| Q37447387 | A Prediction Model Based on Biomarkers and Clinical Characteristics for Detection of Lung Cancer in Pulmonary Nodules |
| Q39077342 | A Prediction Model to Help with the Assessment of Adenopathy in Lung Cancer: HAL. |
| Q38674825 | A classifier integrating plasma biomarkers and radiological characteristics for distinguishing malignant from benign pulmonary nodules. |
| Q47562355 | A plasma miRNA signature for lung cancer early detection |
| Q37613397 | A predictive model to estimate the pretest probability of metastasis in patients with osteosarcoma |
| Q46062333 | Accuracy of Models to Identify Lung Nodule Cancer Risk in the National Lung Screening Trial |
| Q34996322 | Accuracy of clinicians and models for estimating the probability that a pulmonary nodule is malignant |
| Q36862617 | Added value of a serum proteomic signature in the diagnostic evaluation of lung nodules |
| Q37237879 | An Automated Method for Identifying Individuals with a Lung Nodule Can Be Feasibly Implemented Across Health Systems |
| Q33735718 | An Official American Thoracic Society Research Statement: A Research Framework for Pulmonary Nodule Evaluation and Management |
| Q38110322 | An update on the evaluation and management of small pulmonary nodules |
| Q51300743 | Analysis and validation of probabilistic models for predicting malignancy in solitary pulmonary nodules in a population in Brazil. |
| Q88034408 | Applying Risk Prediction Models to Optimize Lung Cancer Screening: Current Knowledge, Challenges, and Future Directions |
| Q97539162 | Approaches to lung nodule risk assessment: clinician intuition versus prediction models |
| Q85239194 | Assessing probability of malignancy in solid solitary pulmonary nodules with a new Bayesian calculator: improving diagnostic accuracy by means of expanded and updated features |
| Q33710108 | Assessment of the cancer risk factors of solitary pulmonary nodules |
| Q57151985 | Atypical cytologic diagnostic category in EUS-FNA of the pancreas: follow-up, outcomes, and predictive models |
| Q37721848 | Autoantibody Signature Enhances the Positive Predictive Power of Computed Tomography and Nodule-Based Risk Models for Detection of Lung Cancer |
| Q51621075 | British Thoracic Society guidelines for the investigation and management of pulmonary nodules. |
| Q34093217 | Circulating tumor microemboli diagnostics for patients with non-small-cell lung cancer. |
| Q50050527 | Clinical model to estimate the pretest probability of malignancy in patients with pulmonary focal Ground-glass Opacity |
| Q42243677 | Combining serum miRNAs, CEA, and CYFRA21-1 with imaging and clinical features to distinguish benign and malignant pulmonary nodules: a pilot study : Xianfeng Li et al.: Combining biomarker, imaging, and clinical features to distinguish pulmonary nod |
| Q92209024 | Comparison of prediction models with radiological semantic features and radiomics in lung cancer diagnosis of the pulmonary nodules: a case-control study |
| Q34785929 | Comparison of three mathematical prediction models in patients with a solitary pulmonary nodule |
| Q34294592 | Cost-effectiveness of initial diagnostic strategies for pulmonary nodules presenting to thoracic surgeons |
| Q35991264 | Decision making in patients with pulmonary nodules |
| Q27025828 | Evaluation of individuals with pulmonary nodules: when is it lung cancer? Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines |
| Q41903827 | Evaluation of undiagnosed solitary lung nodules according to the probability of malignancy in the American College of Chest Physicians (ACCP) evidence-based clinical practice guidelines |
| Q30417598 | Existing general population models inaccurately predict lung cancer risk in patients referred for surgical evaluation |
| Q92730808 | Is It All in the Eye of the Beholder? Comparing Pulmonologists' and Radiologists' Performance |
| Q46972581 | Limited value of logistic regression analysis in solid solitary pulmonary nodules characterization: a single-center experience on 288 consecutive cases |
| Q44770720 | Malignancy risk estimation of pulmonary nodules in screening CTs: Comparison between a computer model and human observers |
| Q27348798 | Management of Pulmonary Nodules by Community Pulmonologists: A Multicenter Observational Study |
| Q37600536 | Natural growth and disease progression of non-small cell lung cancer evaluated with 18F-fluorodeoxyglucose PET/CT. |
| Q35036257 | Novel and convenient method to evaluate the character of solitary pulmonary nodule-comparison of three mathematical prediction models and further stratification of risk factors |
| Q46035215 | Physician Assessment of Pretest Probability of Malignancy and Adherence With Guidelines for Pulmonary Nodule Evaluation. |
| Q30610197 | Predicting lung cancer prior to surgical resection in patients with lung nodules |
| Q30573941 | Probability of cancer in pulmonary nodules detected on first screening CT. |
| Q38254244 | Pulmonary nodule: a comprehensive review and update |
| Q37104316 | Quantitative Computed Tomography Classification of Lung Nodules: Initial Comparison of 2- and 3-Dimensional Analysis |
| Q43169482 | Risk stratification of solitary pulmonary nodules by means of PET using (18)F-fluorodeoxyglucose and SUV quantification |
| Q40233721 | Solid pulmonary nodule risk assessment and decision analysis: comparison of four prediction models in 285 cases |
| Q37615194 | The utility of nodule volume in the context of malignancy prediction for small pulmonary nodules |
| Q28087495 | Using Clinical Risk Models for Lung Nodule Classification |
| Q83650241 | [Slow progression of a ground glass lesion - need for long term vigilance] |
| Q53069561 | [The importance of risk models for management of pulmonary nodules]. |