ReviewJournal of thoracic disease2014
Magnetic resonance imaging for lung cancer screen.
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 18 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
18 citing papers in PubMed, 35 citations in OpenAlex.
- Article
- Free-breathing 3D pulmonary ventilation mapping at 0.55 T using stack-of-spiral out-in bSSFP.Magnetic resonance in medicine · 2026Article
- Ultrashort Echo Time and Zero Echo Time MRI and Their Applications at High Magnetic Fields: A Literature Survey.Investigative magnetic resonance imaging · 2024Article
- Dual-Tuned Coaxial-Transmission-Line RF Coils for HyperpolarizedIEEE transactions on bio-medical engineering · 2024Article
- Article
- Quantitative Imaging Metrics for the Assessment of Pulmonary Pathophysiology: An Official American Thoracic Society and Fleischner Society Joint Workshop Report.Annals of the American Thoracic Society · 2023Article
- State of the Art: Lung Cancer Staging Using Updated Imaging Modalities.Bioengineering (Basel, Switzerland) · 2022Review
- Pancoast Tumor Presenting as Neck Pain in the Chiropractic Office: A Case Report and Literature Review.The American journal of case reports · 2022Review
- Current treatments for non-small cell lung cancer.Frontiers in oncology · 2022Review
- Lung Cancer in Li-Fraumeni Syndrome.JCO precision oncology · 2021Article
- Gold nanomaterials in the management of lung cancer.Emerging topics in life sciences · 2020Review
- Evaluation of whole-body MRI with diffusion-weighted sequences in the staging of pediatric cancer patients.PloS one · 2020Article
- A retrospective study analyzing missed diagnosis of lung metastases at their early stages on computed tomography.Journal of thoracic disease · 2019Article
- A Prediction Model with a Combination of Variables for Diagnosis of Lung Cancer.Medical science monitor : international medical journal of experimental and clinical research · 2017Article
- Magnetic resonance imaging of pulmonary nodules: accuracy in a granulomatous disease-endemic region.European radiology · 2016Article
- Magnetic resonance imaging in the evaluation of idiopathic pulmonary fibrosis: a real possibility, or an attractive challenge?Quantitative imaging in medicine and surgery · 2016Article
- Sparse Reconstruction Techniques in Magnetic Resonance Imaging: Methods, Applications, and Challenges to Clinical Adoption.Investigative radiology · 2016Review
- On pancreatic cancer screening by magnetic resonance imaging with the recent evidence by Del Chiaro and colleagues.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2015Article
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
Lung cancer is the leading cause of cancer related death throughout the world. Lung cancer is an example of a disease for which a large percentage of the high-risk population can be easily identified via a smoking history. This has led to the investigation of lung cancer screening with low-dose helical/multi-detector CT. Evidences suggest that early detection of lung cancer allow more timely therapeutic intervention and thus a more favorable prognosis for the patient. The positive relationship of lesion size to likelihood of malignancy has been demonstrated previously, at least 99% of all nodules 4 mm or smaller are benign, while noncalcified nodules larger than 8 mm diameter bear a substantial risk of malignancy. In the recent years, the availability of high-performance gradient systems, in conjunction with phased-array receiver coils and optimized imaging sequences, has made MR imaging of the lung feasible. It can now be assumed a threshold size of 3-4 mm for detection of lung nodules with MRI under the optimal conditions of successful breath-holds with reliable gating or triggering. In these conditions, 90% of all 3-mm nodules can be correctly diagnosed and that nodules 5 mm and larger are detected with 100% sensitivity. Parallel imaging can significantly shorten the imaging acquisition time by utilizing the diversity of sensitivity profile of individual coil elements in multi-channel radiofrequency receive coil arrays or transmit/receive coil arrays to reduce the number of phase encoding steps required in imaging procedure. Compressed sensing technique accelerates imaging acquisition from dramatically undersampled data set by exploiting the sparsity of the images in an appropriate transform domain. With the combined imaging algorithm of parallel imaging and compressed sensing and advanced 32-channel or 64-channel RF hardware, overall imaging acceleration of 20 folds or higher can then be expected, ultimately achieve free-breathing and no ECG gating acquisitions in lung cancer MRI screening. Further development of protocols, more clinical trials and the use of advanced analysis tools will further evaluate the real significance of lung MRI.
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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.