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Related Experiment Video

Updated: Nov 15, 2025

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Sample-size calculation for preclinical dose-response experiments using heterogeneous tumour models.

Willy Ciecior1, Nadja Ebert2, Nathalie Borgeaud3

  • 1OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany.

Radiotherapy and Oncology : Journal of the European Society for Therapeutic Radiology and Oncology
|March 5, 2021
PubMed
Summary
This summary is machine-generated.

This study presents a new method for sample size calculation in preclinical cancer research, crucial for personalized oncology. Using a Monte Carlo approach for heterogeneous tumor models significantly reduces the number of animals needed for radiotherapy studies.

Keywords:
Dose-modifying factorDose-response curvesPre-clinical researchSample sizeTumour modelsTumour-control assay

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Area of Science:

  • Preclinical oncology research
  • Radiotherapy efficacy studies
  • Cancer stem cell inactivation

Background:

  • Local tumor control is a key endpoint in preclinical radio-oncology, reflecting cancer stem cell inactivation.
  • Preclinical studies increasingly use diverse tumor models to mimic human cancer heterogeneity and support personalized oncology.
  • Accurate sample size planning is essential for reliable results in studies with heterogeneous cohorts.

Purpose of the Study:

  • To develop a method for estimating sample sizes in comparative tumor-control assays with heterogeneous tumor populations.
  • To enable sample size planning for studies investigating different radiotherapy strategies and personalized treatments.
  • To provide a tool for designing robust preclinical cancer research protocols.

Main Methods:

  • A Monte Carlo-based method was developed to estimate sample sizes for 1:1 comparative tumor-control assays.
  • The method utilizes repeated logistic regression analysis, considering dose levels, dose-response curves, and dose-modifying factors (DMF).
  • Power calculations are performed for a specified number of animals per dose group.

Main Results:

  • For a single HNSCC model (FaDu), 140 animals (10 per dose level) are needed to detect a DMF of 1.25 with 82% power.
  • For a heterogeneous cohort of six lung tumor models, 462 animals (21 per dose level) are required to detect a DMF of 1.25 with 81% power.
  • Analyzing a heterogeneous cohort reduces the required animal number by over 50% compared to individual assays.

Conclusions:

  • An approach for estimating animal numbers in comparative tumor-control assays within heterogeneous populations is presented.
  • This method facilitates the inclusion of personalized treatment approaches in experimental arms.
  • The publicly available software aids in planning comparisons of sigmoidal dose-response curves using logistic regression.