Objective: To create multivariable models with readily available clinicopathologic variables for predicting the prognosis of upper tract urothelial carcinomas (UTUC).

Patients And Methods: We retrospectively analyzed patients diagnosed as UTUC and underwent radical nephroureterectomy in 2 high volumes, tertiary care centers. A total of 445 patients and 227 patients met the inclusion criteria were included for constructing the prediction model and external validation, respectively. Univariable and multivariable Cox regression models were used to analyze independent risk factors, and nomogram and calibration curve were constructed by R project.

Results: The median follow-up for the development and external validation cohorts were 33.5 and 32.5 months, respectively. Multivariable analysis detected older age (≥65 years), with concurrent bladder cancer at diagnosis, with both ureter and renal pelvic tumor, lymphovascular invasion, urothelial carcinoma with divergent differentiation, higher pathological grade and stage, and positive lymph node were significantly associated with poorer outcome of UTUC. The c-index of the nomogram with these above-mentioned independent risk factors to predict the cancer specific survival was 0.74 (95% CI, 0.64-0.84) and 0.73 (95%CI, 0.59-0.87) for the development cohort and external validation cohort, respectively.

Conclusions: We developed and externally validated a novel and accurate nomogram with readily available clinicopathological information for predicting the cancer specific survival of UTUC. This nomogram could help clinicians stratify patients with UTUC into different risk groups with distinct prognosis by the total scores obtained from the prediction tool, thus facilitate decision-making and clinical trial designing.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.urolonc.2018.12.027DOI Listing

Publication Analysis

Top Keywords

external validation
16
development external
8
predicting prognosis
8
prognosis upper
8
upper tract
8
tract urothelial
8
urothelial carcinoma
8
radical nephroureterectomy
8
independent risk
8
risk factors
8

Similar Publications

COLOFIT: Development and Internal-External Validation of Models Using Age, Sex, Faecal Immunochemical and Blood Tests to Optimise Diagnosis of Colorectal Cancer in Symptomatic Patients.

Aliment Pharmacol Ther

January 2025

Gastrointestinal and Liver Theme, National Institute for Health Research (NIHR) Nottingham Biomedical Research Centre (BRC), Nottingham University Hospitals NHS Trust and the University of Nottingham, School of Medicine, Queen's Medical Centre, Nottingham, UK.

Background: Colorectal cancer (CRC) is the third most common cancer in the United Kingdom and the second largest cause of cancer death.

Aim: To develop and validate a model using available information at the time of faecal immunochemical testing (FIT) in primary care to improve selection of symptomatic patients for CRC investigations.

Methods: We included all adults (≥ 18 years) referred to Nottingham University Hospitals NHS Trust between 2018 and 2022 with symptoms of suspected CRC who had a FIT.

View Article and Find Full Text PDF

Extension of an ICU-based noninvasive model to predict latent shock in the emergency department: an exploratory study.

Front Cardiovasc Med

December 2024

Emergency Center, Hubei Clinical Research Center for Emergency and Resuscitaion, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China.

Background: Artificial intelligence (AI) has been widely adopted for the prediction of latent shock occurrence in critically ill patients in intensive care units (ICUs). However, the usefulness of an ICU-based model to predict latent shock risk in an emergency department (ED) setting remains unclear. This study aimed to develop an AI model to predict latent shock risk in patients admitted to EDs.

View Article and Find Full Text PDF

Understanding cellular responses to external stimuli is critical for parsing biological mechanisms and advancing therapeutic development. High-content image-based assays provide a cost-effective approach to examine cellular phenotypes induced by diverse interventions, which offers valuable insights into biological processes and cellular states. In this paper, we introduce MorphoDiff, a generative pipeline to predict high-resolution cell morphological responses under different conditions based on perturbation encoding.

View Article and Find Full Text PDF

Microbes of nearly every species can form biofilms, communities of cells bound together by a self-produced matrix. It is not understood how variation at the cellular level impacts putatively beneficial, colony-level behaviors, such as cell-to-cell signaling. Here we investigate this problem with an agent-based computational model of metabolically driven electrochemical signaling in Bacillus subtilis biofilms.

View Article and Find Full Text PDF

Purpose: Sepsis-associated liver injury (SALI) leads to increased mortality in sepsis patients, yet no specialized tools exist for early risk assessment. This study aimed to develop and validate a risk prediction model for early identification of SALI before patients meet full diagnostic criteria.

Patients And Methods: This retrospective study analyzed 415 sepsis patients admitted to ICU from January 2019 to January 2022.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!