (KC) is one of the most common malignant tumors in adults which particularly affects the survival of elderly patients. We aimed to construct a nomogram to predict overall survival (OS) in elderly KC patients after surgery.
METHODS
METHODS
Information on all primary KC patients aged more than 65 years and treated with surgery between 2010 and 2015 was downloaded from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression analysis was used to identify the independent prognostic factors. Consistency index (C-index), receiver operating characteristic curve (ROC), the area under curve (AUC), and calibration curve were used to assess the accuracy and validity of the nomogram. Comparison of the clinical benefits of nomogram and the TNM staging system is done by decision curve analysis (DCA) and time-dependent ROC.
RESULTS
RESULTS
A total of 15,989 elderly KC patients undergoing surgery were included. All patients were randomly divided into training set (N = 11,193, 70%) and validation set (N = 4796, 30%). The nomogram produced C-indexes of 0.771 (95% CI 0.751-0.791) and 0.792 (95% CI 0.763-0.821) in the training and validation sets, respectively, indicating that the nomogram has excellent predictive accuracy. The ROC, AUC, and calibration curves also showed the same excellent results. In addition, DCA and time-dependent ROC showed that the nomogram outperformed the TNM staging system with better net clinical benefits and predictive efficacy.
CONCLUSIONS
CONCLUSIONS
Independent influencing factors for postoperative OS in elderly KC patients were sex, age, histological type, tumor size, grade, surgery, marriage, radiotherapy, and T-, N-, and M-stage. The web-based nomogram and risk stratification system could assist surgeons and patients in clinical decision-making.
Identifiants
pubmed: 37410141
doi: 10.1007/s00432-023-05072-8
pii: 10.1007/s00432-023-05072-8
doi:
Types de publication
Randomized Controlled Trial
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
11873-11889Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Références
Aizer AA, Chen M-H, McCarthy EP, Mendu ML, Koo S, Wilhite TJ et al (2013) Marital status and survival in patients with cancer. J Clin Oncol 31(31):3869–3876. https://doi.org/10.1200/jco.2013.49.6489
doi: 10.1200/jco.2013.49.6489
pubmed: 24062405
pmcid: 4878087
Altekruse SF, Dickie L, Wu X-C, Hsieh M-C, Wu M, Lee R, Delacroix S Jr (2014) Clinical and prognostic factors for renal parenchymal, pelvis, and ureter cancers in SEER registries: collaborative stage data collection system, version 2. Cancer 120(23):3826–3835. https://doi.org/10.1002/cncr.29051
doi: 10.1002/cncr.29051
pubmed: 25412394
Balachandran VP, Gonen M, Smith JJ, DeMatteo RP (2015) Nomograms in oncology: more than meets the eye. Lancet Oncol 16(4):E173–E180. https://doi.org/10.1016/s1470-2045(14)71116-7
doi: 10.1016/s1470-2045(14)71116-7
pubmed: 25846097
pmcid: 4465353
Beckendorf V, Bladou F, Farsi F, Kaemmerlen P, Negrier S, Philip T, Terrier-Lacombe MJ (2000) Standards, options, and recommendations for radiotherapy of kidney cancer. [Standards, Options et Recommandations pour la radiotherapie du cancer du rein]. Cancer Radiotherapie 4(3):223–233. https://doi.org/10.1016/s1278-3218(00)89098-8
doi: 10.1016/s1278-3218(00)89098-8
pubmed: 10897766
Camp RL, Dolled-Filhart M, Rimm DL (2004) X-tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization. Clin Cancer Res 10(21):7252–7259. https://doi.org/10.1158/1078-0432.Ccr-04-0713
doi: 10.1158/1078-0432.Ccr-04-0713
