Evidence map›Paper›PMID 39153102›Full record

ArticleClinical and experimental medicine2024

Impact of tumor multiplicity on the prognosis of patients with primary renal cell carcinoma: a SEER database analysis.

Tianyue Yang, Hongfeng Zheng, Shaojun Chen, Min Gong, Yifan Liu, Wang Zhou, Jianqing Ye, Xiuwu Pan, Xingang Cui

Abstract read
In one paragraph

Article in Clinical and experimental medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Tianyue Yang *Department of Urology, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, 1665 Kongjiang Road, Yangpu District, Shanghai, 200092, China.
Hongfeng ZhengDepartment of Urology, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, 1665 Kongjiang Road, Yangpu District, Shanghai, 200092, China.
Shaojun ChenDepartment of Urology, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, 1665 Kongjiang Road, Yangpu District, Shanghai, 200092, China.
Min Gong *Department of Urology, Seventh People's Hospital of Shanghai University of Traditional Chinese Medicine, Shanghai, 200137, China.
Yifan LiuDepartment of Urology, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, 1665 Kongjiang Road, Yangpu District, Shanghai, 200092, China.
Wang ZhouDepartment of Urology, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, 1665 Kongjiang Road, Yangpu District, Shanghai, 200092, China.
Jianqing YeDepartment of Urology, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, 1665 Kongjiang Road, Yangpu District, Shanghai, 200092, China. ye910@126.com.
Xiuwu PanDepartment of Urology, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, 1665 Kongjiang Road, Yangpu District, Shanghai, 200092, China. panxiuwu@126.com.
Xingang CuiDepartment of Urology, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, 1665 Kongjiang Road, Yangpu District, Shanghai, 200092, China. cuixingang@xinhuamed.com.cn.ORCID http://orcid.org/0000-0002-0920-9439

Funding

Hospital Funded Clinical Research, Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine 21XHDB06National Natural Science Foundation of China 82072806National Natural Science Foundation of China 82173265Natural Science Foundation of Shanghai Municipality 23ZR1441300Shanghai Municipal Health and Family Planning Commission 20204Y0042Shanghai Municipal Human Resources Development Program for Outstanding Leaders in Medical Disciplines 2022LJ002Shanghai Rising-Star Program 23QC1401400
6 · The paper itself

Abstract

To compare clinical characteristics and survival outcomes of patients with multiple renal cell carcinoma versus single renal cell carcinoma. Develop a prognostic model for predicting prognosis in patients with multiple tumors and analyze prognostic factors. Patients with primary multiple renal cell carcinoma were selected from the Surveillance, Epidemiology, and End Results database (2004-2015). They were divided into single-tumor and multiple-tumor groups. Survival analysis was conducted using the Kaplan-Meier method and log-rank test. A Cox regression model was used to identify potential prognostic factors. A total of 19,489 renal cell carcinoma cases were included, with 947 in the multiple-tumor group and 18,542 in the single-tumor group. The multiple-tumor group had lower cancer-specific survival (P = 0.03, HR = 1.431). Cox regression identified risk factors for the multiple-tumor group including number of tumors, gender, combined summary stage, T stage, N stage, tumor size, and type of surgery. The predicted probabilities showed acceptable agreement with the actual observations at 3-, 5-, and 8-years area under the curve values in both the training and validation cohorts (0.831 vs. 0.605; 0.775 vs. 0.672; and 0.797 vs. 0.699, respectively). Compared with single renal cell carcinoma, multiple renal cell carcinoma is associated with decreased cancer-specific survival. Additionally, we identified several prognostic factors including the number of tumors, T stage, tumor size, and type of surgery. These findings offer valuable insights for selecting appropriate treatment strategies for patients diagnosed with multiple renal cell carcinomas.

Indexed as

Carcinoma, Renal CellKidney NeoplasmsSEER ProgramAdultAgedAged, 80 and overFemaleHumansKaplan-Meier EstimateMaleMiddle AgedNeoplasms, Multiple PrimaryNeoplasm StagingPrognosisProportional Hazards ModelsRisk FactorsNomogramsRenal cell carcinomaRisk factorsSEER programSurvival analysis

Identifiers

PMID39153102
PMCPMC11330414

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.