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Quantifying Cognitive Decrements Caused by Cranial Radiotherapy

Quantifying Cognitive Decrements Caused by Cranial Radiotherapy

Published on: October 18, 2011

Artificial Intelligence Uncertainty Quantification in Radiotherapy Applications - A Scoping Review.

Kareem A Wahid^1,2, Zaphanlene Y Kaffey², David P Farris³

¹Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

Medrxiv : the Preprint Server for Health Sciences

|May 27, 2024

Summary

Clinician trust in artificial intelligence (AI) for radiotherapy (RT) requires better uncertainty quantification (UQ). This review identified limited UQ applications beyond auto-contouring and calls for new methods and reporting guidelines.

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Area of Science:

Radiotherapy
Artificial Intelligence
Medical Imaging

Background:

Clinician trust in AI for radiotherapy (RT) is limited, necessitating robust uncertainty quantification (UQ) methods.
This study scopes the literature on UQ in RT to identify research gaps and future directions.

Approach:

A systematic scoping review following PRISMA-ScR guidelines was conducted.
Searches across seven databases up to January 2024 identified 8980 articles, with 56 included after screening.
Data extraction focused on study, RT, AI, and UQ characteristics.

Key Points:

Most UQ research in RT focuses on auto-contouring (50%) and failure detection (60%).
Monte Carlo dropout (32%) and ensembling (16%) are common UQ methods; 55% of studies did not share code/data.
Head and neck cancers were the most studied disease site (32%).

Conclusions:

A lack of diversity exists in UQ applications for RT beyond auto-contouring.
Further research into UQ methods like conformal prediction is needed.
This review may encourage guidelines for UQ reporting and implementation in RT.