pubmed: 15534099
Chen DYT, Uzzo RG (2009) Optimal management of localized renal cell carcinoma: surgery, ablation, or active surveillance. J Natl Compr Canc Netw 7(6):635–643. https://doi.org/10.6004/jnccn.2009.0044
doi: 10.6004/jnccn.2009.0044
pubmed: 19555585
pmcid: 2759676
Choi YD, Kim KS, Ryu S, Park Y, Cho NH, Rha SH et al (2007) Claudin-7 is highly expressed in chromophobe renal cell carcinoma and renal oncocytoma. J Korean Med Sci 22(2):305–310. https://doi.org/10.3346/jkms.2007.22.2.305
doi: 10.3346/jkms.2007.22.2.305
pubmed: 17449941
pmcid: 2693599
Chow W-H, Dong LM, Devesa SS (2010) Epidemiology and risk factors for kidney cancer. Nat Rev Urol 7(5):245–257. https://doi.org/10.1038/nrurol.2010.46
doi: 10.1038/nrurol.2010.46
pubmed: 20448658
pmcid: 3012455
Cohen HT, McGovern FJ (2005) Renal-cell carcinoma. N Engl J Med 353(23):2477–2490. https://doi.org/10.1056/NEJMra043172
doi: 10.1056/NEJMra043172
pubmed: 16339096
Hakky TS, Baumgarten AS, Allen B, Lin H-Y, Ercole CE, Sexton WJ, Spiess PE (2014) Zonal NePhRO scoring system: a superior renal tumor complexity classification model. Clin Genitourin Cancer 12(1):E13–E18. https://doi.org/10.1016/j.clgc.2013.07.009
doi: 10.1016/j.clgc.2013.07.009
pubmed: 24120084
Hollingsworth JM, Miller DC, Daignault S, Hollenbeck BK (2007) Five-year survival after surgical treatment for kidney cancer—a population-based competing risk analysis. Cancer 109(9):1763–1768. https://doi.org/10.1002/cncr.22600
doi: 10.1002/cncr.22600
pubmed: 17351954
Hsieh JJ, Purdue MP, Signoretti S, Swanton C, Albiges L, Schmidinger M et al (2017) Renal cell carcinoma. Nat Rev Dis Primers. https://doi.org/10.1038/nrdp.2017.9
doi: 10.1038/nrdp.2017.9
pubmed: 28276433
pmcid: 5936048
Hu Y, Qi Q, Zheng Y, Wang H, Zhou J, Hao Z et al (2022) Nomogram for predicting the overall survival of patients with early-onset prostate cancer: a population-based retrospective study. Cancer Med 11(17):3260–3271. https://doi.org/10.1002/cam4.4694
doi: 10.1002/cam4.4694
pubmed: 35322943
pmcid: 9468440
Huang WC, Elkin EB, Levey AS, Jang TL, Russo P (2009) Partial nephrectomy versus radical nephrectomy in patients with small renal tumors-is there a difference in mortality and cardiovascular outcomes? J Urol 181(1):55–61. https://doi.org/10.1016/j.juro.2008.09.017
doi: 10.1016/j.juro.2008.09.017
pubmed: 19012918
Huang X, Shu C, Chen L, Yao B (2018) Impact of sex, body mass index and initial pathologic diagnosis age on the incidence and prognosis of different types of cancer. Oncol Rep 40(3):1359–1369. https://doi.org/10.3892/or.2018.6529
doi: 10.3892/or.2018.6529
pubmed: 29956810
pmcid: 6072401
Kanesvaran R, Le Saux O, Motzer R, Choueiri TK, Scotte F, Bellmunt J, Launay-Vacher V (2018) Elderly patients with metastatic renal cell carcinoma: position paper from the International Society of Geriatric Oncology. Lancet Oncol 19(6):E317–E326. https://doi.org/10.1016/s1470-2045(18)30125-6
doi: 10.1016/s1470-2045(18)30125-6
pubmed: 29893263
Kates M, Badalato G, Pitman M, McKiernan J (2011) Persistent overuse of radical nephrectomy in the elderly. Urology 78(3):555–559. https://doi.org/10.1016/j.urology.2011.02.066
doi: 10.1016/j.urology.2011.02.066
pubmed: 21777962
Kim HL, Han KR, Zisman A, Figlin RA, Belldegrun AS (2004) Cachexia-like symptoms predict a worse prognosis in localized T1 renal cell carcinoma. J Urol 171(5):1810–1813. https://doi.org/10.1097/01.ju.0000121440.82581.d3
doi: 10.1097/01.ju.0000121440.82581.d3
pubmed: 15076282
