Incorporating Individual Early Response in the Dose-Effect Relationship of Complete Pathological Response Following...

Area of Science:

• Radiation Oncology
• Medical Imaging
• Oncology

Background:

• Rectal cancer treatment relies on neoadjuvant radiochemotherapy.
• Predicting pathological complete response (pCR) is crucial for treatment planning.
• Current prediction methods lack precision in individualizing therapy.

Purpose of the Study:

• To develop and validate a predictive model for pCR in rectal cancer patients undergoing neoadjuvant radiochemotherapy.
• To integrate early magnetic resonance imaging (MRI) response with dose-effect modeling.
• To personalize radiation therapy by optimizing dose escalation strategies.

Main Methods:

• A dose-response model incorporating the early regression index (ERI) was developed using published data and a meta-analysis.
• The ERI was integrated as a dynamic dose-modifying factor within the Hill model.
• The model was validated on an external cohort using calibration plots and area under the curve (AUC) analysis.

Main Results:

• The validated Hill model demonstrated robust discriminative ability (AUC = 0.77).
• ERI-stratified analysis showed varying pCR rates (0% to 65%) across responsiveness categories.
• Dose escalation of 8.5 Gy EUD in moderate-to-good responders increased pCR by ~20%.

Conclusions:

• The ERI-dose model accurately predicts pCR and was validated on an external cohort.
• The model enables personalized radiation therapy by optimizing dose escalation based on ERI.
• This approach can improve treatment efficacy and reduce toxicity in rectal cancer patients.