Evidence map›Paper›PMID 39390388›Full record

ArticleBMC cancer2024

Prognostic factors and prognostic model of non-metastatic clear cell renal cell carcinoma.

Juanyan Liao, Shuang Zhang, Zhenyu Ding

Abstract read
In one paragraph

Article in BMC cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

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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

6 citing papers in PubMed.

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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

3 authors.

Juanyan LiaoDepartment of Biotherapy, West China Hospital, Sichuan University, 37 Guoxue Lane, Wuhou District, Chengdu, Sichuan, 610041, China.
Shuang ZhangDepartment of Biotherapy, West China Hospital, Sichuan University, 37 Guoxue Lane, Wuhou District, Chengdu, Sichuan, 610041, China. shuang.zhang@scu.edu.cn.
Zhenyu DingDepartment of Biotherapy, West China Hospital, Sichuan University, 37 Guoxue Lane, Wuhou District, Chengdu, Sichuan, 610041, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionAlthough there are some established prognostic evaluation models for clear cell renal cell carcinoma (ccRCC), more robust postoperative prognostic evaluation model is urgently needed. Our study intends to explore new clinical and pathological prognostic factors related to non-metastatic ccRCC, which help to establish a better prognostic risk evaluation model in non-metastatic ccRCC. PATIENTS AND

methodsA retrospective cohort study was conducted in non-metastatic ccRCC patients spanning from 2010 to 2018. Clinical and pathological factors of these patients were collected. Cox regression analysis was employed to assess the relationship between these factors and disease-free survival (DFS), and a nomogram risk prediction model was also constructed.

resultsA total of 1467 patients were ultimately included, comprising 994 men (67.8%), with 800 patients aged between 40 and 60 years old (54.5%), and 80 patients (5.5%) experiencing relapse or metastasis of ccRCC within three years after operation. The follow-up duration ranged from 39 to 146 months. Univariate and multivariate Cox regression analysis identified five independent prognostic factors of DFS (P < 0.05) including sex, tumor maximum diameter, T stage, lactate dehydrogenase (LDH), and basophils. Leveraging these five factors, we established a prognostic evaluation model demonstrating good predictive efficacy.

conclusionMale, tumor maximum diameter, T stage, LDH, and basophils serve as prognostic indicators for DFS in patients with non-metastatic ccRCC. Patients with high scores based on our model exhibit an elevated likelihood of recurrence or metastasis, thereby potentially selecting postoperative patients with high risk for adjuvant therapy.

Indexed as

Carcinoma, Renal CellKidney NeoplasmsNomogramsAdultAgedDisease-Free SurvivalFemaleHumansMaleMiddle AgedNeoplasm Recurrence, LocalNeoplasm StagingPrognosisRetrospective StudiesBasophilsLDHNon-metastatic ccRCCPrognostic factorsPrognostic model

Identifiers

PMID39390388
PMCPMC11468267

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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.