Nomogram predicting cancer-specific mortality in patients with esophageal adenocarcinoma: a competing risk analysis

Xi-Xi Wu1, Ren-Pin Chen1, Rui-Cong Chen2

  • 1Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China.

Abstract

Related Concept Videos

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

The study found that male gender, poor tumor grade, and advanced Tumor Node Metastasis stage were associated with poorer cancer-specific survival (CSS) in multiple primary colorectal cancer (MPCC) patients after surgery. We developed a nomogram to predict the CSS of MPCC patients and contribute to clinical treatment...
650
Establishing a Competing Risk Regression Nomogram Model for Survival Data04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Presented here is a protocol to build nomograms based on the Cox proportional hazards regression model and competing risk regression model. The competing method is a more rational method to apply when competing events are present in the survival...
10.8K
An R-Based Landscape Validation of a Competing Risk Model05:37

An R-Based Landscape Validation of a Competing Risk Model

The present protocol describes codes in R for evaluating the discrimination and calibration abilities of a competing risk model, as well as codes for the internal and external validation of...
2.5K
Subculture and Cryopreservation of Esophageal Adenocarcinoma Organoids: Pros and Cons for Single Cell Digestion10:42

Subculture and Cryopreservation of Esophageal Adenocarcinoma Organoids: Pros and Cons for Single Cell Digestion

This protocol describes the methods of subculture and cryopreservation of esophageal adenocarcinoma organoids with and without single cell digestion to enable researchers to choose appropriate strategies based on their experimental...
4.2K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

This study evaluates prognostic systems for colorectal signet-ring cell carcinoma patients using machine learning models and competing risk analyses. It identifies log odds of positive lymph nodes as a superior predictor compared to pN staging, demonstrating strong predictive performance and aiding clinical decision-making through robust survival prediction...
495
A New Technique for Treating Low-risk Prostate Cancer—Super Active Surveillance05:19

A New Technique for Treating Low-risk Prostate Cancer—Super Active Surveillance

Overtreatment is often administered to patients with low-risk prostate cancer; super-active surveillance can help avoid such...
687