ReviewDer Pathologe2012
[Translational research and diagnostics in lung cancer].
Review in Der Pathologe, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed, 2 citations in OpenAlex.
- Prediction of pathologic stage in non-small cell lung cancer using machine learning algorithm based on CT image feature analysis.BMC cancer · 2019Article
- A genomics-based classification of human lung tumors.Science translational medicine · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung cancer is the most common malignant disease leading to death worldwide. Histologically, it is broadly subcategorized into small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), with the latter mainly consisting of the major entities adenocarcinoma and squamous cell carcinoma. However, molecular profiling of various lung cancer entities has revealed major molecular differences within distinct histological tumor entities, resulting in the integration of molecular alterations in the subclassification of lung cancers. These findings can only estimate the genetic complexity of lung tumors. Large scale molecular profiling has the potential to identify novel diagnostic, prognostic and predictive markers as well as therapeutic targets. Importantly, this recently arising categorization of lung carcinomas can be regarded as an example for the characterization of malignomas of other organ systems. The pioneer model for this molecular subcategorization is the classification of malignant lymphomas.
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.