ReviewJournal of thoracic disease2014
New techniques for assessing response after hypofractionated radiotherapy for lung cancer.
Review in Journal of thoracic disease, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed, 22 citations in OpenAlex.
- Circulating Tumor Cell Assessment in Presumed Early Stage Non-Small Cell Lung Cancer Patients Treated with Stereotactic Body Radiation Therapy: A Prospective Pilot Study.International journal of radiation oncology, biology, physics · 2018Trial
- Development and validation of multi-sequence magnetic resonance imaging radiomics models for predicting tumor response to radiotherapy in locally advanced non-small cell lung cancer.Journal of thoracic disease · 2025Article
- Tumor Volume Regression during and after Radiochemotherapy: A Macroscopic Description.Journal of personalized medicine · 2022Article
- Pulmonary quantitative CT imaging in focal and diffuse disease: current research and clinical applications.The British journal of radiology · 2018Review
- Radiomics for Response and Outcome Assessment for Non-Small Cell Lung Cancer.Technology in cancer research & treatment · 2018Review
- Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology.Frontiers in oncology · 2016Article
- Article
- Early PET-CT After Stereotactic Radiotherapy for Stage 1 Non-small Cell Lung Carcinoma Is Predictive of Local Control.In vivo (Athens, Greece)Article
- SBRT for lung oligometastases: Who is the perfect candidate?Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation OncologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hypofractionated radiotherapy (HFRT) is an effective and increasingly-used treatment for early stage non-small cell lung cancer (NSCLC). Stereotactic ablative radiotherapy (SABR) is a form of HFRT and delivers biologically effective doses (BEDs) in excess of 100 Gy10 in 3-8 fractions. Excellent long-term outcomes have been reported; however, response assessment following SABR is complicated as radiation induced lung injury can appear similar to a recurring tumor on CT. Current approaches to scoring treatment responses include Response Evaluation Criteria in Solid Tumors (RECIST) and positron emission tomography (PET), both of which appear to have a limited role in detecting recurrences following SABR. Novel approaches to assess response are required, but new techniques should be easily standardized across centers, cost effective, with sensitivity and specificity that improves on current CT and PET approaches. This review examines potential novel approaches, focusing on the emerging field of quantitative image feature analysis, to distinguish recurrence from fibrosis after SABR.
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.