Evidence map›Paper›PMID 39284988›Full record

ArticleAnnals of surgical oncology2024

Developing a Novel Prognostic Model Based on Muscle-Invasive Bladder Cancer Types: A Multicenter Retrospective Cohort Study of Patients Who Received Radical Cystectomy and Chemotherapy.

Shicong Lai, Jianyong Liu, Haopu Hu, Yuxuan Song, Samuel Seery, Runfeng Ni, Huanrui Wang, Guan Zhang, Hao Hu, Tao Xu

Abstract readMulticenter Study
PubMed Publisher
In one paragraph

Article in Annals of surgical oncology, 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. Review
  2. Article
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

10 authors.

Shicong Lai *Department of Urology, Peking University People's Hospital, Beijing, China.
Jianyong Liu *Department of Urology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China.
Haopu HuDepartment of Urology, Peking University People's Hospital, Beijing, China.
Yuxuan SongDepartment of Urology, Peking University People's Hospital, Beijing, China.
Samuel SeerySchool of Pharmacy, Newcastle University, Newcastle upon Tyne, UK.
Runfeng NiDepartment of Urology, Peking University People's Hospital, Beijing, China.
Huanrui WangDepartment of Urology, Peking University People's Hospital, Beijing, China.
Guan ZhangDepartment of Urology, China-Japan Friendship Hospital, Beijing, China. gzhang2016@sina.com.
Hao HuDepartment of Urology, Peking University People's Hospital, Beijing, China. huhao@bjmu.edu.cn.
Tao XuDepartment of Urology, Peking University People's Hospital, Beijing, China. xutao@pkuph.edu.cn.

Funding

Peking University Medicine Sailing Program for Young Scholars' Scientific & Technological Innovation and the Fundamental Research Funds for the Central Universities EBS number 2127000305Peking University Medicine Sailing Program for Young Scholars' Scientific & Technological Innovation and the Fundamental Research Funds for the Central Universities Grant BMU2023YFJHPY009Peking University People's Hospital Research And Development Funds RDJP2022-69
6 · The paper itself

Abstract

backgroundTo develop a prognostic model to manage patients with muscle-invasive bladder cancer (MIBC) undergoing radical cystectomy (RC) and chemotherapy. PATIENTS AND

methodsClinicopathologic characteristics and survival data were collated from a North American database to develop a model. Genomic and clinicopathologic data were also obtained from European and Asian databases to externally validate the model. Patients were classified as either "primary" or "progressive" MIBC according to non-muscle invasive stage history. Optimized cancer-specific survival (CSS) models, based on MIBC types, were constructed using Cox's proportional hazard regression. Differences of biological function and tumor immunity, between two risk-based groups stratified according to the prognostic model, were estimated.

resultsThere were 2631 participants in the American cohort, 291 in the European cohort and 142 in the Asian cohort. Under Cox's regression analysis, tumor stage, lymph node stage, age, ethnicity, and MIBC types were independent CSS predictors (all p < 0.05). The constructed nomogram, which integrated these variables, improved the predictive power. This model had good discrimination and calibration. Patients were categorized into high- or low-risk groups according to the total points calculated. Kaplan-Meier curves revealed that patients in the high-risk group had poorer survival (p < 0.001). This was confirmed with two external validation cohorts (both with p < 0.001). Higher stromal scores and increased M0 and M2 macrophage numbers were observed in samples from the high-risk group, whereas regulatory T cells had lower infiltration in these populations (all with p < 0.05).

conclusionsThis MIBC type-based nomogram provides accurate CSS predictions, which could help improve patient management and clinical decision-making.

Indexed as

CystectomyNeoplasm InvasivenessNeoplasm StagingNomogramsUrinary Bladder NeoplasmsAgedAntineoplastic Combined Chemotherapy ProtocolsCarcinoma, Transitional CellCombined Modality TherapyFemaleFollow-Up StudiesHumansMaleMiddle AgedPrognosisRetrospective StudiesPrimary muscle-invasive bladder cancerPrognosesPrognostic modelProgressive muscle-invasive bladder cancerRadical cystectomy

Identifiers

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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 Socratic graph.