ArticleOncotarget2017
Assessment of the cancer risk factors of solitary pulmonary nodules.
Article in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 3 of them syntheses that pooled 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.
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.
Who cites it
16 citing papers in PubMed, 3 syntheses or guidelines pooled it, 39 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
- Lung cancer risk prediction models based on pulmonary nodules: A systematic review.Thoracic cancer · 2022Pooled it
- Solitary pulmonary nodule malignancy predictive models applicable to routine clinical practice: a systematic review.Systematic reviews · 2021Pooled it
- Computed tomography radiomics study of invasion and instability of lung adenocarcinoma manifesting as ground glass nodule.Journal of thoracic disease · 2024Article
- Pneumothorax after percutaneous CT-guided lung nodule biopsy: a prospective, multicenter study.Quantitative imaging in medicine and surgery · 2024Article
- Tumor blood vessel in 3D reconstruction CT imaging as an risk indicator for growth of pulmonary nodule with ground-glass opacity.Journal of cardiothoracic surgery · 2023Article
- A strategy to reduce the false-positive rate after low-dose computed tomography in lung cancer screening: A multicenter prospective cohort study.Cancer medicine · 2023Article
- Value of dual-layer spectral detector computed tomography in the diagnosis of benign/malignant solid solitary pulmonary nodules and establishment of a prediction model.Frontiers in oncology · 2023Article
- Predictors of Invasiveness in Adenocarcinoma of Lung with Lepidic Growth Pattern.Medical sciences (Basel, Switzerland) · 2022Article
- Comprehensive Analysis of Clinical Logistic and Machine Learning-Based Models for the Evaluation of Pulmonary Nodules.JTO clinical and research reports · 2022Article
- [Advances and Clinical Application of Malignant Probability Prediction Models for Solitary Pulmonary Nodule].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2021Review
- Recurrence and disease-free survival outcomes after computed tomography-guided needle biopsy in stage IA non-small cell lung cancer patients in China: a propensity score matching analysis.Quantitative imaging in medicine and surgery · 2021Article
- Development of risk prediction models for lung cancer based on tumor markers and radiological signs.Journal of clinical laboratory analysis · 2021Article
- [Progress of Lung Cancer Screening with Low Dose Helical Computed Tomography].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2020Review
- Observational
- Applying Risk Prediction Models to Optimize Lung Cancer Screening: Current Knowledge, Challenges, and Future Directions.Current epidemiology reports · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There are no large samples or exact prediction models for assessing the cancer risk factors of solitary pulmonary nodules (SPNs) in the Chinese population. We retrospectively analyzed the clinical and imaging data of patients with SPNs who underwent computer tomography guided needle biopsy in our hospital from Jan 1st of 2011 to March 30th of 2016. These patients were divided into a development data set and a validation data set. These groups included 1078 and 344 patients, respectively. A prediction model was developed from the development data set and was validated with the validation data set using logistic regression. The predictors of cancer in our model included female gender, age, pack-years of smoking, a previous history of malignancy, nodule size, lobulated and spiculated edges, lobulation alone and spiculation alone. The Area Under the Curves, sensitivity and specificity of our model in the development and validation data sets were significantly higher than those of the Mayo model and VA model (p < 0.001). We established the largest sampling risk prediction model of SPNs in a Chinese cohort. This model is particularly applicable to SPNs > 8 mm in size. SPNs in female patients, as well as SPNs featuring a combination of lobulated and spiculated edges or lobulated edges alone, should be evaluated carefully due to the probability that they are malignant.
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.