Cargando…

The dosimetric impact of deep learning-based auto-segmentation of organs at risk on nasopharyngeal and rectal cancer

PURPOSE: To investigate the dosimetric impact of deep learning-based auto-segmentation of organs at risk (OARs) on nasopharyngeal and rectal cancer. METHODS AND MATERIALS: Twenty patients, including ten nasopharyngeal carcinoma (NPC) patients and ten rectal cancer patients, who received radiotherapy...

Descripción completa

Detalles Bibliográficos
Autores principales:	Guo, Hongbo, Wang, Jiazhou, Xia, Xiang, Zhong, Yang, Peng, Jiayuan, Zhang, Zhen, Hu, Weigang
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	BioMed Central 2021
Materias:	Research
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8220801/ https://www.ncbi.nlm.nih.gov/pubmed/34162410 http://dx.doi.org/10.1186/s13014-021-01837-y

Ejemplares similares

Geometric and dosimetric evaluation of deep learning based auto‐segmentation for clinical target volume on breast cancer
por: Zhong, Yang, et al.
Publicado: (2023)

Dosimetric comparisons of VMAT, IMRT and 3DCRT for locally advanced rectal cancer with simultaneous integrated boost
por: Zhao, Jun, et al.
Publicado: (2015)

MV CBCT-Based Synthetic CT Generation Using a Deep Learning Method for Rectal Cancer Adaptive Radiotherapy
por: Zhao, Jun, et al.
Publicado: (2021)

A Preliminary Experience of Implementing Deep-Learning Based Auto-Segmentation in Head and Neck Cancer: A Study on Real-World Clinical Cases
por: Zhong, Yang, et al.
Publicado: (2021)

Dosimetric impact of deep learning-based CT auto-segmentation on radiation therapy treatment planning for prostate cancer
por: Kawula, Maria, et al.
Publicado: (2022)

An Artificial Intelligence-Based Full-Process Solution for Radiotherapy: A Proof of Concept Study on Rectal Cancer
por: Xia, Xiang, et al.
Publicado: (2021)

Incorporating breath holding and image guidance in the adjuvant gastric cancer radiotherapy: a dosimetric study
por: Hu, Weigang, et al.
Publicado: (2012)

Dosimetric impact of different bladder and rectum filling during prostate cancer radiotherapy
por: Chen, Zhi, et al.
Publicado: (2016)

Evaluation of a hybrid automatic planning solution for rectal cancer
por: Peng, Jiyou, et al.
Publicado: (2022)

Reproducibility with repeat CT in radiomics study for rectal cancer
por: Hu, Panpan, et al.
Publicado: (2016)

Deep learning auto-segmentation on multi-sequence magnetic resonance images for upper abdominal organs
por: Amjad, Asma, et al.
Publicado: (2023)

Deep learning-based two-step organs at risk auto-segmentation model for brachytherapy planning in parotid gland carcinoma
por: Li, Zhen-Yu, et al.
Publicado: (2022)

Comparative clinical evaluation of atlas and deep-learning-based auto-segmentation of organ structures in liver cancer
por: Ahn, Sang Hee, et al.
Publicado: (2019)

Clinical evaluation of deep learning and atlas‐based auto‐segmentation for critical organs at risk in radiation therapy
por: Gibbons, Eddie, et al.
Publicado: (2022)

Is internal target volume accurate for dose evaluation in lung cancer stereotactic body radiotherapy?
por: Peng, Jiayuan, et al.
Publicado: (2016)

Automatic Contour Refinement for Deep Learning Auto-segmentation of Complex Organs in MRI-guided Adaptive Radiation Therapy
por: Ding, Jie, et al.
Publicado: (2022)

Deep learning-based auto-segmentation of targets and organs-at-risk for magnetic resonance imaging only planning of prostate radiotherapy
por: Elguindi, Sharif, et al.
Publicado: (2019)

Radiomics features on radiotherapy treatment planning CT can predict patient survival in locally advanced rectal cancer patients
por: Wang, Jiazhou, et al.
Publicado: (2019)

