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Related Concept Videos

Cancer Survival Analysis01:21

Cancer Survival Analysis

542
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
542
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

279
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
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Survival Tree01:19

Survival Tree

280
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
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Truncation in Survival Analysis01:09

Truncation in Survival Analysis

427
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
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Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

423
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
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Censoring Survival Data01:09

Censoring Survival Data

395
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
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Related Experiment Video

Updated: Nov 30, 2025

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
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Published on: September 27, 2024

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TCox: Correlation-Based Regularization Applied to Colorectal Cancer Survival Data.

Carolina Peixoto1, Marta B Lopes2,3, Marta Martins4

  • 1INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Rua Alves Redol 9, 1000-029 Lisboa, Portugal.

Biomedicines
|November 13, 2020
PubMed
Summary

TCox, a novel method for colorectal cancer (CRC) research, identifies key genes by analyzing distinct correlation patterns in normal and tumor tissues. This approach aids in discovering new molecular drivers for improved CRC patient prognosis and treatment strategies.

Keywords:
Cox regressionRNA-seq dataTCGA dataregularized optimizationsurvival analysis

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Area of Science:

  • Oncology
  • Bioinformatics
  • Genomics

Background:

  • Colorectal cancer (CRC) presents significant challenges in therapy and prognosis due to its heterogeneous nature.
  • Molecular data aids in patient classification and treatment but high-dimensional gene expression data complicates novel gene selection.
  • Identifying reliable biomarkers is crucial for precision oncology and improving patient outcomes.

Purpose of the Study:

  • To introduce TCox, a novel penalization function for Cox models designed to select genes with distinct correlation patterns in normal versus tumor tissues.
  • To evaluate the efficacy of TCox against other regularized survival models, including Elastic Net, HubCox, and OrphanCox.
  • To demonstrate the utility of TCox in identifying novel molecular drivers for colorectal cancer survival.

Main Methods:

  • Development and application of TCox, a weighted regularizer, for gene selection in colorectal cancer.
  • Comparison of TCox with Elastic Net, HubCox, and OrphanCox using gene expression and clinical data from The Cancer Genome Atlas (TCGA).
  • Rigorous model evaluation through 100 repeated tests to ensure robustness and reliability of results.

Main Results:

  • TCox successfully identified crucial genes involved in colorectal cancer survival, with 18 overlapping features selected by other models.
  • The TCox model uniquely selected genes capable of stratifying patients into distinct risk groups.
  • The study highlighted the importance of network information in identifying biomarkers from high-dimensional gene expression data.

Conclusions:

  • TCox effectively identifies novel molecular drivers of CRC survival by leveraging correlation-based network information from both tumor and normal tissues.
  • Network information is highly relevant for biomarker discovery in high-dimensional gene expression data.
  • The findings encourage the development of network-based feature selection methods for precision oncology.