Thymectomy for Myasthenia Gravis: Recent Observations and Comparisons With Past Experience
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Editor's Choice: Inflection Points
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2021, Computerized Medical Imaging and GraphicsCitation Excerpt :Studies using both imaging and clinical features are reported for identifying malignant nodules. Early in 1989, Edwards et al. (1989) collected clinical and imaging data for 165 retrospective patients and built a Bayesian algorithm to estimate benign and malignancy. The same model yielded a sensitivity of 96 % and a specificity of 89 % for a prospective study with 100 consecutive cases (47 cases with lesion < 3 cm; 53 cases with lesion > 3 cm).
A practical algorithmic approach to the diagnosis and management of solitary pulmonary nodules: Part 2: Pretest probability and algorithm
2013, ChestCitation Excerpt :Probability of malignancy can then be calculated easily from the odds. A number of authors developed this approach during the 1970s and 1980s,18–23 but Gurney et al24,25 provided the most rigorous test. They derived likelihood ratios from a database of 3,858 patients and then validated the model by comparing it with subjective clinical assessments.
Prediction of true positive lung cancers in individuals with abnormal suspicious chest radiographs-A prostate, lung, colorectal, and ovarian cancer screening trial study
2009, Journal of Thoracic OncologyCitation Excerpt :Decision analyses have indicated that incorporating probability of malignant SPN is important in guiding clinical management of SPN.5,35,36 Some predictive models have been developed on unrepresentative samples or have required extensive workup data,4,6,37 and they have not gained widespread acceptance. Swensen et al.7 developed a model to predict whether SPN (4–30 mm) were lung cancer.
Computer-Aided Diagnosis of Lung Cancer and Pulmonary Embolism in Computed Tomography-A Review
2008, Academic RadiologyCitation Excerpt :Some criteria have been suggested to estimate the likelihood of malignancy of solitary pulmonary nodules (13,22–27). Computer-assisted classification of malignant and benign lung nodules has been attempted and promising results have been reported (28–32). Gurney et al (25,33) used Bayesian analysis and an artificial neural network (34) to classify radiographic and clinical features and achieved a higher accuracy than subjective classification by radiologists.
The pulmonologist's perspective regarding the solitary pulmonary nodule
2002, Seminars in Thoracic and Cardiovascular Surgery
We thank Jeffrey Gorbin, DrPH, Senior Statistician, Department of Biomathematics, UCLA School of Medicine, for the statistical analysis of the data.