Loading…

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

Background To develop a prognostic model to manage patients with muscle-invasive bladder cancer (MIBC) undergoing radical cystectomy (RC) and chemotherapy. Patients and Methods Clinicopathologic characteristics and survival data were collated from a North American database to develop a model. Genomi...

Full description

Saved in:
Bibliographic Details
Published in:Annals of surgical oncology 2024-12, Vol.31 (13), p.8967-8977
Main Authors: Lai, Shicong, Liu, Jianyong, Hu, Haopu, Song, Yuxuan, Seery, Samuel, Ni, Runfeng, Wang, Huanrui, Zhang, Guan, Hu, Hao, Xu, Tao
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Background To develop a prognostic model to manage patients with muscle-invasive bladder cancer (MIBC) undergoing radical cystectomy (RC) and chemotherapy. Patients and Methods Clinicopathologic 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. Results There 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). Conclusions This MIBC type-based nomogram provides accurate CSS predictions, which could help improve patient management and clinical decision-making.
ISSN:1068-9265
1534-4681
1534-4681
DOI:10.1245/s10434-024-16226-4