Evidence map›Paper›PMID 18831800›Full record

ArticleBMC genomics2008

Analyzing adjuvant radiotherapy suggests a non monotonic radio-sensitivity over tumor volumes.

Jack Y Yang, Andrzej Niemierko, Mary Qu Yang, Youping Deng

Open access · goldAbstract read
In one paragraph

Article in BMC genomics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.0field-weighted citation impact, top 20% of its field
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

2 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Article
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 at 4 institutions in 1 country.

Jack Y YangDepartment of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA. jyang@bwh.harvard.edu
Andrzej Niemierko
Mary Qu Yang
Youping Deng
Harvard University · USMassachusetts General Hospital · USNational Human Genome Research Institute · USUniversity of Southern Mississippi · US

Funding

ASSESSMENT AND OPTIMIZATION OF RADIATION THERAPYR01CA050628 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI NIEMIERKO, ANDRZEJ · 1989 to 2007
$2.6M
NCI NIH HHS R01 CA050628NCI NIH HHS R01 CA50628
6 · The paper itself

Abstract

backgroundAdjuvant Radiotherapy (RT) after surgical removal of tumors proved beneficial in long-term tumor control and treatment planning. For many years, it has been well concluded that radio-sensitivities of tumors upon radiotherapy decrease according to the sizes of tumors and RT models based on Poisson statistics have been used extensively to validate clinical data.

resultsWe found that Poisson statistics on RT is actually derived from bacterial cells despite of many validations from clinical data. However cancerous cells do have abnormal cellular communications and use chemical messengers to signal both surrounding normal and cancerous cells to develop new blood vessels and to invade, to metastasis and to overcome intercellular spatial confinements in general. We therefore investigated the cell killing effects on adjuvant RT and found that radio-sensitivity is actually not a monotonic function of volume as it was believed before. We present detailed analysis and explanation to justify above statement. Based on EUD, we present an equivalent radio-sensitivity model.

conclusionWe conclude that radio sensitivity is a sophisticated function over tumor volumes, since tumor responses upon radio therapy also depend on cellular communications.

Indexed as

Radiation ToleranceTumor BurdenBacteriaHumansModels, BiologicalNeoplasmsPoisson DistributionRadiotherapy, AdjuvantSignal Transduction

Identifiers

PMID18831800
PMCPMC2559899
OpenAlexW2161780396

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.