ArticlePhysics and imaging in radiation oncology2021
Deep learning auto-segmentation and automated treatment planning for trismus risk reduction in head and neck cancer radiotherapy.
Article in Physics and imaging in radiation oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- Pooled it
- Evaluation of a quasi-automatic treatment planning workflow for head-and-neck cancer in radiotherapy.Physics and imaging in radiation oncology · 2026Article
- ChatGPT and Microsoft Copilot for Cochlear Implant Side Selection: A Preliminary Study.Audiology research · 2025Article
- Artificial Intelligence in Head and Neck Surgery.Otolaryngologic clinics of North America · 2024Review
- Clinical acceptance and dosimetric impact of automatically delineated elective target and organs at risk for head and neck MR-Linac patients.Frontiers in oncology · 2024Article
- Artificial neural network-assisted prediction of radiobiological indices in head and neck cancer.Frontiers in artificial intelligence · 2024Article
- Deep Learning Techniques and Imaging in Otorhinolaryngology-A State-of-the-Art Review.Journal of clinical medicine · 2023Review
- Clinical implementation of deep learning-based automated left breast simultaneous integrated boost radiotherapy treatment planning.Physics and imaging in radiation oncology · 2023Article
- Validation of clinical acceptability of deep-learning-based automated segmentation of organs-at-risk for head-and-neck radiotherapy treatment planning.Frontiers in oncology · 2023Article
- Stress-testing pelvic autosegmentation algorithms using anatomical edge cases.Physics and imaging in radiation oncology · 2023Article
- Deep Learning-Based Segmentation of Head and Neck Organs-at-Risk with Clinical Partially Labeled Data.Entropy (Basel, Switzerland) · 2022Article
- Towards real-time radiotherapy planning: The role of autonomous treatment strategies.Physics and imaging in radiation oncology · 2022Article
- Nested block self-attention multiple resolution residual network for multiorgan segmentation from CT.Medical physics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors at 1 institution in 1 country.
Funding
Abstract
background and purposeReducing trismus in radiotherapy for head and neck cancer (HNC) is important. Automated deep learning (DL) segmentation and automated planning was used to introduce new and rarely segmented masticatory structures to study if trismus risk could be decreased. MATERIALS AND
methodsAuto-segmentation was based on purpose-built DL, and automated planning used our in-house system, ECHO. Treatment plans for ten HNC patients, treated with 2 Gy × 35 fractions, were optimized (ECHO
resultsSmall systematic differences were observed between the doses to the six auto-segmented OARs and their manual counterparts (median: ECHO
conclusionsAutomated treatment planning can efficiently incorporate new structures from DL auto-segmentation, which results in trismus risk sparing without deteriorating treatment plan quality. Auto-planning and deep learning auto-segmentation together provide a powerful platform to further improve treatment planning.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.