Related Experiment Video
Updated: May 1, 2026
08:34
Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies
Published on: February 6, 2019
20.1K
Clinical Prediction Models for Contact X-Ray Brachytherapy in Managing Rectal Cancers: A Scoping Review
Muneeb Ul Haq1,2, D Mark Pritchard1,3, Arthur Sun Myint2
1Institute of Systems, Molecular and Integrative Biology, The University of Liverpool, Liverpool, UK.
Cancer Medicine
|April 3, 2025
Summary
Existing models for predicting rectal cancer response to radiotherapy lack broad clinical applicability. Further research is needed to develop a Contact X-ray brachytherapy (CXB)-specific model for better patient outcomes.
Area of Science:
- Oncology
- Radiotherapy
- Medical Informatics
Background:
- No current models predict rectal cancer response to Contact X-ray brachytherapy (CXB).
- Existing models focus on external beam radiotherapy, not CXB.
- This review evaluates models for predicting radiotherapy response to inform CXB model development.
Purpose of the Study:
- Critically evaluate existing predictive models for rectal cancer response to radiotherapy.
- Identify limitations and biases in current models.
- Lay the foundation for a CXB-specific prediction model.
Main Methods:
- Random-effects meta-analysis of published models.
- Assessment of model risk of bias and applicability using PROBAST.
- Documentation of frequently used predictive factors.
Main Results:
- Fifteen models from twelve papers were analyzed.
- Models predicting response by Tumour Regression Grade (TRG) showed pooled AUC of 0.82.
- Models predicting pathologic complete response (pCR) had pooled AUC of 0.76.
- Age, tumour grade, and T stage were common predictors, but models had high bias and limited applicability.
Conclusions:
- Existing models, while statistically robust, lack broad clinical applicability due to insufficient external validation.
- A future CXB-specific model requires dedicated data collection and inclusion of identified predictive factors.
- Improved models are crucial for enhancing clinical utility in rectal cancer treatment.
