Pulmonary nodules: Assessing the imaging biomarkers of malignancy in a "coffee-break".

INTRODUCTION: Although nodule volumetry is a recognized biomarker of malignancy in pulmonary nodules (PNs), caution is needed in its interpretation because of variables such as respiratory volume variation and inter-scan variability of up to 25%. CT Texture Analysis (CTTA) is a potential independent biomarker of malignancy but inter-scan variability and respiratory volume variation has not been assessed. METHODS: In this prospective cohort study, 40 patients (20 with an indeterminate PN and 20 with pulmonary metastases) underwent two LDCTs within a 60-min period (the "Coffee-break") with the aim of assessing the repeatability of CTTA and semi-automated volume measurements. Texture features were extracted from each automatic contoured region surrounding the PN. Patients were also randomized to two inspiratory control groups: normal breath hold, and controlled lung volume to study the influence of inspiratory control on these measurements. RESULTS: The mean difference in volume between the two scans was 6.3%,SD:29.9%. The textural features displayed 95% CI below ±17.8%, and were less variable than nodule volume (95%CI ±â€¯28.9%). All features had high repeatability, calculated by the concordance correlation coefficient, (0.84 ≤ CCC ≤ 0.99). All measurements were more repeatable for the controlled lung volume group than the normal breath-hold group. CONCLUSION: CTTA repeatability was comparable to automatic volumetric measurements, and appears to be improved using controlled volume breath holding.

A retrospective validation study of three models to estimate the probability of malignancy in patients with small pulmonary nodules from a tertiary oncology follow-up centre.

AIM: To estimate the probability of malignancy in small pulmonary nodules (PNs) based on clinical and radiological characteristics in a non-screening population that includes patients with a prior history of malignancy using three validated models. MATERIALS AND METHODS: Retrospective data on clinical and radiological characteristics was collected from the medical records of 702 patients (379 men, 323 women; range 19-94 years) with PNs ≤12 mm in diameter at a single centre. The final diagnosis was compared to the probability of malignancy calculated by one of three models (Mayo, VA, and McWilliams). Model accuracy was assessed by receiver operating characteristics (ROC). The models were calibrated by comparing predicted and observed rates of malignancy. RESULTS: The area under the ROC curve (AUC) was highest for the McWilliams model (0.82; 95% confidence interval [CI]: 0.78-0.91) and lowest for the Mayo model (0.58; 95% CI: 0.55-0.59). The VA model had an AUC of (0.62; 95% CI: 0.47-0.64). Performance of the models was significantly lower than that in the published literature. CONCLUSIONS: The accuracy of the three models is lower in a non-screening population with a high prevalence of prior malignancy compared to the papers that describe their development. To the authors' knowledge, this is the largest study to validate predictive models for PNs in a non-screening clinically referred patient population, and has potential implications for the implementation of predictive models.