Evidence map›Paper›PMID 27549860›Full record

ReviewBiology direct2016

Optimal treatment and stochastic modeling of heterogeneous tumors.

Hamidreza Badri, Kevin Leder

Abstract readReview
In one paragraph

Review in Biology direct, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Hamidreza BadriDepartment of Industrial and Systems Engineering, University of Minnesota, Minneapolis, MN, 55455, USA.
Kevin LederDepartment of Industrial and Systems Engineering, University of Minnesota, Minneapolis, MN, 55455, USA. lede0024@umn.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

unlabelledIn this work we review past articles that have mathematically studied cancer heterogeneity and the impact of this heterogeneity on the structure of optimal therapy. We look at past works on modeling how heterogeneous tumors respond to radiotherapy, and take a particularly close look at how the optimal radiotherapy schedule is modified by the presence of heterogeneity. In addition, we review past works on the study of optimal chemotherapy when dealing with heterogeneous tumors. REVIEWERS: This article was reviewed by Thomas McDonald, David Axelrod, and Leonid Hanin.

Indexed as

Models, TheoreticalHumansNeoplasmsStochastic ProcessesRadiotherapyStochastic modelingTumor heterogeneity

Identifiers

PMID27549860
PMCPMC4994177

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.