Klatte T, Ficarra V, Gratzke C, Kaouk J, Kutikov A, Macchi V et al (2015) A literature review of renal surgical anatomy and surgical strategies for partial nephrectomy. Eur Urol 68(6):980–992. https://doi.org/10.1016/j.eururo.2015.04.010
doi: 10.1016/j.eururo.2015.04.010
pubmed: 25911061
pmcid: 4994971
Lee K-H, Kim B-C, Jeong S-H, Jeong CW, Ku JH, Kwak C, Kim HH (2020) Histone demethylase LSD1 regulates kidney cancer progression by modulating androgen receptor activity. Int J Mol Sci. https://doi.org/10.3390/ijms21176089
doi: 10.3390/ijms21176089
pubmed: 33396939
pmcid: 7795533
Levi F, Ferlay J, Galeone C, Lucchini F, Negri E, Boyle P, La Vecchia C (2008) The changing pattern of kidney cancer incidence and mortality in Europe. BJU Int 101(8):949–958. https://doi.org/10.1111/j.1464-410X.2008.07451.x
doi: 10.1111/j.1464-410X.2008.07451.x
pubmed: 18241251
Li Y, Gong Y, Ning X, Peng D, Liu L, He S et al (2018) Downregulation of CLDN7 due to promoter hypermethylation is associated with human clear cell renal cell carcinoma progression and poor prognosis. J Exp Clin Cancer Res. https://doi.org/10.1186/s13046-018-0924-y
doi: 10.1186/s13046-018-0924-y
pubmed: 30591064
pmcid: 6307166
Li J, Yang S, Li Y, Li C, Xia Y, Zhu S, Xia J (2022) The C-reactive protein to albumin ratio is an independent prognostic factor in patients with hepatocellular carcinoma undergoing transarterial chemoembolization: a large cohort study. Cardiovasc Intervent Radiol 45(9):1295–1303. https://doi.org/10.1007/s00270-022-03208-w
doi: 10.1007/s00270-022-03208-w
pubmed: 35835873
Liu X, Yue S, Huang H, Duan M, Zhao B, Liu J, Xiang T (2021) Risk stratification model for predicting the overall survival of elderly triple-negative breast cancer patients: a population-based study. Front Med. https://doi.org/10.3389/fmed.2021.705515
doi: 10.3389/fmed.2021.705515
pubmed: 34962624
pmcid: 8976706
Ljungberg B, Albiges L, Abu-Ghanem Y, Bensalan K, Dabestani S, Montes SF-P et al (2019) European association of urology guidelines on renal cell carcinoma: the 2019 update. Eur Urol 75(5):799–810. https://doi.org/10.1016/j.eururo.2019.02.011
doi: 10.1016/j.eururo.2019.02.011
pubmed: 30803729
Makhov P, Joshi S, Ghatalia P, Kutikov A, Uzzo RG, Kolenko VM (2018) Resistance to systemic therapies in clear cell renal cell carcinoma: mechanisms and management strategies. Mol Cancer Ther 17(7):1355–1364. https://doi.org/10.1158/1535-7163.Mct-17-1299
doi: 10.1158/1535-7163.Mct-17-1299
pubmed: 29967214
pmcid: 6034114
Mazzone E, Nazzani S, Preisser F, Tian Z, Marchioni M, Bandini M et al (2018) Partial nephrectomy seems to confer a survival benefit relative to radical nephrectomy in metastatic renal cell carcinoma. Cancer Epidemiol 56:118–125. https://doi.org/10.1016/j.canep.2018.08.006
doi: 10.1016/j.canep.2018.08.006
pubmed: 30173050
Merrill RM, Johnson E (2017) Benefits of marriage on relative and conditional relative cancer survival differ between males and females in the USA. J Cancer Surviv 11(5):578–589. https://doi.org/10.1007/s11764-017-0627-y
doi: 10.1007/s11764-017-0627-y
pubmed: 28770444
Motzer RJ, Jonasch E, Agarwal N, Alva A, Baine M, Beckermann K et al (2022) Kidney cancer, version 3.2022. J Natl Comprehensive Cancer Network 20(1):71–89. https://doi.org/10.6004/jnccn.2022.0001
doi: 10.6004/jnccn.2022.0001
Nakagawa S, Cuthill IC (2007) Effect size, confidence interval and statistical significance: a practical guide for biologists. Biol Rev 82(4):591–605. https://doi.org/10.1111/j.1469-185X.2007.00027.x
doi: 10.1111/j.1469-185X.2007.00027.x
