ArticleThe oncologist2025
Cancer interception during treatment: using growth kinetics to create a continuous variable for assessing disease response.
Article in The oncologist, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Abiraterone vs Enzalutamide Among US Veterans With Metastatic Hormone-Sensitive Prostate Cancer.JAMA network open · 2025Article
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Authors and funding
5 authors.
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Abstract
backgroundWe applied 11 mathematical models of tumor growth to clinical trial data available from public and private sources. We have previously described the remarkable capacity for a simple biexponential model to describe the kinetics of tumor growth, fit thousands of datasets, and correlate with overall survival. We sought to extend our analysis to additional tumor types and to evaluate whether alternate growth models could describe the time course of disease burden in the small subset of patients in whom the biexponential model failed. PATIENTS AND
methodsData were available from 17 140 patients including imaging data for 3346 patients and serum levels of tumor markers for 13 794 patients. Data from patients were analyzed using the biexponential model to determine rates of tumor growth (g) and regression (d); for those whose data could not be described by this model, fit of their data was assessed using seven alternative models. The rates of tumor growth (g rate), a continuous variable, were examined for association with the gold-standard of clinical trials, overall survival.
resultsAs we have previously reported, data from most patients fit a simple model of exponential growth and exponential regression (86%). Data from another 7% of patients fit an alternative model. We found growth rate correlates inversely with overall survival, remarkably even when data from various histologies are considered together. For patients with multiple timepoints of tumor measurement, growth rate could often be estimated even during the phase when only net regression could be discerned.
conclusionsValidation of a simple mathematical model across different cancers and its application to existing clinical data allowed estimation of the rate of growth of a treatment resistant subpopulation of cancer cells. The quantification of available clinical data using the growth rate of tumors in individual patients and its strong correlation with overall survival makes the growth rate an excellent marker of the efficacy of therapy.
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