Nomogram predicting response after chemoradiotherapy in rectal cancer using sequential PETCT imaging: a multicentric...

Area of Science:

• Oncology
• Radiology
• Medical Imaging

Background:

• Locally advanced rectal cancer (LARC) requires tailored treatment strategies.
• Accurate prediction of treatment response is crucial for optimizing patient management.

Purpose of the Study:

• To develop and externally validate a predictive model for pathologic complete response (pCR) in LARC.
• The model integrates clinical features and early (18)F-FDG PET/CT imaging.
• To aid treatment decisions during chemoradiotherapy (CRT).

Main Methods:

• Prospective data from the THUNDER trial were used for model training and validation.
• Clinical parameters (age, gender, cT, cN stage) and PET parameters (SUVmax, SUVmean, MTV, maximal diameter) were analyzed.
• Response indices from sequential PET scans were calculated.
• Multivariate logistic regression identified predictive features for pCR (ypT0N0).

Main Results:

• The model achieved pCR rates of 21.4% (training) and 23.1% (validation).
• Key predictors included cT-stage, cN-stage, and response indices of SUVmean and maximal tumor diameter.
• Model performance (AUC) was 0.78 (training) and 0.70 (validation).
• The high probability group achieved 100% correct pCR prediction in training and 67% in validation.

Conclusions:

• An accurate, externally validated predictive model for pCR in LARC was developed.
• The model, available at www.predictcancer.org, can guide treatment adjustments during CRT.
• It helps identify complete responders for watchful waiting, good responders for RT boost, and poor responders for intensified chemotherapy.