A Mathematical Model for Predicting Malignancy of Solitary Pulmonary Nodules

No abstract is available for this paper.

• Publication year

  2012

• Venue

  World Journal of Surgery

• Publication date

  2012-02-02

• Fields of study

  Mathematics, Medicine

• Identifiers
  DOI 10.1007/s00268-012-1449-8PMID 22297626
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

• Solitary and multiple resected adenocarcinomas after CT screening for lung cancer: histopathologic features and their prognostic implications.

  2009cited by this paper
• Management of solitary pulmonary nodule.

  2008cited by this paper
• The evaluation and management of the solitary pulmonary nodule

  2008cited by this paper
• A clinical model to estimate the pretest probability of lung cancer in patients with solitary pulmonary nodules.

  2007cited by this paper
• Evidence for the treatment of patients with pulmonary nodules: when is it lung cancer?: ACCP evidence-based clinical practice guidelines (2nd edition).

  2007cited by this paper
• Benign and Malignant Solitary Pulmonary Nodule:Analysis of Risk Factors

  2006cited by this paper
• Guidelines for management of small pulmonary nodules detected on CT scans: a statement from the Fleischner Society.

  2005cited by this paper
• Relationship between a history of antecedent cancer and the probability of malignancy for a solitary pulmonary nodule.

  2004cited by this paper
• The solitary pulmonary nodule.

  2003cited by this paper
• Clinical practice. The solitary pulmonary nodule.

  2003cited by this paper
• Cancer genetics.

  2002cited by this paper
• Cancer genetics

  2001cited by this paper
• Genetic epidemiology of multistage carcinogenesis.

  2001cited by this paper
• Cancer epidemiology in the last century and the next decade

  2001cited by this paper
• Differentiation of malignant from benign solitary pulmonary lesions using chest radiography, spiral CT and HRCT.

  2000cited by this paper
• Molecular epidemiology: on the path to prevention?

  2000cited by this paper
• Solitary pulmonary nodules: Part I. Morphologic evaluation for differentiation of benign and malignant lesions.

  2000cited by this paper
• Computerized analysis of the likelihood of malignancy in solitary pulmonary nodules with use of artificial neural networks.

  2000cited by this paper
• Solitary pulmonary nodules: clinical prediction model versus physicians.

  1999cited by this paper
• The probability of malignancy in solitary pulmonary nodules. Application to small radiologically indeterminate nodules.

  1997cited by this paper
• Determining the likelihood of malignancy in solitary pulmonary nodules with Bayesian analysis. Part I. Theory.

  1993cited by this paper
• Solitary pulmonary nodules: comparison of classification with standard, thin-section, and reference phantom CT.

  1991cited by this paper
• CT demonstration of calcification in carcinoma of the lung.

  1990cited by this paper
• CT of the pulmonary nodule: a cooperative study.

  1986cited by this paper
• AN ANALYSIS OF 155 SOLITARY LUNG LESIONS ILLUSTRATING THE DIFFERENTIAL DIAGNOSIS OF MIXED TUMOURS OF THE LUNG.

  1965cited by this paper

• Incidence and risk factors for malignancy in patients with incidental solitary pulmonary nodules: a systematic review and meta-analysis

  2026cites this paper
• Performance comparison of four published lung cancer prediction models applied to a cohort from the National Lung Screening Trial

  2025cites this paper
• The probability of lung cancer in patients with pulmonary nodules detected via low-dose computed tomography screening in China

  2025cites this paper
• Deep learning-based CT image for pulmonary nodule classification with intrathoracic fat: A multicenter study

  2025cites this paper
• Diagnostic Approach to Solitary Pulmonary Nodules Discussed in Multidisciplinary Lung Cancer Council

  2023cites this paper
• Deep learning-based computer-aided diagnostic models versus other methods for predicting malignancy risk in CT-detected pulmonary nodules

  2023cites this paper
• A strategy to reduce the false‐positive rate after low‐dose computed tomography in lung cancer screening: A multicenter prospective cohort study

  2023cites this paper
• Lung cancer risk prediction models based on pulmonary nodules: A systematic review

  2022cites this paper
• Pulmonary nodule malignancy probability: a diagnostic accuracy meta-analysis of the Mayo model.

  2022cites this paper
• Patient and Nodule Characteristics Associated with a Lung Cancer Diagnosis Among Individuals with Incidentally Detected Lung Nodules.

  2022cites this paper
• Clinical Scores, Biomarkers and IT Tools in Lung Cancer Screening—Can an Integrated Approach Overcome Current Challenges?

  2022cites this paper
• Establishment and validation of a clinical model for diagnosing solitary pulmonary nodules

  2022cites this paper
• Factors associated with concurrent malignancy risk among patients with incidental solitary pulmonary nodule: A systematic review taskforce for developing rapid recommendations

  2022cites this paper
• A new classifier constructed with platelet features for malignant and benign pulmonary nodules based on prospective real-world data

  2022cites this paper
• Predictors of malignancy in patients with solitary pulmonary nodules undergoing pulmonary resection

  2022influential citation
• Establishment and validation of a prediction model for the probability of malignancy in solid solitary pulmonary nodules in northwest China

  2021influential citation
• Development and validation of a prediction model for malignant pulmonary nodules

  2021cites this paper
• Pulmonary Nodules

  2021cites this paper
• A simple assessment of lung nodule location for reduction in unnecessary invasive procedures

  2021cites this paper
• The probability of lung cancer in patients with incidentally detected pulmonary nodules: clinical characteristics and accuracy of prediction models.

