Objectives: The quantitative analysis of tumor progression-monitored during and immediately after therapeutic interventions-can yield valuable insights into both long-term disease dynamics and treatment efficacy. Methods: We used a computational approach designed to support clinical decision-making, with a focus on personalized patient care, based on modeling therapy effects using effective parameters of the Gompertz law. Results: The method is applied to data from in vivo models undergoing neoadjuvant chemoradiotherapy, as well as conventional and FLASH radiation treatments. Conclusions: This user-friendly, phenomenological model captures distinct phases of treatment response and identifies a critical dose threshold distinguishing complete response from partial response or tumor regrowth. These findings lay the groundwork for real-time quantitative monitoring of disease progression during therapy and contribute to a more tailored and predictive clinical strategy.

Biotechnology Research Center Technology Innovation Institute Yas Island Abu Dhabi P O Box 9639 United Arab Emirates

Department of Medicine and Surgery University of Enna Kore 94100 Enna Italy

Faculty of Mathematics and Physics Charles University 5 Holešovičkách 2 18000 Prague 8 Czech Republic

Institute for Applied Computing National Research Council of Italy Via dei Taurini 19 00185 Rome Italy

Istituto Nazionale Fisica Nucleare Sezione di Catania 95123 Catania Italy

Istituto Oncologico del Mediterraneo 95128 Viagrande Italy

REM Radioterapia 95029 Viagrande Italy

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