pubmed: 17944619
Nazzani S, Mazzone E, Preisser F, Tian Z, Mistretta FA, Shariat SF et al (2019) Rates of lymph node invasion and their impact on cancer specific mortality in upper urinary tract urothelial carcinoma. Ejso 45(7):1238–1245. https://doi.org/10.1016/j.ejso.2018.12.004
doi: 10.1016/j.ejso.2018.12.004
pubmed: 30563773
Payedimarri AB, Concina D, Portinale L, Canonico M, Seys D, Vanhaecht K, Panella M (2021) Prediction models for public health containment measures on COVID-19 using artificial intelligence and machine learning: a systematic review. Int J Environ Res Public Health. https://doi.org/10.3390/ijerph18094499
doi: 10.3390/ijerph18094499
pubmed: 34770107
pmcid: 8582978
Peyton CC, Rothberg MB, Jiang V, Heavner MG, Hemal AK (2017) Comparative analysis of renal functional outcomes and overall survival of elderly vs nonelderly patients undergoing radical nephrectomy. J Endourol 31(2):198–203. https://doi.org/10.1089/end.2016.0525
doi: 10.1089/end.2016.0525
pubmed: 27881019
Quivy A, Daste A, Harbaoui A, Duc S, Bernhard J-C, Gross-Goupil M, Ravaud A (2013) Optimal management of renal cell carcinoma in the elderly: a review. Clin Interv Aging 8:433–442. https://doi.org/10.2147/cia.S30765
doi: 10.2147/cia.S30765
pubmed: 23626463
pmcid: 3632583
Ristau BT, Handorf EA, Cahn DB, Kutikov A, Uzzo RG, Smaldone MC (2018) Partial Nephrectomy is not associated with an overall survival advantage over radical nephrectomy in elderly patients with stage Ib-II renal masses: an analysis of the national cancer data base. Cancer 124(19):3839–3848. https://doi.org/10.1002/cncr.31582
doi: 10.1002/cncr.31582
pubmed: 30207380
Rudnicka E, Napierala P, Podfigurna A, Meczekalski B, Smolarczyk R, Grymowicz M (2020) The World Health Organization (WHO) approach to healthy ageing. Maturitas 139:6–11. https://doi.org/10.1016/j.maturitas.2020.05.018
doi: 10.1016/j.maturitas.2020.05.018
pubmed: 32747042
pmcid: 7250103
Schmitges J, Trinh Q-D, Sun M, Hansen J, Bianchi M, Jeldres C et al (2012) Higher perioperative morbidity and in-hospital mortality in patients with end-stage renal disease undergoing nephrectomy for non-metastatic kidney cancer: a population-based analysis. BJU Int 110(6B):E183–E190. https://doi.org/10.1111/j.1464-410X.2012.10936.x
doi: 10.1111/j.1464-410X.2012.10936.x
pubmed: 22321256
Schober P, Vetter TR (2019) Chi-square tests in medical research. Anesth Analg 129(5):1193–1193. https://doi.org/10.1213/ANE.0000000000004410
doi: 10.1213/ANE.0000000000004410
pubmed: 31613806
Shum CF, Bahler CD, Sundaram CP (2017) Matched comparison between partial nephrectomy and radical nephrectomy for T2 N0 M0 tumors, a study based on the national cancer database. J Endourol 31(8):800–805. https://doi.org/10.1089/end.2017.0190
doi: 10.1089/end.2017.0190
pubmed: 28486848
Siegel RL, Miller KD, Fuchs HE, Jemal A (2021) Cancer statistics, 2021. CA-A Cancer J Clin 71(1):7–33. https://doi.org/10.3322/caac.21654
doi: 10.3322/caac.21654
Tahbaz R, Schmid M, Merseburger AS (2018) Prevention of kidney cancer incidence and recurrence: lifestyle, medication and nutrition. Curr Opin Urol 28(1):62–79. https://doi.org/10.1097/mou.0000000000000454
doi: 10.1097/mou.0000000000000454
pubmed: 29059103
Tan H-J, Chamie K, Daskivich TJ, Litwin MS, Hu JC (2016) Patient function, long-term survival, and use of surgery in patients with kidney cancer. Cancer 122(24):3776–3784. https://doi.org/10.1002/cncr.30275
doi: 10.1002/cncr.30275
pubmed: 27518165
