Related Experiment Video

Updated: Jun 9, 2025

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

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

Published on: September 27, 2024

223

Relapse and survival after relapse among children with cancer in Denmark: 2001-2021

Mie Mølgaard Andersen1,2, Marie Christine Lundius Sørensen1,2, Kjeld Schmiegelow1,3

  • 1Department of Paediatrics and Adolescent Medicine, University Hospital Rigshospitalet, Copenhagen, Denmark.

Pediatric Blood & Cancer
|October 22, 2024
PubMed

Insights

Childhood cancer survival improved with better treatments. Relapse rates decreased and survival after relapse increased for hematological and solid tumors in Denmark, but CNS tumors showed no change.

Area of Science:

  • Pediatric Oncology
  • Cancer Epidemiology
  • Clinical Research

Background:

  • Advances in pediatric cancer treatment and supportive care have enhanced survival rates.
  • Investigating temporal trends in relapse and post-relapse survival is crucial for understanding treatment efficacy.

Purpose of the Study:

  • To analyze recent changes in the incidence of relapse and survival after relapse in children with cancer in Denmark.
  • To identify trends for hematological cancers, solid tumors, and central nervous system (CNS) tumors.

Main Methods:

  • A register-based study of 2890 children diagnosed with cancer before age 15 (2001-2021).
  • Utilized Aalen-Johansen and Kaplan-Meier estimators to assess cumulative incidence of relapse and survival probability post-relapse.
  • Included hematological cancers, solid tumors, and CNS tumors with specific timeframes.

Main Results:

  • The 5-year cumulative incidence of relapse decreased in hematological cancers (14% to 11%) and solid tumors (21% to 18%) between 2001-2010 and 2011-2021.
  • The 5-year survival after relapse improved for hematological cancers (44% to 61%) and solid tumors (38% to 46%).
  • Malignant CNS tumors showed stable relapse incidence (49%) and survival (20%) between 2010-2015 and 2016-2021.

Conclusions:

  • Improvements in reducing relapse incidence and increasing survival post-relapse were observed for childhood hematological cancers and solid tumors in Denmark.
  • A persistent survival gap remains for children who relapse, highlighting the need for intensified, targeted treatments.
  • Further research and treatment advancements are necessary for high-risk pediatric cancer patients, particularly those with CNS tumors.
Abstract

Keywords:
Denmarkchildhood cancerpopulation‐based studyrelapsesurvival

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.7K
Detecting Anastasis In Vivo by CaspaseTracker Biosensor20:16

Detecting Anastasis In Vivo by CaspaseTracker Biosensor

Published on: February 1, 2018

9.2K

Related Concept Videos

Cancer Survival Analysis01:21

Cancer Survival Analysis

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...
328
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
184
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

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...
155
Actuarial Approach01:20

Actuarial Approach

The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
63
Treatment Resistant Cancers02:56

Treatment Resistant Cancers

Cancer is the second leading cause of death in the United States. A cancer cell is genetically unstable and hence can mutate faster. They can also modify their microenvironment and escape immune surveillance. The difficulties in treating cancer are further compounded by the emergence of rapid resistance to anticancer drugs. The most common ways to attain resistance in cancer cells include alteration in drug transport and metabolism, modification of drug target, elevated DNA damage response, or...
3.3K
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

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.
97