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  1. Home
  3. Proposal Of A Risk Stratification Algorithm For Extracranial Malignant Rhabdoid Tumors.
  1. Home
  3. Proposal Of A Risk Stratification Algorithm For Extracranial Malignant Rhabdoid Tumors.

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Published on: August 16, 2020

Proposal of a Risk Stratification Algorithm for Extracranial Malignant Rhabdoid Tumors.

Juno Eberl1, Angelika Eggert1,2, Monika Scheer1,3

  • 1Department of Pediatric Hematology and Oncology, Charité-Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität Zu Berlin, Berlin, Germany.

Cancer Medicine
|June 25, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Malignant rhabdoid tumors (MRT) are aggressive cancers. This study developed a risk-scoring system and nomogram to predict survival and stratify patients, aiming to improve outcomes for extracranial MRT.

Keywords:
cross‐age evaluationmalignant rhabdoid tumornomogrampopulation‐based analysisrisk stratification

Related Experiment Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Area of Science:

  • Oncology
  • Cancer Research
  • Tumor Biology

Background:

  • Malignant rhabdoid tumors (MRT) are aggressive neoplasms affecting both children and adults.
  • Identifying prognostic factors is crucial for risk stratification and treatment planning.

Purpose of the Study:

  • To identify independent prognostic factors for extracranial MRT (eMRT).
  • To develop an age-spanning risk stratification system for eMRT patients.
  • To create tools for predicting individual patient risk and survival outcomes.

Main Methods:

  • Analysis of 324 extracranial MRT cases from the SEER 17 database (2000-2019).
  • Univariate analysis using Kaplan-Meier estimator to identify potential prognostic factors.
  • Cox proportional-hazards regression to determine independent prognostic factors.
  • Development of a nomogram and a risk-scoring system for risk stratification.

Main Results:

  • Overall survival rates at 3, 5, and 10 years were 35.1%, 31.1%, and 27.9%, respectively.
  • Age, tumor size, site, and disease stage were significant independent prognostic factors.
  • A nomogram and a risk-scoring system were developed, stratifying patients into distinct risk groups with significantly different survival rates.

Conclusions:

  • This study identified distinct patient subsets with varying survival probabilities in eMRT.
  • The developed nomogram and risk-scoring system can aid in estimating individual survival and assigning risk-tailored therapies.
  • The findings aim to improve treatment strategies and survival for patients with this aggressive disease.