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Updated: Sep 2, 2025

Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy

Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy

Published on: June 7, 2015

Knowledge-based planning algorithm for lung SBRT with robust Bayesian stochastic frontier analysis and missing data

Angelika Kroshko^1,2,3, Olivier Morin⁴, Louis Archambault^1,2,3

¹Service de physique médicale et de radioprotection, Centre intégré de cancérologie, CHU de Québec - Université Laval, 1401 18e rue, Quebec, QC, G1J1Z4, Canada.

Medical Physics

|August 10, 2022

Summary

This summary is machine-generated.

This study introduces a robust method using Bayesian stochastic frontier analysis and missing data management to predict dose-volume histogram (DVH) metrics for lung stereotactic body radiation therapy (SBRT). The approach accurately estimates parameters, enhancing treatment planning.

Keywords:

SBRT knowledge-based planning lung cancer

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Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

Area of Science:

Medical Physics
Radiation Oncology
Data Science

Background:

Accurate prediction of dose-volume histogram (DVH) metrics is crucial for optimizing lung stereotactic body radiation therapy (SBRT) plans.
Missing data in patient datasets can hinder the development of robust predictive models for treatment planning.

Purpose of the Study:

To develop a knowledge-based planning technique using Bayesian stochastic frontier analysis (BSFA).
To implement a novel missing data management strategy for handling incomplete organ-at-risk data.
To predict DVH metrics for lung SBRT using a complete dataset.

Main Methods:

A retrospective database of 299 lung SBRT patients was utilized.
Geometric metrics were employed to predict 16 DVH metrics for critical structures.
The predictive model was validated on an independent test set of 50 patients.

Main Results:

The developed model demonstrated accurate prediction of DVH metrics.
Mean differences between observed and predicted values were within acceptable ranges (e.g., 1.5 ± 1.9 Gy for spinal cord PRV D0.35cc).

Conclusions:

The integrated missing data management model proved robust in parameter estimation.
BSFA combined with missing data handling is a viable approach for predicting DVH metrics in lung SBRT planning.