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Image-Based Deep Neural Network for Individualizing Radiotherapy Dose Is Transportable Across Health Systems.

James Randall¹, P Troy Teo¹, Bin Lou¹

¹Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ.

JCO Clinical Cancer Informatics

|January 18, 2023

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This summary is machine-generated.

A deep learning model predicts lung cancer treatment failure using computed tomography scans. This model, when applied across different health systems, demonstrated its ability to guide personalized radiotherapy doses and improve patient outcomes.

Area of Science:

Radiation Oncology
Medical Imaging
Artificial Intelligence

Background:

Lung cancer treatment relies on accurate radiotherapy dosing.
Predicting treatment failure is crucial for individualizing patient care.
Current methods for predicting treatment response can be limited.

Purpose of the Study:

To develop and validate a deep neural network (DL) model for predicting radiation sensitivity in lung cancer patients.
To assess the transportability of this DL model across different healthcare systems.
To guide the individualization of radiotherapy dose to minimize treatment failures.

Main Methods:

A multitask deep neural network was trained on lung computed tomography (CT) images from 849 patients to generate a DL score predicting local failure.

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The DL score was used to derive an individualized radiation dose estimate (iGray) aiming for <5% failure probability at 24 months.

The model's performance and transportability were validated in an external cohort of 271 patients.

Main Results:

The DL model effectively predicted treatment failures in an external cohort, with a concordance index of 0.68.
Patients with high DL scores in the external cohort had a significantly higher rate of local failure (28.5% vs 10.2%).
The iGray dose estimation correlated with local failure rates, supporting its clinical utility.

Conclusions:

The developed DL model shows promise for predicting treatment failures in lung cancer patients treated with stereotactic body radiotherapy.
The model demonstrates transportability across different health systems, supporting its potential for widespread implementation.
DL-guided treatment planning tools can enhance personalized radiotherapy in the field of radiation oncology.