The use of machine learning models to predict progression-free survival and overall survival outcomes from waterfall plots in randomized clinical trials (MAP-OUTCOMES).

ABSTRACT:

Background

Depth of tumor response (DepOR) of individual patients, as visualized by waterfall plots, is a short-term endpoint that may represent a surrogate for survival-based outcomes such as progression-free survival (PFS) and overall survival (OS). We hypothesized that PFS/OS could be predicted from waterfall plots in randomized clinical trials (RCTs) using a novel machine-learning (ML) computational model.

Materials and methods

A literature-based search was carried out for phase II/III RCTs testing noncytotoxic systemic therapy, which included waterfall plots with corresponding PFS/OS results. Studies were defined as positive or negative based on achievement of an a priori-stated primary endpoint. Trial data and images of waterfall plots were manually extracted and then

SUBMITTER: Alshankati K 

PROVIDER: S-EPMC12281959 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature