Long the domain of astrologers and tarot card readers, prediction has recently become downright fashionable. While quant-minded individuals like Billy Beane and Nate Silver have achieved fame and fortune using probabilistic forecasting, dozens of smartphone apps deliver the predictive insight of clinical risk scores to doctors’ fingertips. Why all the enthusiasm? Accurate predictions allow us to prepare for the future.
Testing their predictive mettle in this week’s NEJM, Dr. Annette McWilliams (British Columbia Cancer Agency, Vancouver, Canada) and colleagues ask a deceptively simple research question: If a low-dose computed tomography (LDCT) lung cancer screening test detects a lung nodule, can we use the information at hand to accurately predict if it is malignant? Using clinical and LDCT data from 1871 current or former smokers in the PanCan study, the investigators developed a model to predict when a newly discovered nodule was cancerous. Model variables included age, family history of lung cancer, and the presence of emphysema as well as nodule size, type, and location. Next, the investigators tested this prediction model in a cohort of 1090 current and former smokers enrolled in several British Columbia Cancer Agency chemoprevention trials. They found their model successfully discriminated between higher-risk and lower-risk nodules even within this validation cohort (AUC = 0.97, 95%CI 0.95-0.99), suggesting that the model can also be generalized to other... |
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