ReviewJournal of thoracic disease2025
3DCT reconstruction-does 3DCT improve anatomical lung resection?-a narrative review of the literature.
Review in Journal of thoracic disease, 2025. 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.
- RoboticJournal of thoracic disease · 2026Article
- Impact of patient-specific three-dimensional reconstruction on surgical planning and perioperative outcomes in early-stage lung cancer: a randomised clinical trial.Frontiers in oncology · 2026Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Objective: Minimally invasive and sublobar anatomical lung resection has become the gold standard for stage I non-small cell lung cancer (NSCLC) under 2 cm. Preoperative surgical planning with three-dimensional computed tomography (3DCT) reconstruction has become more common to improve safety and accuracy of resection. The aim of this review is to evaluate the evidence for 3DCT reconstruction in improving perioperative outcomes in patients undergoing anatomical lung resection. Methods: A targeted literature review of evidence for 3DCT reconstruction in the perioperative period, focusing on quantitative data. Studies were included if they offered comparative data between two-dimensional computed tomography (2DCT) and 3DCT, or if they reported outcomes directly influenced using 3D imaging. Articles were excluded if they did not address preoperative imaging strategies, lacked peer review, or failed to provide sufficient data for analysis. Key Content and Findings: Forty papers were identified for this review. Seventeen only described bronchovascular patterns and anatomical variations with no surgical procedure performed and were excluded. Twenty-three described 3DCT reconstruction in relation to surgical resection. Nine studies assessed resection margins, with one reporting a change from segmentectomy to lobectomy due to 3DCT findings (10.5%). Two showed improved margin adequacy with 3DCT, though overall evidence remains limited. Across six comparative studies, two reported reduced blood loss with 3DCT, while others showed no difference. Operative time showed mixed results: three retrospective studies reported shorter durations, though the single randomised controlled trial, the DRIVATS study, found no difference. There was also no difference in clinical outcomes such as chest tube drainage, postoperative complications and postoperative hospital stay. However, this may be due to segmentectomy being a heterogeneous group of operations, as well as underpowered studies. Conclusions: Although randomized evidence demonstrating the superiority of 3DCT reconstruction over conventional computed tomography (CT) is lacking, 3DCT remains a valuable adjunct for visualising complex anatomical structures and guiding operative planning.
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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.