  2021cites this paper
• Solitary pulmonary nodule malignancy predictive models applicable to routine clinical practice: a systematic review

  2021influential citation
• Narrative review of radiomics for classifying pulmonary nodules and potential impact on lung cancer screening

  2021cites this paper
• 低剂量螺旋CT肺癌筛查研究进展

  2020cites this paper
• Evaluation of models for predicting the probability of malignancy in patients with pulmonary nodules

  2020cites this paper
• Predicting Lung Cancer Risk of Incidental Solid and Subsolid Pulmonary Nodules in Different Sizes

  2020cites this paper
• A prediction model to evaluate the pretest risk of malignancy in solitary pulmonary nodules: evidence from a large Chinese southwestern population

  2020cites this paper
• Establishment and validation of a mathematical diagnosis model to distinguish benign pulmonary nodules from early non-small cell lung cancer in Chinese people

  2020cites this paper
• Artificial intelligence as a diagnostic tool for lung nodule evaluation

  2020cites this paper
• A novel clinical model for predicting malignancy of solitary pulmonary nodules: a multicenter study in chinese population

  2020cites this paper
• External validation and recalibration of the Brock model to predict probability of cancer in pulmonary nodules using NLST data

  2019cites this paper
• Implementation planning for lung cancer screening in China

  2019cites this paper
• Post-imaging pulmonary nodule mathematical prediction models: are they clinically relevant?

  2019cites this paper
• Computed tomography-guided biopsy of small lung nodules: diagnostic accuracy and analysis for true negatives

  2019cites this paper
• The Value of 18F-FDG PET/CT Mathematical Prediction Model in Diagnosis of Solitary Pulmonary Nodules

  2018cites this paper
• The Value of 18 F-FDG PET / CT Mathematical Prediction Model in Diagnosis of Solitary Pulmonary Nodules

  2018cites this paper
• Models to Estimate the Probability of Malignancy in Patients with Pulmonary Nodules

  2018cites this paper
• Development and validation of a nomogram to estimate the pretest probability of cancer in Chinese patients with solid solitary pulmonary nodules: A multi‐institutional study

  2017cites this paper
• Evaluation of the solitary pulmonary nodule: size matters, but do not ignore the power of morphology

  2017cites this paper
• Malignancy risk estimation of pulmonary nodules in screening CTs: Comparison between a computer model and human observers

  2017cites this paper
• Coil Localization-Guided Video-Assisted Thoracoscopic Surgery for Lung Nodules

  2017cites this paper
• LUNGx Challenge for computerized lung nodule classification

  2016cites this paper
• Pushing the boundaries: feature extraction from the lung improves pulmonary nodule classification

  2016cites this paper
• A New method for Predicting Early-stage Lung nodules based on PSO-SVM Hybrid Algorithm

  2016cites this paper
• Solid pulmonary nodule risk assessment and decision analysis: comparison of four prediction models in 285 cases

  2016cites this paper
• 实性孤立性肺结节诊断模型的建立

  2016cites this paper
• Predictive Accuracy of the PanCan Lung Cancer Risk Prediction Model -External Validation based on CT from the Danish Lung Cancer Screening Trial

  2015cites this paper
• (18)F-FDG-PET/CT in the assessment of pulmonary solitary nodules: comparison of different analysis methods and risk variables in the prediction of malignancy.

  2015cites this paper
• F-FDG-PET/CT in the assessment of pulmonary solitary nodules: comparison of different analysis methods and risk variables in the prediction of malignancy

  2015cites this paper
• Improved pulmonary nodule classification utilizing lung parenchyma texture features

  2015cites this paper
• The diagnostic accuracy of integrated positron emission tomography/computed tomography in the evaluation of pulmonary mass lesions in a tuberculosis-endemic area.

  2015cites this paper
• Establishing Assistant Diagnosis Models of Solitary Pulmonary Nodules Based on Intelligent Algorithms

  2015cites this paper
• Improved pulmonary nodule classification utilizing quantitative lung parenchyma features

  2015cites this paper
• Size of solitary pulmonary nodule was the risk factor of malignancy.

  2014cites this paper
• Limited value of logistic regression analysis in solid solitary pulmonary nodules characterization: A single‐center experience on 288 consecutive cases

  2014cites this paper
• Novel and Convenient Method to Evaluate the Character of Solitary Pulmonary Nodule-Comparison of Three Mathematical Prediction Models and Further Stratification of Risk Factors

  2013influential citation
• A Mathematical Prediction Model—Another Way to Evaluate the Character of SPN

  2012influential citation