Cancer interception during treatment: using growth kinetics to create a continuous variable for assessing disease...

Area of Science:

• Oncology
• Mathematical Modeling
• Biostatistics

Background:

• Eleven mathematical models of tumor growth were applied to extensive clinical trial data.
• A biexponential model previously demonstrated efficacy in describing tumor growth kinetics and correlating with survival.
• This study aimed to extend the analysis to more cancer types and evaluate alternative models for challenging datasets.

Purpose of the Study:

• To validate and extend the application of a simple mathematical model for tumor growth kinetics.
• To assess the utility of alternative models when the primary model fails.
• To evaluate the correlation between tumor growth rate and overall survival across diverse cancer types.

Main Methods:

• Analysis of data from 17,140 patients, including imaging and serum tumor markers.
• Application of a biexponential model to determine tumor growth (g) and regression (d) rates.
• Assessment of seven alternative models for datasets not fitting the biexponential model.
• Examination of the association between continuous growth rate (g rate) and overall survival.

Main Results:

• The biexponential model successfully described tumor growth and regression in 86% of patients.
• An alternative model fit the data for an additional 7% of patients.
• Tumor growth rate showed an inverse correlation with overall survival, consistent across different histologies.
• Growth rates could be estimated even during net regression phases with sufficient data points.

Conclusions:

• A simple mathematical model effectively quantifies tumor growth across various cancers.
• The estimated tumor growth rate serves as a robust marker for therapy efficacy.
• This approach allows for the estimation of treatment-resistant cancer cell subpopulations.