Performance Evaluation of Four Deep Learning-Based CAD Systems and Manual Reading for Pulmonary Nodules Detection, Volume Measurement, and Lung-RADS Classification Under Varying Radiation Doses and Reconstruction Methods.

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

3 citing papers in PubMed.

1. Feasibility of BMI-based sub-milliSievert low-dose CT in individualized detection of lung nodules.European radiology · 2026
   Article
2. Multi-spatial channel attention and inceptionv3-based CAD system with optimized MLP for lung cancer detection.Journal of computer-aided molecular design · 2026
   Article
3. Performance of multi-planar volume rendering versus maximum intensity projection and minimum intensity projection on pulmonary nodule detection.Quantitative imaging in medicine and surgery · 2026
   Article

Corrections and comments

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Authors and funding

5 authors.

Funding

Abstract

PubMed holds no abstract for this paper.

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