This project aims to develop and evaluate the impact of a multivariable lung nodule malignancy predictor for the detection of lung nodules, which may go on to develop into cancerous growth. We know that the risk of these nodules is not uniform, and some have characteristics that may increase the risk for cancer development. To date, standard practice for assessing malignancy risk for incidental lung nodules is either the subjective risk assessment of an experienced thoracic radiologist or performed using a linear regression model referred to as the Brock model. The Brock model was developed from participants enrolled in the Pan-Canadian Early Detection of Lung Cancer Study (Pan-Canadian Detection of Lung Cancer Study), has been validated in lung cancer screening and clinical populations and is recommended by the British Thoracic Society guidelines for pulmonary nodules.
New advances in the field of artificial intelligence (AI) have resulted in alternative malignancy prediction models that have shown to outperform the Brock model in lung cancer screening cohorts. Development and validation of similar prediction models for incidental lung nodules in clinical practice have not been established. The main reason for this is that it is challenging to build relevant cohorts due to an absence of well indexed datasets at local institutes together with a relatively low incidence of lung cancer in a single institute. This PICTURES exemplar can meet these limitations and provide a well-defined and stratified cohort including both proven benign and malignant incidental lung nodules.
The output of this study will be a multivariable predictor that uses logistic regression to estimate likelihood of lung nodule malignancy from multiple variables, such as age, sex, family history, and nodule characteristics derived from CT scans.
The multivariable predictor will be publicly accessible and function in a similar way to existing models such as the Lung Nodule Risk Calculators found here https://www.sts.org/resources/lung-nodule-resources/lung-nodule-risk-calculators. We believe our model will outperform previous models because it has been developed using routinely collected, population level data and information contained in CT scans.
The primary benefit of this exemplar project will be the improvement of existing publicly available risk assessment tools, to support early detection of lung cancer.
CT chest scan:
