Evidence map›Paper›PMID 34119384›Full record

ArticleZeitschrift fur medizinische Physik2022

Tumour volume distribution can yield information on tumour growth and tumour control.

Uwe Schneider, Jürgen Besserer

Erratum issuedOpen access · hybridAbstract read
In one paragraph

Article in Zeitschrift fur medizinische Physik, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.1field-weighted citation impact, top 55% of its field
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors at 1 institution in 1 country.

Uwe SchneiderDepartment of Physics, Science Faculty, University of Zürich, Zürich, Switzerland; Radiotherapy Hirslanden, Witellikerstrasse 40, CH-8032 Zürich, Switzerland. Electronic address: uwe.schneider@uzh.ch.
Jürgen BessererDepartment of Physics, Science Faculty, University of Zürich, Zürich, Switzerland; Radiotherapy Hirslanden, Witellikerstrasse 40, CH-8032 Zürich, Switzerland.
University of Zurich · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIt is shown that tumour volume distributions can yield information on two aspects of cancer research: tumour induction and tumour control. MATERIALS AND

methodsFrom the hypothesis that the intrinsic distribution of breast cancer volumes follows an exponential distribution, firstly the probability density function of tumour growth time was deduced via a mathematical transformation of the probability density functions of tumour volumes. In a second step, the distribution of tumour volumes was used to model the variation of the clonogenic cell number between patients in order to determine tumour control probabilities for radiotherapy patients.

resultsDistribution of lag times, i.e. the time from the appearance of the first fully malignant cell until a clinically observable cancer, can be used to deduce the probability of tumour induction as a function of patient age. The integration of the volume variation with a Poisson-TCP model results in a logistic function which explains population-averaged survival data of radiotherapy patients.

conclusionsThe inclusion of tumour volume distributions into the TCP formalism enables a direct link to be deduced between a cohort TCP model (logistic) and a TCP model for individual patients (Poisson). The TCP model can be applied to non-uniform tumour dose distributions.

Indexed as

NeoplasmsHumansProbabilityRadiotherapy DosageTumor BurdenLag timeTumor control probabilityTumor size

Identifiers

PMID34119384
PMCPMC9948830
OpenAlexW3172117597

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.