Tumor Volume Regression during and after Radiochemotherapy: A Macroscopic Description

Area of Science:

• Oncology
• Medical Imaging
• Algorithm Development

Background:

• Tumor volume regression is crucial for guiding clinical decisions in cancer therapy.
• Quantitative assessment of treatment response can inform dose modification and surgical timing.
• Current methods may lack precise, patient-specific tracking of tumor volume dynamics.

Purpose of the Study:

• To develop and validate a macroscopic algorithm for quantitatively monitoring tumor volume changes over time.
• To assess the algorithm's utility in guiding treatment decisions during and after radiochemotherapy.
• To evaluate the algorithm's performance in preclinical models and clinical patient data.

Main Methods:

• Development of a macroscopic algorithm for quantitative tumor volume analysis.
• Validation of the algorithm using cell-line xenografts in mice.
• Application of the algorithm to patient data from radiochemotherapy trials.

Main Results:

• The algorithm successfully quantified tumor volume regression and evolution during and after treatment.
• Preclinical validation demonstrated the method's reliability.
• Successful application to patient data showed its clinical relevance for monitoring treatment response.

Conclusions:

• The proposed algorithm provides a quantitative, patient-oriented method for tracking tumor volume dynamics.
• This tool can support clinical decision-making for optimizing cancer treatment strategies.
• The algorithm shows promise for personalized cancer care and treatment response evaluation.