ArticleEuropean radiology2021
Natural history of pathologically confirmed pulmonary subsolid nodules with deep learning-assisted nodule segmentation.
Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06405815 (Development of a Tumor and Peritumor Radiomics-based Model to Predict the High-risk Surgical Margin and Prognosis of T3 and T4 Non-Small Cell Lung Cancer), which is not on this map. Cited by 29 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.
Development of a Tumor and Peritumor Radiomics-based Model to Predict the High-risk Surgical Margin and Prognosis of T3 and T4 Non-Small Cell Lung Cancer
Who cites it
29 citing papers in PubMed, 33 citations in OpenAlex.
- Combined Model Integrating Clinical-Imaging and Deep Learning to Discriminate Between Persistent Inflammatory and Malignant Subsolid Pulmonary Nodules.Journal of imaging informatics in medicine · 2026Article
- [Advances in the Growth Risk Assessment and Precision Management of Pulmonary Subsolid Nodules].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2026Review
- Narrative review: the research advances of artificial intelligence in the prediction of pulmonary nodule growth.Journal of thoracic disease · 2026Review
- Correlation analysis between growth heterogeneity and genetic mutations in resected subsolid lung adenocarcinoma based on long-term computed tomography (CT) follow-up.Translational lung cancer research · 2026Article
- Impact of CT acquisition settings on the stability of radiomic features and the performance of pulmonary nodule classification models.Insights into imaging · 2026Article
- Comparative analysis of two-dimensional and three-dimensional machine learning models for predicting the degree of invasion in lung non-mucinous adenocarcinomas presenting as subsolid nodules.Frontiers in oncology · 2026Article
- Preoperative CT-based radiomics to predict the 5-year growth of residual nodules after resection of dominant lung tumors in patients with multiple lung subsolid nodules.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Correlation between CT growth patterns and invasiveness progression in neoplastic subcentimeter sub-solid nodules.Annals of medicine · 2025Article
- Influencing factors and prediction of growth heterogeneity in solid nodule non-small cell lung cancer based on artificial intelligence: a prospective study.Translational lung cancer research · 2025Article
- Longitudinal variability of CT imaging features for predicting pulmonary nodule invasiveness: A multicenter study.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2025Article
- Relationships between growth rate and Ki-67 and immune indices in ground-glass nodule-featured lung adenocarcinoma.BMC cancer · 2025Article
- Natural course of lung adenocarcinoma manifesting as ground-glass nodules: invasiveness assessment based on growth evaluation.Translational lung cancer research · 2025Article
- Deep learning reconstruction improves computer-aided pulmonary nodule detection and measurement accuracy for ultra-low-dose chest CT.BMC medical imaging · 2025Article
- Diagnostic accuracy of deep learning for the invasiveness assessment of ground-glass nodules with fine segmentation: a systematic review and meta-analysis.Quantitative imaging in medicine and surgery · 2025Article
- Multimodal integration to identify the invasion status of lung adenocarcinoma intraoperatively.iScience · 2024Article
- Development of a nomogram-based model incorporating radiomic features from follow-up longitudinal lung CT images to distinguish invasive adenocarcinoma from benign lesions: a retrospective study.BMC pulmonary medicine · 2024Article
- Comparison of different classification systems for pulmonary nodules: a multicenter retrospective study in China.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Prediction of benign and malignant ground glass pulmonary nodules based on multi-feature fusion of attention mechanism.Frontiers in oncology · 2024Article
- A narrative review of the clinical approach to subsolid pulmonary nodules.Annals of translational medicine · 2023Review
- Baseline whole-lung CT features deriving from deep learning and radiomics: prediction of benign and malignant pulmonary ground-glass nodules.Frontiers in oncology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 5 institutions in 2 countries.
Funding
Abstract
objectiveTo explore the natural history of pulmonary subsolid nodules (SSNs) with different pathological types by deep learning-assisted nodule segmentation.
methodsBetween June 2012 and June 2019, 95 resected SSNs with preoperative long-term follow-up were enrolled in this retrospective study. SSN detection and segmentation were performed on preoperative follow-up CTs using the deep learning-based Dr. Wise system. SSNs were categorized into invasive adenocarcinoma (IAC, n = 47) and non-IAC (n = 48) groups; according to the interval change during the preoperative follow-up, SSNs were divided into growth (n = 68), nongrowth (n = 22), and new emergence (n = 5) groups. We analyzed the cumulative percentages and pattern of SSN growth and identified significant factors for IAC diagnosis and SSN growth.
resultsThe mean preoperative follow-up was 42.1 ± 17.0 months. More SSNs showed growth or new emergence in the IAC than in the non-IAC group (89.4% vs. 64.6%, p = 0.009). Volume doubling time was non-significantly shorter for IACs than for non-IACs (1436.0 ± 1188.2 vs. 2087.5 ± 1799.7 days, p = 0.077). Median mass doubling time was significantly shorter for IACs than for non-IACs (821.7 vs. 1944.1 days, p = 0.001). Lobulated sign (p = 0.002) and SSN mass (p = 0.004) were significant factors for differentiating IACs. IACs showed significantly higher cumulative growth percentages than non-IACs in the first 70 months of follow-up. The growth pattern of SSNs may conform to the exponential model. The initial volume (p = 0.042) was a predictor for SSN growth.
conclusionsIACs appearing as SSNs showed an indolent course. The mean growth rate was larger for IACs than for non-IACs. SSNs with larger initial volume are more likely to grow. KEY POINTS: • Invasive adenocarcinomas (IACs) appearing as subsolid nodules (SSNs), with a mean volume doubling time (VDT) of 1436.0 ± 1188.2 days and median mass doubling time (MDT) of 821.7 days, showed an indolent course. • The VDT was shorter for IACs than for non-IACs (1436.0 ± 1188.2 vs. 2087.5 ± 1799.7 days), but the difference was not significant (p = 0.077). The median MDT was significantly shorter for IACs than for non-IACs (821.7 vs. 1944.1 days, p = 0.001). • SSNs with lobulated sign and larger mass (> 390.5 mg) may very likely be IACs. SSNs with larger initial volume are more likely to grow.
Indexed as
Identifiers
What OpenQuestion holds
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.