Dosimetric benefit to organs at risk following margin reductions in nasopharyngeal carcinoma treated with intensity-modulated radiation therapy
por: Mao, Yan-Ping, et al.
Publicado: (2015)

Deep Learning for Differentiating Benign From Malignant Parotid Lesions on MR Images
por: Xia, Xianwu, et al.
Publicado: (2021)

The Tumor Target Segmentation of Nasopharyngeal Cancer in CT Images Based on Deep Learning Methods
por: Li, Shihao, et al.
Publicado: (2019)

Geometrical and dosimetric evaluation of breast target volume auto-contouring
por: Simões, Rita, et al.
Publicado: (2019)

Deep learning‐based auto‐segmentation of clinical target volumes for radiotherapy treatment of cervical cancer
por: Ma, Chen‐Ying, et al.
Publicado: (2021)

Comprehensive clinical evaluation of deep learning-based auto-segmentation for radiotherapy in patients with cervical cancer
por: Chung, Seung Yeun, et al.
Publicado: (2023)

Evaluation of deep learning‐based auto‐segmentation algorithms for delineating clinical target volume and organs at risk involving data for 125 cervical cancer patients
por: Wang, Zhi, et al.
Publicado: (2020)

CBCT-based volumetric and dosimetric variation evaluation of volumetric modulated arc radiotherapy in the treatment of nasopharyngeal cancer patients
por: Jin, Xiance, et al.
Publicado: (2013)

Deep learning empowered volume delineation of whole-body organs-at-risk for accelerated radiotherapy
por: Shi, Feng, et al.
Publicado: (2022)

Are simple IMRT beams more robust against MLC error? Exploring the impact of MLC errors on planar quality assurance and plan quality for different complexity beams
por: Wang, Jiazhou, et al.
Publicado: (2016)

Investigating rectal toxicity associated dosimetric features with deformable accumulated rectal surface dose maps for cervical cancer radiotherapy
por: Chen, Jiawei, et al.
Publicado: (2018)

Deep learning auto-segmentation of cervical skeletal muscle for sarcopenia analysis in patients with head and neck cancer
por: Naser, Mohamed A., et al.
Publicado: (2022)

Evaluation of auto-segmentation for EBRT planning structures using deep learning-based workflow on cervical cancer
por: Wang, Jiahao, et al.
Publicado: (2022)

Clinical feasibility of deep learning-based auto-segmentation of target volumes and organs-at-risk in breast cancer patients after breast-conserving surgery
por: Chung, Seung Yeun, et al.
Publicado: (2021)

Deep Learning for Fully-Automated Localization and Segmentation of Rectal Cancer on Multiparametric MR
por: Trebeschi, Stefano, et al.
Publicado: (2017)

Multimodal image translation via deep learning inference model trained in video domain
por: Fan, Jiawei, et al.
Publicado: (2022)

Abdominal multi-organ auto-segmentation using 3D-patch-based deep convolutional neural network
por: Kim, Hojin, et al.
Publicado: (2020)

MRI-based radiomics signature is a quantitative prognostic biomarker for nasopharyngeal carcinoma
por: Ming, Xue, et al.
Publicado: (2019)

DCTR U-Net: automatic segmentation algorithm for medical images of nasopharyngeal cancer in the context of deep learning
por: Zeng, Yan, et al.
Publicado: (2023)

Deep learning auto-segmentation and automated treatment planning for trismus risk reduction in head and neck cancer radiotherapy
por: Thor, Maria, et al.
Publicado: (2021)

Clinical Target Volume Auto-Segmentation of Esophageal Cancer for Radiotherapy After Radical Surgery Based on Deep Learning
por: Cao, Ruifen, et al.
Publicado: (2021)

Validation of an established deep learning auto-segmentation tool for cardiac substructures in 4D radiotherapy planning scans
por: Walls, Gerard M., et al.
Publicado: (2022)

Cannot write session to /tmp/vufind_sessions/sess_cp9kgntrqhsb0ng84kldccpvlu