Issues

The cover image is adapted from Figure 1 and Figure 2 in the article, “Machine Learning and Real-World Data to Predict Lung Cancer Risk in Routine Care,” by Chandran and colleagues. The prognostic relationship between diet and endometrial cancer survival remains largely unknown. According to the National Cancer Institute SEER database, while the median age for lung cancer is 71 years, while almost 30% of lung cancers are diagnosed in individuals younger than 65 years of age. Therefore, as a proof-of-concept, this study assessed feasibility in using machine learning to predict lung cancer risk over a 3-year period, and to identify a ‘high-risk subset’ among a relatively young cohort of patients 45–65 years of age, in a large Electronic Health Record (EHR) dataset. Although the incidence of lung cancer is lower in younger individuals, the model aimed to identify adults that comprise a high-risk group that could benefit from more detailed risk assessment. Over 4.7 million individuals, aged 45–65 years with no history of any cancer or lung cancer screening, diagnostic, or treatment procedures, with an outpatient visit in 2013 were identified in the Optum^® De-identified HER Dataset. The model performed well temporally and externally, while performance was reduced for Asians and Hispanics. The high-dimensional model trained using big data identified a subset of patients with high lung cancer risk. The model demonstrated transportability to EHR and claims data, while underscoring the need to assess racial disparities when using machine learning methods. This internally and externally validated real-world data-based lung cancer prediction model is available on an open-source platform for broad sharing and application. For more information, see the article beginning on page 337.