A5084764805- Advanced Radiotherapy Techniques
- Radiomics and Machine Learning in Medical Imaging
- Head and Neck Cancer Studies
- Lung Cancer Diagnosis and Treatment
- Radiation Dose and Imaging
- Advances in Oncology and Radiotherapy
- Radiation Effects in Electronics
- Radiation Therapy and Dosimetry
- Radiation Detection and Scintillator Technologies
Google (United States)
2021-2024
Google (United Kingdom)
2020-2021
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 while also introducing interoperator variability, resulting in downstream radiation dose differences. Although auto-segmentation algorithms offer potentially time-saving solution, the challenges defining, quantifying, achieving expert...
Over half a million individuals are diagnosed with head and neck cancer each year worldwide. Radiotherapy is an important curative treatment for this disease, but it requires manual time consuming delineation of radio-sensitive organs at risk (OARs). This planning process can delay treatment, while also introducing inter-operator variability resulting downstream radiation dose differences. While auto-segmentation algorithms offer potentially time-saving solution, the challenges in defining,...
Organ-at-risk segmentation for head and neck cancer radiation therapy is a complex time-consuming process (requiring up to 42 individual structure, may delay start of treatment or even limit access function-preserving care. Feasibility using deep learning (DL) based autosegmentation model reduce contouring time without compromising contour accuracy assessed through blinded randomized trial oncologists (ROs) retrospective, de-identified patient data.
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, gold-standard accuracy, is resource-intensive and slow dose optimization, while speedier analytical approximation has compromised accuracy. Our objective was prototype deep-learning-based model dose-averaged LET (LET
Abstract This document reports the design of a retrospective study to validate clinical acceptability deep-learning-based model for autosegmentation organs-at-risk (OARs) use in radiotherapy treatment planning head & neck (H&N) cancer patients.
<sec> <title>BACKGROUND</title> 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 while also introducing interoperator variability, resulting in downstream radiation dose differences. Although auto-segmentation algorithms offer potentially time-saving solution, the challenges...