S75 A Clinical Model to Estimate the Probability of Pulmonary Nodule Malignancy in a Population of Oncology Follow-up Patients. (12th November 2015) - Talwar, A; Pickup, LC; Willaime, JMY; Gooding, M; Kadir, T; Rahman, NM; Gleeson, F
Abstract : Introduction: The new BTS Pulmonary Nodule Guidelines 2015 recommend the use of composite prediction models to assess the pre-test probability of malignancy in patients presenting with pulmonary nodules (PNs). These models were not developed for use in patients with a history of malignancy within five years of presentation with a PN. In order to assist in the diagnosis of PNs, CT texture analysis has been proposed as a potential biomarker in tumour characterisation. 1 Image texture refers to the statistical analysis of spatial intensity variations of the pixels within an image to produce a CT texture score. 2 Aims and objectives: To evaluate four existing models for the probability of malignancy in the target population. To create and validate prediction models for probability of malignancy for patients undergoing oncology follow-up for an indeterminate PN. Methods: Retrospective data on clinical and radiological characteristics were collected from the medical records of 61 patients with a PN (mean diameter 7 mm, SD 4 mm) that had an active or previous history (within 5 years) of primary lung or extra-thoracic malignancy. The gold standard diagnosis of the nodules was established by histology or 2-year stable follow-up. Three multivariable logistic regression models were evaluated using a leave-one-out cross-validation strategy: Model 1: Age, Sex, Smoking status, Emphysema, Nodule diameter. Model 2: Age, Sex, Smoking status, Emphysema, CT Texture score. Model 3: CT Texture score only. The models' performance, measured using the area under the ROC curve (AUC), were reported and further compared to existing clinical models. Results: The highest AUC, 0.86, was obtained from Model 3 (texture score only). Utilising clinical parameters (Model 2) did not improve performance. In comparison, AUCs for previously published clinical models were 0.76(Mayo), 0.84(Herder), 0.66(VA) and 0.70(McWilliams) (Figure.1 ). Conclusion: This texture feature model is successful at discriminating benign from malignant nodules in a population of patients undergoing oncology follow-up. While not significantly better than the Herder model (which incorporates PET avidity), this model offers improved risk stratification for PNs in the absence of PET in this patient group. References: 1 RSNA 2014, SSC03-05 2 IEEE International Conference doi: 10.1109/SMC.2013.663