ArticleCancer management and research2020
Predicting Lung Cancer Risk of Incidental Solid and Subsolid Pulmonary Nodules in Different Sizes.
Article in Cancer management and research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- Incidence and risk factors for malignancy in patients with incidental solitary pulmonary nodules: a systematic review and meta-analysis.Annals of medicine · 2026Pooled it
- Differentiating benign from malignant pulmonary nodules in the context of bronchiectasis: a retrospective study.Annals of medicine · 2026Article
- Comparison of Pulmonary Nodule Morphology on Pre-Contrast and Delayed Post-Contrast Stack-of-Spirals Ultrashort Echo Time MRI.Diagnostics (Basel, Switzerland) · 2026Article
- A Noninvasive Circulating Tumor DNA Methylation Classifier to Identify Benign Pulmonary Nodules.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Article
- A prediction model based on computed tomography characteristics for identifying malignant from benign sub-centimeter solid pulmonary nodules.Journal of thoracic disease · 2024Article
- The differential computed tomography features between small benign and malignant solid solitary pulmonary nodules with different sizes.Quantitative imaging in medicine and surgery · 2024Article
- Radiomics-based decision support tool assists radiologists in small lung nodule classification and improves lung cancer early diagnosis.British journal of cancer · 2023Article
- Lung nodule pre-diagnosis and insertion path planning for chest CT images.BMC medical imaging · 2023Article
- Article
- Video-Assisted Thoracic Surgery Core Needle Biopsy for Pulmonary Nodules in Patients with Impaired Lung Function: Is It Feasible and Safe?Journal of chest surgery · 2023Article
- Developing of risk models for small solid and subsolid pulmonary nodules based on clinical and quantitative radiomics features.Journal of thoracic disease · 2021Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveMalignancy prediction models for pulmonary nodules are most accurate when used within nodules similar to those in which they were developed. This study was to establish models that respectively predict malignancy risk of incidental solid and subsolid pulmonary nodules of different size. MATERIALS AND
methodsThis retrospective study enrolled patients with 5-30 mm pulmonary nodules who had a histopathologic diagnosis of benign or malignant. The median time to lung cancer diagnosis was 25 days. Four training/validation datasets were assembled based on nodule texture and size: subsolid nodules (SSNs) ≤15 mm, SSNs between 15 and 30 mm, solid nodules ≤15 mm and those between 15 and 30 mm. Univariate logistic regression was used to identify potential predictors, and multivariate analysis was used to build four models.
resultsThe study identified 1008 benign and 1813 malignant nodules from a single hospital, and by random selection 1008 malignant nodules were enrolled for further analysis. There was a much higher malignancy rate among SSNs than solid nodules (rate, 75% vs 39%,
conclusionWe developed four models to help characterize subsolid and solid pulmonary nodules of different sizes. The established models may provide decision-making information for thoracic radiologists and clinicians.
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