Deep Learning-Based Dose Prediction for Automated, Individualized Quality Assurance of Head and Neck Radiation Therapy Plans.

[1]  A. Garden,et al.  Knowledge‐based planning for the radiation therapy treatment plan quality assurance for patients with head and neck cancer , 2022, Journal of applied clinical medical physics.

[2]  Yaoqin Xie,et al.  Dose prediction via distance-guided deep learning: initial development for nasopharyngeal carcinoma radiotherapy. , 2022, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[3]  Victor G. L. Alves,et al.  OpenKBP-Opt: an international and reproducible evaluation of 76 knowledge-based planning pipelines , 2022, Physics in medicine and biology.

[4]  A. Jemal,et al.  Cancer statistics, 2022 , 2022, CA: a cancer journal for clinicians.

[5]  Skylar S. Gay,et al.  Technical Note: Dose prediction for head and neck radiotherapy using a three-dimensional dense dilated U-net architecture. , 2021, Medical physics.

[6]  D. Georg,et al.  Technical Note: Dose prediction for radiation therapy using feature‐based losses and One Cycle Learning , 2021, Medical physics.

[7]  Jianfei Liu,et al.  Technical Note: A Cascade 3D U-Net for Dose Prediction in Radiotherapy. , 2021, Medical physics.

[8]  A. Alfouzan Radiation therapy in head and neck cancer , 2021, Saudi medical journal.

[9]  L. Court,et al.  Clinical Acceptability of Automated Radiation Treatment Planning for Head and Neck Cancer Using the Radiation Planning Assistant. , 2021, Practical radiation oncology.

[10]  T. Purdie,et al.  OpenKBP: The open-access knowledge-based planning grand challenge , 2020, Medical physics.

[11]  C. Cardenas,et al.  Dose-volume correlates of the prevalence of patient-reported trismus in long-term survivorship after oropharyngeal IMRT: a cross-sectional dosimetric analysis. , 2020, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[12]  H. Rocha,et al.  Clinical validation of a graphical method for radiation therapy plan quality assessment , 2020, Radiation oncology.

[13]  Jinzhong Yang,et al.  Automatic detection of contouring errors using convolutional neural networks , 2019, Medical physics.

[14]  Jiawei Fan,et al.  Automatic treatment planning based on three‐dimensional dose distribution predicted from deep learning technique , 2018, Medical physics.

[15]  Kuo Men,et al.  A feasibility study on an automated method to generate patient‐specific dose distributions for radiotherapy using deep learning , 2018, Medical physics.

[16]  G. E. Marai,et al.  Chronic radiation-associated dysphagia in oropharyngeal cancer survivors: Towards age-adjusted dose constraints for deglutitive muscles , 2018, Clinical and translational radiation oncology.

[17]  Steve B. Jiang,et al.  3D radiotherapy dose prediction on head and neck cancer patients with a hierarchically densely connected U-net deep learning architecture , 2018, Physics in medicine and biology.

[18]  Mona Kamal,et al.  Dose-volume correlates of mandibular osteoradionecrosis in Oropharynx cancer patients receiving intensity-modulated radiotherapy: Results from a case-matched comparison. , 2017, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[19]  David A. Jaffray,et al.  Fully automated treatment planning for head and neck radiotherapy using a voxel-based dose prediction and dose mimicking method , 2016, Physics in medicine and biology.

[20]  Jayashree Kalpathy-Cramer,et al.  Beyond mean pharyngeal constrictor dose for beam path toxicity in non-target swallowing muscles: Dose-volume correlates of chronic radiation-associated dysphagia (RAD) after oropharyngeal intensity modulated radiotherapy. , 2016, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[21]  Quynh-Thu Le,et al.  Importance of Radiation Oncologist Experience Among Patients With Head-and-Neck Cancer Treated With Intensity-Modulated Radiation Therapy. , 2016, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[22]  B. Slotman,et al.  Can knowledge-based DVH predictions be used for automated, individualized quality assurance of radiotherapy treatment plans? , 2015, Radiation oncology.

[23]  Quynh-Thu Le,et al.  Institutional clinical trial accrual volume and survival of patients with head and neck cancer. , 2015, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[24]  C. Fuller,et al.  Late radiation-associated dysphagia (late-RAD) with lower cranial neuropathy after oropharyngeal radiotherapy: a preliminary dosimetric comparison. , 2014, Oral oncology.

[25]  C. Chien,et al.  High case volume of radiation oncologists is associated with better survival of nasopharyngeal carcinoma patients treated with radiotherapy: a multifactorial cohort analysis , 2011, Clinical otolaryngology : official journal of ENT-UK ; official journal of Netherlands Society for Oto-Rhino-Laryngology & Cervico-Facial Surgery.

[26]  Joseph O Deasy,et al.  Quantitative Analyses of Normal Tissue Effects in the Clinic (QUANTEC): an introduction to the scientific issues. , 2010, International journal of radiation oncology, biology, physics.

[27]  E. Yorke,et al.  Use of normal tissue complication probability models in the clinic. , 2010, International journal of radiation oncology, biology, physics.