Forecasting Individual Patients' Best Time for Surgery in Colon-Rectal Cancer by Tumor Regression during and after...

Area of Science:

• Oncology
• Mathematical Biology
• Surgical Oncology

Background:

• Locally advanced rectal cancer standard treatment involves neoadjuvant chemoradiotherapy followed by surgery.
• A complete clinical response may allow for a watch-and-wait strategy, necessitating reliable response biomarkers.
• Current methods for assessing treatment response and surgical timing can be improved.

Purpose of the Study:

• To evaluate the utility of mathematical growth models in predicting surgical timing after neoadjuvant therapy for rectal cancer.
• To identify quantitative differences in tumor response parameters between complete and partial responders.
• To explore the application of Gompertz's Law and Logistic Law for personalized treatment strategies.

Main Methods:

• Fitting Gompertz's Law and Logistic Law to tumor volume regression data during and after neoadjuvant chemoradiotherapy.
• Analyzing macroscopic growth parameters derived from mathematical models.
• Correlating model parameters with clinical response (partial vs. complete).

Main Results:

• Macroscopic parameters from Gompertz's Law and Logistic Law accurately reflect tumor regression during and after neoadjuvant therapy.
• Quantitative differences in these parameters were observed between patients with partial and complete responses.
• Model parameters provide a reliable basis for estimating treatment effects and optimal surgical timing.

Conclusions:

• Mathematical modeling of tumor growth offers a quantitative method to assess neoadjuvant chemoradiotherapy response in rectal cancer.
• This approach can aid in decision-making for watch-and-wait strategies versus early or delayed surgery.
• Personalized surgical timing based on individual tumor response is feasible using these mathematical tools.