ArticleESMO open2025
The use of machine learning models to predict progression-free survival and overall survival outcomes from waterfall plots in randomized clinical trials (MAP-OUTCOMES).
Article in ESMO open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Prediction of stunting and its socioeconomic determinants among adolescent girls in Ethiopia using machine learning algorithms.PloS one · 2025Article
- Employing supervised machine learning algorithms for classification and prediction of anemia among youth girls in Ethiopia.Scientific reports · 2024Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDepth 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
methodsA 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 processed through a semi-automatic extraction process. We developed the MAP-OUTCOMES (MAchine learning model to Predict PFS and OS OUTCOMES) model using regularized logistic regression. This model was applied to a training set comprising 70% of the data, and 30% was used for a test set.
resultsA total of 91 unique RCTs were identified, and 82 (93 trial pairs) retained for the ML analysis. Most of the trials were phase III (75%), with 67% using PFS as the primary endpoint and a mean sample size of 350 patients per arm. The most common tumor type was genitourinary (22%), and small-molecule targeted agents (27%) were the most frequent regimen. The model's performance achieved 71% accuracy [95% confidence interval (CI) 0.536-0.862, P = 0.18] with an area under the curve (AUC) of 65% (95% CI 0.333-0.938, P = 0.157) and area under the precision-recall curve (AUPRC) of 90% (95% CI 0.779-0.995, P = 0.171) in the 28 trials used for the test set.
conclusionsThe MAP-OUTCOMES model demonstrated the feasibility of using ML to predict survival-based outcomes from waterfall plots, thus providing a potential tool for early trial evaluation. Improving the model's performance with more training data and creating independent datasets are necessary steps to assess its generalizability for prospective clinical applications.
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