Evidence map›Paper›PMID 25221615›Full record

ArticleComputational and mathematical methods in medicine2014

Computer implementation of a new therapeutic model for GBM tumor.

Ali Jamali Nazari, Dariush Sardari, Ahmad Reza Vali, Keivan Maghooli

Abstract read
In one paragraph

Article in Computational and mathematical methods in medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Ali Jamali NazariDepartment of Medical Radiation Engineering, Islamic Azad University, Tehran Science and Research Branch, Tehran 14515-775, Iran.ORCID 0000-0002-9427-121X
Dariush SardariDepartment of Medical Radiation Engineering, Islamic Azad University, Tehran Science and Research Branch, Tehran 14515-775, Iran.
Ahmad Reza ValiElectrical Engineering Department, Control Engineering Group, Malek Ashtar University of Technology, Tehran, Iran.
Keivan MaghooliDepartment of Biomedical Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Modeling the tumor behavior in the host organ as function of time and radiation dose has been a major study in the previous decades. Here the effort in estimation of cancerous and normal cell proliferation and growth in glioblastoma multiform (GBM) tumor is presented. This paper introduces a new mathematical model in the form of differential equation of tumor growth. The model contains dose delivery amount in the treatment scheme as an input term. It also can be utilized to optimize the treatment process in order to increase the patient survival period. Gene expression programming (GEP) as a new concept is used for estimating this model. The LQ model has also been applied to GEP as an initial value, causing acceleration and improvement of the algorithm estimation. The model shows the number of the tumor and normal brain cells during the treatment process using the status of normal and cancerous cells in the initiation of treatment, the timing and amount of dose delivery to the patient, and a coefficient that describes the brain condition. A critical level is defined for normal cell when the patient's death occurs. In the end the model has been verified by clinical data obtained from previous accepted formulae and some of our experimental resources. The proposed model helps to predict tumor growth during treatment process in which further treatment processes can be controlled.

Indexed as

Models, GeneticRadiation DosageAlgorithmsBrain NeoplasmsCell ProliferationGlioblastomaHumansKaplan-Meier Estimate

Identifiers

PMID25221615
PMCPMC4144396

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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.