Evidence map›Paper›PMID 25570493›Full record

ArticleAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference2014

Development of a diffusion-based mathematical model for predicting chemotherapy effects.

Zhihui Wang, Romica Kerketta, Yao-Li Chuang, Vittorio Cristini

Abstract read
In one paragraph

Article in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 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
–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

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.

Zhihui Wang
Romica Kerketta
Yao-Li Chuang
Vittorio Cristini

Funding

TRANS-NETWORK PROJECTSU54CA143907 · NCI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI NOLAN, GARRY P · 2009 to 2013
$15.2M
Texas Center for Cancer NanomedicineU54CA151668 · NCI · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI CRISTINI, VITTORIO, FERRARI, MAURO · 2010 to 2014
$12.4M
TRANS-NETWORK PROJECTSU54CA143837 · NCI · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI FERRARI, MAURO · 2009 to 2014
$10.8M
The Core of the Computational BiologyU54CA149196 · NCI · METHODIST HOSPITAL RESEARCH INSTITUTE · PI WONG, STEPHEN TC · 2010 to 2015
$10.6M
NCI NIH HHS 1U54CA143837NCI NIH HHS 1U54CA143907NCI NIH HHS 1U54CA149196NCI NIH HHS 1U54CA151668NCI NIH HHS U54 CA143837NCI NIH HHS U54 CA143907NCI NIH HHS U54 CA149196NCI NIH HHS U54 CA151668
6 · The paper itself

Abstract

Mathematical modeling of drug transport can complement current experimental and clinical investigations to understand drug resistance mechanisms, which eventually will help to develop patient-specific chemotherapy treatments. In this paper, we present a general time- and space-dependent mathematical model based on diffusion theory for predicting chemotherapy outcome. This model has two important parameters: the blood volume fraction and radius of blood vessels divided by drug diffusion penetration length. Model analysis finds that a larger ratio of the radius of blood vessel to diffusion penetration length resulted in to a larger fraction of tumor killed, thereby leading to a better treatment outcome. Clinical translation of the model can help quantify and predict the optimal dosage size and frequency of chemotherapy for individual patients.

Indexed as

Models, TheoreticalAntineoplastic AgentsBlood VesselsColorectal NeoplasmsHumansLiver NeoplasmsTomography, X-Ray ComputedAntineoplastic Agents

Identifiers

PMID25570493
PMCPMC4985527

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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.