Sargent, Daniel J.

6520 Background: We previously reported that alternative cutpoints and alternate categorical metrics to RECIST standards provided no meaningful improvement in OS prediction. Here, we sought to identify and validate continuous, longitudinal TM-based metrics for predicting OS. Methods: Data from 13 trials (1739 patients with sufficient imaging-based measurements from breast, lung or colon cancer) were randomly split (60:40) into training and validation datasets and landmarked at 12-weeks (w). Multiple candidate metrics that capture tumor curve trajectory were considered: first slope (or % change) from baseline to 6 w, last slope (or % change) from 6 w to 12 w, and indicator of increase (<5% (includes no increase or a decrease) vs. ≥5%) from the prior assessment (baseline to 6 w, 6 w to 12 w). Multivariable Cox models stratified by study and number of baseline target lesions (<3 vs. >3) and adjusted for baseline tumor burden (ratio of the sum of lesions/number of lesions) were fit for each tumor type. Predictive ability was assessed via concordance (c)-index (0.5: no association to 1.0: perfect association), and hazard ratios/p-values (HR/p). Results: Patients whose tumor size increased by at least 5% from the prior assessment, as well as whose tumors showed a positive slope (or % change) from 6 to 12 w (indicating tumor growth) had worse OS. The first slope (or % change) metric was less consistent, with an initial decrease in slope suggesting worse OS in some cases. Predictive ability of these metrics varied across tumor type with poorest prediction in Breast (Table). Conclusions: Continuous TM-based metrics performed no better than categorical RECIST metrics for OS prediction. [Table: see text]