ArticleCureus2024
Offline Adaptive Radiotherapy for Large Non-small Cell Lung Cancer With Volumetric Modulated Arc Therapy Replanning After Creation of Dose Distribution Using Cone-Beam Computed Tomographic Images of Image-Guided Radiotherapy.
Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There is a paucity of studies reporting the use of automated contouring function on cone-beam computed tomography (CBCT) images acquired during image-guided radiotherapy (IGRT) with offline adaptive radiotherapy for locally advanced non-small cell lung cancer (NSCLC). This case report discusses the use of an automated contouring function on CBCT images acquired during IGRT to quantify the dose distribution variations associated with tumor shrinkage in a patient with large NSCLC. A 72-year-old woman with locally advanced squamous cell carcinoma of the lung (T4N1M0; tumor diameter: 14 cm) underwent chemoradiotherapy. Two offline adaptive radiotherapy sessions were performed, resulting in no evidence of recurrence or distant metastasis, as well as no side effects of Grade 2 or higher toxicity.
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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.