Automated imaging as an adjunct to serum and clinical biomarkers: a new validated prediction tool for metastatic castration-resistant prostate cancer

Abstract Purpose: Contemporary prostate cancer prognostic models do not include imaging and generally are based on pretreatment parameters. We sought to develop an externally validated model that used novel quantification of soft-tissue and bone disease, integrated with standard clinical and serum biomarkers, at baseline and up to 6 months of treatment. Patients and Methods: Two randomized phase 3 trials, Cougar COU-AA-302 (NCT00887198; for derivation) and Alliance A031201 (NCT01949337; for validation), were used to evaluate the added value of early on-treatment bone imaging and over 1000 radiomics features on CT, used in conjunction with clinical and serum biomarkers in first-line metastatic castration-resistant prostate cancer. Predictive accuracy measures were computed to determine whether these early on-treatment biomarkers could reliably sort patients into risk groups that inform overall survival and whether the patient-specific biomarker risk score could precisely predict their overall survival time. Results: Imaging improved patient risk stratification but did not improve individual survival predictions. The strongest risk prediction model was developed for patients with bone-only metastases. This model was also the least complex, relying on just 16 risk factors, whereas all other models were high-dimensional, incorporating approximately 1100 intercept and 1100 slope features from the early on-treatment biomarker trajectories. Conclusions: Pretreatment and early on-treatment serum and automated quantitative imaging markers can well discriminate risk of death. Imaging improves this risk categorization relative to serum biomarkers alone. Such models can give early outcome predictions and can be used in future trials that involve imaging, even using traditional techniques such as bone scintigraphy.