ArticleClinical pharmacology and therapeutics2008
New imaging approaches for understanding lung cancer response to treatment.
Article in Clinical pharmacology and therapeutics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Recent technological and application developments in computed tomography and magnetic resonance imaging for improved pulmonary nodule detection and lung cancer staging.Journal of magnetic resonance imaging : JMRI · 2010Review
Corrections and comments
- Erratum issuedWeydert, J [added]
Authors and funding
5 authors.
Funding
Abstract
The survival rate for lung cancer patients has barely improved over the past 30 years. New evaluation benchmarks for cancer response are needed to test therapy agents in a cost-effective and timely manner. From recent work, it is evident that primary lung cancers are very complex structures containing not only cancerous cells but also fibrotic and inflammatory cells and necrotic tissue. A greater understanding of the three-dimensional structure of primary lung cancer is emerging, allowing for the first time an appreciation of how this biomass is represented in medical imaging data. It is only through a greater understanding of the lung cancer biomass that we can define rational and early-response measures, including specific cellular responses such as cancer cell death or growth inhibition. In doing so, we can define response metrics that will shorten new drug discovery times and reduce costs, allowing for the evaluation of many more agents with therapeutic potential.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.