S15 Improving the risk stratification for malignancy in small pulmonary nodules from an unselected patient population. (15th November 2017) - Talwar, A; Willaime, JMY; Rahman, N; Gooding, M; Kadir, T; Gleeson, F
Abstract : Introduction: Distinguishing between benign and malignant small pulmonary nodules (PNs) detected on CT scanning is a significant challenge. Such nodules are commonly detected in clinical practice as incidental findings or in patients with a history of prior malignancy. CT texture analysis (CTTA) has been proposed as a potential imaging biomarker in tumour characterisation. Image texture refers to the statistical analysis of spatial intensity variations of the pixels within an image to produce a CT texture score. This score is then mapped onto a probability of malignancy from 0–1. Aims and Objectives: To create a registry of patients with small solid PNs from an unselected population of patients. Patient demographic data were combined with information acquired from CT derived parameters such as shape, size, and texture analysis (CTTA) to develop and validate a generalised linear model to determine the probability of malignancy of PNs. A parallel prospective interventional cohort study was also conducted to assess whether CTTA repeatability was comparable to automatic volumetric measurements when a patient is scanned twice on the same day. Methods: Between January 2012 to September 2014, 1008 patients presenting with small solid PNs were identified. The gold standard diagnosis of the nodules was established by histology or nodule stability at 2 years of CT follow-up. Results: The prevalence of malignant PNs was 31.6% (319/1008). Significant independent predictors of malignancy included prior history of malignancy within 5 years (OR=117.4, (95% confidence interval(CI)):67.1 to 272.8, p<0.001); larger nodule diameter (OR=9.7, CI: 4.1 to 17.6, p<0.001); nodule count (OR=1.6, CI:1.3 to 1.8, p<0.001) and nodule spiculation (OR=118.4, CI:61.9 to 772.3, p<0.001). The models' performance using the area under the ROC curve (AUC) was 0.969. When CTTA was used alone the AUC was 0.800 (figure 1 ). CTTA displayed ULR and LLR below ±17.8%, comparable to volume using Bland-Altman and also had high repeatability {CCC (0.84≤CCC≤0.99)}. Conclusion: This study has highlighted the potential clinical utility of CTTA in the risk stratification of PNs. It has also shown that CTTA is a highly repeatable imaging biomarker of malignancy, akin to volume measurements but with the advantage of not requiring imaging follow-up.