ArticleQuantitative imaging in medicine and surgery2026
Evaluation of high-grade growth patterns in invasive adenocarcinomas manifesting as ground-glass lesions via clinical and qualitative and quantitative computed tomography features.
Article in Quantitative imaging in medicine and surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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1 citing paper in PubMed.
- CT-based deep learning radiomics predicts micropapillary/solid components in lung adenocarcinoma.Journal of thoracic disease · 2026Article
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8 authors.
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Abstract
Background: High-grade growth patterns (HGPs) have a negative impact on the prognosis of patients with lung invasive adenocarcinoma (IAC). This study aimed to use clinical and computed tomography (CT) features to determine the presence of HGPs and pathological grade based on the proportion of HGPs in ground-glass opacity (GGO)-IACs. Methods: From September 2017 to December 2024, a total of 1,881 patients comprising 1,979 GGO-IACs (group A: 490 with HGPs; group B: 1,489 without HGPs) were retrospectively enrolled. The patients' clinical characteristics and CT features of lesions in both groups were analyzed and compared, and models for predicting the presence of HGPs and pathological grade were constructed. Results: The multivariate analysis revealed that the independent predictors of HGPs were an ill-defined boundary [odds ratio (OR) =4.442], part-solid manifestation (OR =2.423), marginally distributed solid component (OR =3.727), spiculation (OR =2.956), pleural indentation (OR =1.968), pleural tag (OR =1.751), and a consolidation-to-tumor ratio (CTR) >21.5% (OR =1.029, P<0.001). The area under the curve (AUC) of the model for predicting the presence of HGPs was 0.792 [95% confidence interval (CI): 0.767-0.816]. Additionally, the independent predictors of poorly differentiated GGO-IACs were an ill-defined boundary (OR =1.867), spiculation (OR =2.197), a marginally distributed component (OR =1.929), absence of lobulation (OR =2.639), and a CTR >28.5% (OR =1.023, P<0.001). The AUC of the model for predicting poorly differentiated GGO-IACs was 0.739 (95% CI: 0.689-0.790). Conclusions: The CT-based predictive models demonstrated good performance in assessing the presence of HGPs and poorly differentiated lesions in patients with suspected GGO-IACs.
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