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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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SU-E-J-37: Radiation Dose during Chemoembolization: A Predictive Model.

B Black¹, A Jones¹, R Bassett¹

¹UT MD Anderson Cancer Center, Houston, TX.

Medical Physics

|May 19, 2017

Summary

This summary is machine-generated.

This study developed a radiation dose prediction model for hepatic chemoembolization, identifying factors like BMI and lesion number to estimate patient exposure and improve interventional radiology safety.

Keywords:

Biomedical modeling Cancer Computed tomography Cone beam computed tomography Dosimetry Fluoroscopy Linear regression Medical imaging Multivariate analysis Radiation treatment

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Y-90 Radioembolization and PD-1 Inhibitor as Neoadjuvant Treatment in Hepatocellular Carcinoma

Y-90 Radioembolization and PD-1 Inhibitor as Neoadjuvant Treatment in Hepatocellular Carcinoma

Published on: May 24, 2024

Area of Science:

Interventional Radiology
Medical Physics
Oncology

Background:

Hepatic chemoembolization involves significant radiation exposure.
Accurate radiation dose prediction is crucial for patient safety and treatment planning.

Purpose of the Study:

To identify variables correlated with radiation dose during hepatic chemoembolization.
To quantify the impact of these variables on radiation dose.
To develop a predictive model for radiation dose in this procedure.

Main Methods:

Retrospective review of 77 patients undergoing hepatic chemoembolization.
Analysis of radiation dose metrics (DAP, CD, cone beam CT, fluoroscopy time) and clinical parameters (BMI, session number, embolization details, lesion characteristics, regimen, treated lobe).
Univariate and multivariate linear regression models were used to identify significant predictors of cumulative dose (CD).

Main Results:

Higher BMI, initial session, single lesions, primary tumor, and specific chemoembolization regimens were significant predictors of radiation dose.
A predictive model was developed with an adjusted R-squared of 52.9%, indicating it explains over half of the dose variation.
The model showed considerable variability in predicted dose ranges.

Conclusions:

A feasible radiation dose prediction model for image-guided interventions was created.
Further refinements are needed to enhance model accuracy.
Prospective dose modeling is increasingly important for managing radiation exposure in patient care.