Thoenes W, Storkel S, Rumpelt HJ (1986) Histopathology and classification of renal cell tumors (adenomas, oncocytomas and carcinomas). The basic cytological and histopathological elements and their use for diagnostics. Pathol Res Pract 181(2):125–143. https://doi.org/10.1016/s0344-0338(86)80001-2
doi: 10.1016/s0344-0338(86)80001-2
pubmed: 3737468
Thompson RH, Boorjian SA, Lohse CM, Leibovich BC, Kwon ED, Cheville JC, Blute ML (2008) Radical nephrectomy for pT1a renal masses may be associated with decreased overall survival compared with partial nephrectomy. J Urol 179(2):468–471. https://doi.org/10.1016/j.juro.2007.09.077
doi: 10.1016/j.juro.2007.09.077
pubmed: 18076931
Tian S, Sun S, Mao W, Zhang L, Zhang G, Xu B, Chen M (2021) Development and validation of prognostic nomogram for young patients with kidney cancer. Int J General Med 14:5091–5103. https://doi.org/10.2147/ijgm.S331627
doi: 10.2147/ijgm.S331627
Tripepi G, Jager KJ, Dekker FW, Zoccali C (2010) Selection bias and information bias in clinical research. Nephron Clin Pract 115(2):C94–C99. https://doi.org/10.1159/000312871
doi: 10.1159/000312871
pubmed: 20407272
Volpe A, Kachura JR, Geddie WR, Evans AJ, Gharajeh A, Saravanan A, Jewett MAS (2007) Techniques, safety and accuracy of sampling of renal tumors by fine needle aspiration and core biopsy. J Urol 178(2):379–386. https://doi.org/10.1016/j.juro.2007.03.131
doi: 10.1016/j.juro.2007.03.131
pubmed: 17561170
Wang W, Liu J, Liu L (2021) Development and validation of a prognostic model for predicting overall survival in patients with bladder cancer: a SEER-based study. Front Oncol. https://doi.org/10.3389/fonc.2021.692728
doi: 10.3389/fonc.2021.692728
pubmed: 35769548
pmcid: 8739965
Yan F, Wang Q, Xia M, Ru Y, Hu W, Yan G et al (2021) MIIP inhibits clear cell renal cell carcinoma proliferation and angiogenesis via negative modulation of the HIF-2 alpha-CYR61 axis. Cancer Biol Med. https://doi.org/10.20892/j.issn.2095-3941.2020.0296
doi: 10.20892/j.issn.2095-3941.2020.0296
pubmed: 34931765
pmcid: 9257321
Yang J, Li Y, Liu Q, Li L, Feng A, Wang T et al (2020) Brief introduction of medical database and data mining technology in big data era. J Evid Based Med 13(1):57–69. https://doi.org/10.1111/jebm.12373
doi: 10.1111/jebm.12373
pubmed: 32086994
pmcid: 7065247
Yang Z, Zi Q, Xu K, Wang C, Chi Q (2021) Development of a macrophages-related 4-gene signature and nomogram for the overall survival prediction of hepatocellular carcinoma based on WGCNA and LASSO algorithm. Int Immunopharmacol. https://doi.org/10.1016/j.intimp.2020.107238
doi: 10.1016/j.intimp.2020.107238
pubmed: 34955407
pmcid: 8608623
Zhanghuang C, Wang J, Yao Z, Li L, Xie Y, Tang H et al (2022) Development and validation of a nomogram to predict cancer-specific survival in elderly patients with papillary renal cell carcinoma. Front Public Health. https://doi.org/10.3389/fpubh.2022.874427
doi: 10.3389/fpubh.2022.874427
pubmed: 36330113
pmcid: 9624381
Zhou Z-R, Wang W-W, Li Y, Jin K-R, Wang X-Y, Wang Z-W et al (2019) In-depth mining of clinical data: the construction of clinical prediction model with R. Ann Transl Med. https://doi.org/10.21037/atm.2019.08.63
doi: 10.21037/atm.2019.08.63
pubmed: 37090053
pmcid: 10116425
Zhou Y, Zhang R, Ding Y, Wang Z, Yang C, Tao S, Liang C (2020) Prognostic nomograms and Aggtrmmns scoring system for predicting overall survival and cancer-specific survival of patients with kidney cancer. Cancer Med 9(8):2710–2722. https://doi.org/10.1002/cam4.2916
doi: 10.1002/cam4.2916
pubmed: 32087609
pmcid: 7163106