Evidence map›Paper›PMID 42142196›Full record

ArticleInsights into imaging2026

Density homogeneity as a crucial CT indicator for differentiating malignant and benign subcentimeter solid pulmonary nodules: A retrospective multi-center study.

Wen-Tao Zhang, Hui Gan, Wei Luo, Min Zhao, Can Ding, Xue-Feng Jiang, Ting Fu, Fa-Jin Lv, Zhi-Gang Chu

Abstract read
In one paragraph

Article in Insights into imaging, 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

9 authors.

Wen-Tao Zhang *Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Hui Gan *Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Wei LuoDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Min ZhaoDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Can DingDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Xue-Feng JiangDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Ting FuDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Fa-Jin LvDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China. fajinlv@163.com.
Zhi-Gang ChuDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China. chuzg0815@163.com.ORCID http://orcid.org/0000-0003-4016-7270

Funding

Joint Project of Chongqing Science and Technology Commission and Chongqing Public Health Commission 2025GDRC011
6 · The paper itself

Abstract

objectivesTo determine the significance of density homogeneity in differentiating malignant and benign subcentimeter solid nodules (SNs). MATERIALS AND

methodsBetween January 2018 and July 2024, 735 subcentimeter malignant SNs (SMSNs) and 814 subcentimeter benign SNs (SBSNs) from Center 1 (training set), as well as 244 SBSNs and 163 SMSNs from two other centers (validation set), were included. Patients' clinical characteristics (e.g., age, gender) and CT features of lesions (e.g., density homogeneity, diameter) were analyzed and compared, focusing on assessing the significance of density homogeneity in differential diagnosis. The optimal cutoff value of standard deviation (SD) of mean CT value for distinguishing visually heterogeneous and homogeneous SNs was calculated and validated, respectively.

resultsThe optimal SD cutoff for density heterogeneity was 57.3 HU, with an area under the curve (AUC) of 0.928 (p < 0.001) in the validation set. In training set, heterogeneous density demonstrated the highest predictive efficiency among all CT features (AUC: 0.722), and significantly improved the performance of predictive model after incorporating this indicator (AUC from 0.698 to 0.824, p < 0.001). The model indicated that heterogeneous density, lobulation, spiculation, and air bronchogram sign were independent predictors of SMSNs (all p < 0.05). In validation set, density heterogeneity also demonstrated the highest predictive efficiency (AUC: 0.738), and significantly enhanced the predictive model's performance (AUC from 0.679 to 0.831, p < 0.001).

conclusionsHeterogeneous subcentimeter SNs, especially those with lobulation, spiculation, or air bronchogram sign, should raise a high suspicion of malignancy; therefore, close monitoring is required. CRITICAL RELEVANCE STATEMENT: Accurately differentiating malignant and benign solid pulmonary nodules at the subcentimeter stage is still challenging; the present study confirmed that heterogeneous density of lesions is a stable and reliable radiological predictor of malignancy. KEY POINTS: Benign and malignant subcentimeter solid pulmonary nodules share considerable overlaps in CT morphological features, making differential diagnosis challenging. Heterogeneous lesions were significantly more common in SMSNs than in benign ones, particularly in the group of nodules ≤ 8 mm. Heterogeneous density exhibited superior efficiency than lobulation, spiculation, pleural indentation, vacuole sign and other morphological features in differentiating subcentimeter SNs. Heterogeneous subcentimeter SNs, particularly those with lobulation, spiculation, or air bronchogram sign, should be highly suspected for malignancy.

Indexed as

Diagnosis (differential)Lung neoplasmsTomography (X-ray computed)

Identifiers

PMID42142196
PMCPMC13179974

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.