Shishir Karthik

Risk of malignancy in pulmonary nodules: A validation study of four prediction models

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 (18)Fluorine-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 (4-30mm 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 models 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<0.001 and p=0.002 respectively). In patients undergoing FDG PET-CT, the Herder model had significantly higher accuracy than the other three models (AUC 0.924). When the models were tested on all patients in the cohort (i.e. including those outside the original model inclusion criteria) AUC values were reduced, yet remained high especially for the Herder model (AUC 0.916). For sub-centimetre nodules, AUC values for the Mayo and Brock models were 0.788 and 0.852 respectively. 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.

Lung cancer stage-shift following a symptom awareness campaign

Background Lung cancer outcomes in the UK are worse than in many other developed nations. Symptom awareness campaigns aim to diagnose patients at an earlier stage to improve cancer outcomes. Methods An early diagnosis campaign for lung cancer commenced in Leeds, UK in 2011 comprising public and primary-care facing components. Rates of community referral for chest X-ray and lung cancer stage (TNM seventh edition) at presentation were collected from 2008 to 2015. Linear trends were assessed by χ2 test for trend in proportions. Headline figures are presented for the 3 years pre-campaign (2008–2010) and the three most recent years for which data are available during the campaign (2013–2015). Findings Community-ordered chest X-ray rates per year increased from 18 909 in 2008–2010 to 34 194 in 2013–2015 (80.8% increase). A significant stage shift towards earlier stage lung cancer was seen (χ2(1)=32.2, p<0.0001). There was an 8.8 percentage point increase in the proportion of patients diagnosed with stage I/II lung cancer (26.5% pre-campaign vs 35.3% during campaign) and a 9.3% reduction in the absolute number of patients diagnosed with stage III/IV disease (1254 pre-campaign vs 1137 during campaign). Interpretation This is the largest described lung cancer stage-shift in association with a symptom awareness campaign. A causal link between the campaign and stage-shift cannot be proven but appears plausible. Limitations of the analysis include a lack of contemporary control population.

S74 Assessing the diagnostic accuracy of the British Thoracic Society algorithm for investigation of solid pulmonary nodules

Background The British Thoracic Society guidelines (2015) on the investigation and management of pulmonary nodules recommend the use of two risk prediction tools to assess the likelihood of malignancy in solid pulmonary nodules (Brock model following initial CT and the model described by Herder et al. following PET-CT). Management strategies are suggested on the basis of these risk assessments. The aim of this study was to assess the performance of this algorithm in patients with solid pulmonary nodules recruited from a UK teaching hospital. Method Patients with solid pulmonary nodules (4–30 mm) were retrospectively identified from the lung cancer MDT and a nodule follow-up clinic (n = 221). All patients had a final diagnosis confirmed by histology or radiological stability on 2-year follow up. Results The median age was 69 years. The prevalence of malignancy was 37.1% (29.9% primary lung cancer, 7.2% metastatic disease). 25 patients where PET-CT was recommended by the guideline but did not occur were excluded from subsequent analysis. Ten patients had nodules <5 mm and therefore would have been immediately discharged. All these nodules were benign. CT surveillance was recommended for 106 patients (37 with nodule <8 mm, 45 with malignant risk of <10% following initial CT, and 24 with malignant risk of <10% following PET-CT). 94% of these 106 patients had benign disease, 2% had primary lung cancer and 4% had metastatic disease. Surgical/non-surgical treatment was recommended for 58 patients where the malignant risk was >70% following PET-CT. 81% of these patients had primary lung cancer, 10% had metastatic disease and 9% were benign. For nodules with a malignant risk of between 10 and 70% following PET-CT, the guidelines recommend consideration of biopsy with alternatives of CT surveillance or surgical resection depending on patient preference and fitness. Of the 22 patients with nodules in this range, 36% were benign, 55% primary lung cancer and 9% metastatic disease. Conclusion The solid nodule algorithm from the BTS guidelines shows good accuracy in discriminating benign from malignant nodules, recommending appropriate management in a high proportion of cases. Further studies should evaluate this and the other management algorithms with prospectively collected data.