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An R-Based Landscape Validation of a Competing Risk Model

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Published on: September 16, 2022

A marginal structural model for normal tissue complication probability.

Thai-Son Tang¹, Zhihui Liu^1,2, Ali Hosni^2,3

¹Dalla Lana School of Public Health, University of Toronto, 155 College Street, Toronto, Ontario M5T 3M7, Canada.

Biostatistics (Oxford, England)

|July 9, 2024

Summary

This summary is machine-generated.

This study introduces a causal framework for normal tissue complication probability (NTCP) modeling in radiation therapy. It offers new methods to assess treatment plan safety by considering dose and volume effects on toxicity risk.

Keywords:

dose-volume histograms marginal structural models multiple monotone regression normal tissue complication probability radiotherapy treatment planning stochastic interventions

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Area of Science:

Radiation Oncology
Medical Physics
Biostatistics

Background:

Radiation therapy aims to maximize tumor dose while sparing healthy tissues.
Dose-volume histograms (DVHs) summarize organ dose distributions for treatment plan evaluation.
Current normal tissue complication probability (NTCP) models primarily predict patient risk from DVH features.

Purpose of the Study:

To develop a causal inference framework for evaluating the safety of alternative radiotherapy treatment plans.
To propose novel causal estimands and estimators for NTCP.
To investigate the application of these methods in anal canal cancer radiotherapy.

Main Methods:

Proposed causal estimands for NTCP using deterministic and stochastic interventions.
Developed estimators based on marginal structural models.
Imposed bivariable monotonicity between dose, volume, and toxicity risk.
Conducted simulations to study estimator properties.

Main Results:

The proposed causal framework provides a novel approach to assess treatment plan safety.
Simulations demonstrated the properties of the developed estimators.
The methods were illustrated using patient data from anal canal cancer radiotherapy.

Conclusions:

Causal inference offers a robust method for evaluating radiotherapy treatment plan safety beyond traditional NTCP modeling.
The proposed marginal structural models and estimators can improve the assessment of dose-volume toxicity relationships.
This approach has potential implications for optimizing radiation therapy and reducing normal tissue complications.