What is the real value of predictive models?

Abstract

Identification of image-based phenotypes that can be combined with clinical data and emerging biomarkers has the potential for great clinical impact. The development of these predictive models will not only improve evaluation, risk stratification, diagnosis and treatment of non-small cell lung cancer (NSCLC) patients, but also, make clinicians work easier, allowing for an integrated and practical analysis of all patients' characteristics (1).However, regardless of the increasing amount of data obtained from each patient, clinical utility and implementation of these models remain controversial and more challenging than initially expected.Hannequin et al.(2) present the results of their retrospective study where positron emission tomography/ computed tomography (PET/CT) and CT scan radiomic features fail to predict overall survival (OS) and progressionfree survival (PFS) when compared to other traditionally used clinical variables, as gender and stage.In this issue of Translational Lung Cancer Research, Hannequin et al. describe the results of different predictive models including clinical characteristics, as gender and tumor stage, radiomic features obtained from CT scan and PET-CT images and histological features as PD-L1 expression. Despite the intrinsic power author's observe in CT and PET-CT radiomic features to predict survival, the truth is their models are not able to predict OS and PFS better than traditional models based in clinical data.As the author's describe, among the main limitations