Publications by authors named "Alexei Zverovitch"
This study aims to address the limitations of traditional methods for calculating linear energy transfer (LET), a critical component in assessing relative biological effectiveness (RBE). Currently, Monte Carlo (MC) simulation, the gold-standard for accuracy, is resource-intensive and slow for dose optimization, while the speedier analytical approximation has compromised accuracy. Our objective was to prototype a deep-learning-based model for calculating dose-averaged LET (LET) using patient anatomy and dose-to-water (D) data, facilitating real-time biological dose evaluation and LET optimization within proton treatment planning systems.
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- The study examines the use of a deep learning (DL) autosegmentation model to speed up organ-at-risk segmentation in radiation therapy for head and neck cancer, aiming to improve treatment access without sacrificing accuracy.
- Expert radiation oncologists created gold standard (GS) contours on CT images, and a custom 3D U-Net DL model was trained to generate contour predictions, which were then compared to contours made by medical dosimetry assistants (MDAs) in a randomized trial.
- Results showed that using the DL model significantly reduced contouring time by 76% overall and 35% specifically in RO revisions, while the accuracy of DL-generated contours was equal or superior to those revised by MDAs, with 76%
Background: Over half a million individuals are diagnosed with head and neck cancer each year globally. Radiotherapy is an important curative treatment for this disease, but it requires manual time to delineate radiosensitive organs at risk. This planning process can delay treatment while also introducing interoperator variability, resulting in downstream radiation dose differences.
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