Abstract Objectives Clinical prediction models assess the likelihood of malignancy in pulmonary nodules detected by computed tomography (CT). This study aimed to validate four such models in a UK population of patients with pulmonary nodules. Three models used clinical and CT characteristics to predict risk (Mayo Clinic, Veterans Association, Brock University) with a fourth model (Herder et al. [4] ) additionally incorporating 18Fluorine-Fluorodeoxyglucose (FDG) avidity on positron emission tomography–computed tomography (PET–CT). Materials and methods The likelihood of malignancy was calculated for patients with pulmonary nodules (4–30 mm diameter) and data used to calculate the area under the receiver operating characteristic curve (AUC) for each model. The models were used in a restricted cohort of patients based on each model's exclusion criteria and in the total cohort of all patients. Results Two hundred and forty-four patients were studied, of whom 139 underwent FDG PET–CT. Ninety-nine (40.6%) patients were subsequently confirmed to have malignant nodules (33.2% primary lung cancer, 7.4% metastatic disease). The Mayo and Brock models performed similarly (AUC 0.895 and 0.902 respectively) and both were significantly better than the Veterans Association model (AUC 0.735, p Conclusions The Mayo and Brock models showed good accuracy for determining likelihood of malignancy in nodules detected on CT scan. In patients undergoing FDG PET–CT for nodule evaluation, the highest accuracy was seen for the model described by Herder et al. incorporating FDG avidity.
Decisions about managing solitary pulmonary nodules often involve estimates of the likelihood that the nodule is malignant. We used Bayes’ theorem to devise a simple scheme for estimating the likelihood that a solitary pulmonary nodule is malignant based on the diameter of the nodule, the patient’s age and history of cigarette smoking, and data on the overall prevalence of malignancy in solitary nodules. This method may improve the accuracy of estimating the likelihood of malignancy for individu...
A B S T R AC T Background Major issues in the implementation of screening for lung cancer by means of lowdose computed tomography (CT) are the definition of a positive result and the management of lung nodules detected on the scans. We conducted a populationbased prospective study to determine factors predicting the probability that lung nodules detected on the first screening low-dose CT scans are malignant or will be found to be malignant on follow-up. Methods We analyzed data from two cohorts...
Abstract Background: Estimating the clinical probability of malignancy in patients with a solitary pulmonary nodule (SPN) can facilitate the selection and interpretation of subsequent diagnostic tests. Methods: We used multiple logistic regression analysis to identify independent clinical predictors of malignancy and to develop a parsimonious clinical prediction model to estimate the pretest probability of malignancy in a geographically diverse sample of 375 veterans with SPNs. We used data from...
Background The added value of 18 F-fluorodeoxyglucose (FDG) positron emission tomography (PET) scanning as a function of pretest risk assessment in indeterminate pulmonary nodules is still unclear. Objective To obtain an external validation of the prediction model according to Swensen and colleagues, and to quantify the potential added value of FDG-PET scanning as a function of its operating characteristics in relation to this prediction model, in a population of patients with radiologically ind...
Background: A clinical prediction model to identify malignant nodules based on clinical data and radiological characteristics of lung nodules was derived using logistic regression from a random sample of patients (n=419) and tested on data from a separate group of patients (n=210). Objective: To use multivariate logistic regression to estimate the probability of malignancy in radiologically indeterminate solitary pulmonary nodules (SPNs) in a clinically relevant subset of patients with SPNs that...
Abstract null null Introduction null To analyse clinicopathological characteristics of patients operated for pulmonary solitary nodule (PSN) and 18F-FDG integrated PET-CT scan after surgical resection. null null null Methodology null Retrospective study on a prospective database of patients operated from January 2007 to October 2017 for PSN without preoperative diagnosis. Dependent variable was anatomopathological result (benign vs malignant) of PSN. Variables of the study were: age, sex, PET-CT...
Purpose of review Lung cancer remains the leading cause of cancer-related death in the United States, with poor overall 5-year survival. Early detection and diagnosis are key to survival as demonstrated in lung cancer screening trials. However, with increasing implementation of screening guidelines and use of computed tomography, there has been a sharp rise in the incidence of indeterminate pulmonary nodules (IPNs). Risk stratification of IPNs, particularly those in the intermediate-risk categor...
Introduction Implementation of low-dose chest computed tomography (CT) lung cancer screening and the ever-increasing use of cross-sectional imaging are resulting in the identification of many screen- and incidentally detected indeterminate pulmonary nodules. While the management of nodules with low or high pre-test probability of malignancy is relatively straightforward, those with intermediate pre-test probability commonly require advanced imaging or biopsy. Noninvasive risk stratification tool...
Last. Gary D. Kao(UPenn: University of Pennsylvania)H-Index: 35
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Recent treatment advances have improved outcomes for patients with non-small cell lung cancer (NSCLC), often utilizing tumor molecular characterization to identify targetable mutations. This is further enhanced by advancements in “liquid biopsies”, using peripheral blood for noninvasive, serial sampling of tumor biology. While tumor genomic alterations have established therapeutic implications in metastatic NSCLC, research is also ongoing to develop applications for tissue and liquid biomarkers ...
Lung cancer is the leading cause of cancer related death around the world although early diagnosis remains vital to enabling access to curative treatment options. This article briefly describes the current role of imaging, in particular 2-deoxy-2-[18F]fluoro-D-glucose (FDG) PET/CT, in lung cancer and specifically the role of artificial intelligence with CT followed by a detailed review of the published studies applying artificial intelligence (ie, machine learning and deep learning), on FDG PET ...
Background. Identifying malignant pulmonary nodules and detecting early-stage lung cancer (LC) could reduce mortality. This study investigated the clinical value of a seven-autoantibody (7-AAB) panel in combination with the Mayo model for the early detection of LC and distinguishing benign from malignant pulmonary nodules (MPNs). Methods. The concentrations of the elements of a 7-AAB panel were quantitated by enzyme-linked immunosorbent assay (ELISA) in 806 participants. The probability of MPNs ...
Pulmonary cancer is one of the most dangerous cancers with a high incidence and mortality. An early accurate diagnosis and treatment of pulmonary cancer can observably increase the survival rates, where computer-aided diagnosis systems can largely improve the efficiency of radiologists. In this article, we propose a deep automated lung nodule diagnosis system based on three-dimensional convolutional neural network (3D-CNN) and support vector machine (SVM) with multiple kernel learning (MKL) algo...
PURPOSE Lung cancer is the leading cause of cancer death and there have been clinical prediction models. This study aimed to evaluate the diagnostic performance of published models and create new models to evaluate the probability of malignant solitary pulmonary nodules (SPNs) in Chinese population. METHODS We consecutively enrolled 2061 patients with SPNs from West China Hospital between January 2008 and December 2016, each SPN was pathologically confirmed. First, four published prediction mode...
Health care is the maintenance of health via the prevention, diagnosis, and treatment of disease. The disease that persists over a long period of time is known as chronic disease. Chronic diseases may create additional activity restrictions. Common chronic conditions include lung disease, heart stroke, cancer, obesity, and diabetes. Chronic diseases usually show no symptoms and hence not diagnosed in advance. Hence, it is necessary to predict the patient-specific chronic diseases in early stage ...