Evidence map›Paper›PMID 41775904›Full record

ArticleRadiologie (Heidelberg, Germany)2026

Clinical value of ultra-low-dose chest CT with deep learning image reconstruction in the detection and characterization of pulmonary nodules.

Qiuju Fan, Hui Tan, Guangming Ma, Nan Yu, Yanbing Guo, Zhanli Ren

Abstract read
PubMed Publisher
In one paragraph

Article in Radiologie (Heidelberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Qiuju FanDepartment of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, No.-2#, Weiyang West Road, 712000, Xianyang, Shaanxi, China.
Hui TanDepartment of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, No.-2#, Weiyang West Road, 712000, Xianyang, Shaanxi, China.
Guangming MaDepartment of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, No.-2#, Weiyang West Road, 712000, Xianyang, Shaanxi, China.
Nan YuDepartment of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, No.-2#, Weiyang West Road, 712000, Xianyang, Shaanxi, China.
Yanbing GuoDepartment of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, No.-2#, Weiyang West Road, 712000, Xianyang, Shaanxi, China.
Zhanli RenDepartment of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, No.-2#, Weiyang West Road, 712000, Xianyang, Shaanxi, China. rzl19900220@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo evaluate the clinical value of ultra-low-dose CT (ULDCT) with deep learning image reconstruction (DLIR) in the diagnosis of pulmonary nodules. MATERIALS AND

methodsThis was a prospective study that included 115 patients with suspected pulmonary nodules who provided informed consent to participate. All patients were examined by both standard-dose CT (SDCT) and ULDCT. The SDCT images were reconstructed with adaptive statistical iterative reconstruction‑V at 40% (ASIR-V40%; group A), while the ULDCT images were reconstructed with ASIR-V40% (group B) and with high-strength DLIR (DLIR‑H; group C). The radiation dose and the number of detected lung nodules of both scanning modes were recorded. The CT values and noise values (SD) of lung tissue, aorta, and muscle were measured, and the signal-to-noise ratio (SNR) was calculated. Overall image quality and malignant signs of lung nodules were evaluated in a double-blind manner and analyzed using the pathological diagnosis as the gold standard. Measurements were statistically compared between ULDCT and SDCT.

resultsThe radiation dose of ULDCT was 0.25 ± 0.08 mSv, that is, a 92.6% reduction compared with the 3.38 ± 0.97 mSv of SDCT (P < 0.001). There was no significant difference in CT values of the lung, aorta, and muscle among the three groups (all P > 0.05). There was no difference in SD, SNR, subjective diagnostic confidence, and malignant signs of pulmonary nodules between groups A and C (P > 0.05), but these parameters were all better than in group B (P < 0.05). The agreement between the two observers on malignant signs was high (kappa = 0.81). The number of pulmonary nodules detected in groups A, B, and C was 247, 238, and 247, respectively.

conclusionImage quality obtained using ULDCT with DLIR‑H is comparable to that of SDCT with ASIR-V40%. The ULDCT method enables excellent detection and diagnosis of nodules with significant dose reduction.

Indexed as

Deep learning reconstructionImage qualityPulmonary nodulesUltra-low dose

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.