Connection

Co-Authors

This is a "connection" page, showing publications co-authored by ADAM S GARDEN and LAURENCE EDWARD COURT.
Connection Strength

1.687
  1. Deep Learning-Based Dose Prediction for Automated, Individualized Quality Assurance of Head and Neck Radiation Therapy Plans. Pract Radiat Oncol. 2023 May-Jun; 13(3):e282-e291.
    View in: PubMed
    Score: 0.218
  2. Knowledge-based planning for the radiation therapy treatment plan quality assurance for patients with head and neck cancer. J Appl Clin Med Phys. 2022 Jun; 23(6):e13614.
    View in: PubMed
    Score: 0.207
  3. Generating High-Quality Lymph Node Clinical Target Volumes for Head and Neck Cancer Radiation Therapy Using a Fully Automated Deep Learning-Based Approach. Int J Radiat Oncol Biol Phys. 2021 03 01; 109(3):801-812.
    View in: PubMed
    Score: 0.186
  4. Automatic detection of contouring errors using convolutional neural networks. Med Phys. 2019 Nov; 46(11):5086-5097.
    View in: PubMed
    Score: 0.173
  5. Auto-delineation of oropharyngeal clinical target volumes using 3D convolutional neural networks. Phys Med Biol. 2018 11 07; 63(21):215026.
    View in: PubMed
    Score: 0.163
  6. Atlas ranking and selection for automatic segmentation of the esophagus from CT scans. Phys Med Biol. 2017 Nov 14; 62(23):9140-9158.
    View in: PubMed
    Score: 0.152
  7. Forecasting longitudinal changes in oropharyngeal tumor morphology throughout the course of head and neck radiation therapy. Med Phys. 2014 Aug; 41(8):081708.
    View in: PubMed
    Score: 0.121
  8. Predicting oropharyngeal tumor volume throughout the course of radiation therapy from pretreatment computed tomography data using general linear models. Med Phys. 2014 May; 41(5):051705.
    View in: PubMed
    Score: 0.119
  9. A Visualization and Radiation Treatment Plan Quality Scoring Method for Triage in a Population-Based Context. Adv Radiat Oncol. 2024 Aug; 9(8):101533.
    View in: PubMed
    Score: 0.059
  10. Clinical acceptability of automatically generated lymph node levels and structures of deglutition and mastication for head and neck radiation therapy. Phys Imaging Radiat Oncol. 2024 Jan; 29:100540.
    View in: PubMed
    Score: 0.058
  11. 18FDG positron emission tomography mining for metabolic imaging biomarkers of radiation-induced xerostomia in patients with oropharyngeal cancer. Clin Transl Radiat Oncol. 2021 Jul; 29:93-101.
    View in: PubMed
    Score: 0.049
  12. Quality assurance assessment of diagnostic and radiation therapy-simulation CT image registration for head and neck radiation therapy: anatomic region of interest-based comparison of rigid and deformable algorithms. Radiology. 2015 Mar; 274(3):752-63.
    View in: PubMed
    Score: 0.031
  13. Estimation of daily interfractional larynx residual setup error after isocentric alignment for head and neck radiotherapy: quality assurance implications for target volume and organs-at-risk margination using daily CT on- rails imaging. J Appl Clin Med Phys. 2014 Jan 08; 16(1):5108.
    View in: PubMed
    Score: 0.029
  14. Anisotropic margin expansions in 6 anatomic directions for oropharyngeal image guided radiation therapy. Int J Radiat Oncol Biol Phys. 2013 Nov 01; 87(3):596-601.
    View in: PubMed
    Score: 0.028
  15. Auto-segmentation of low-risk clinical target volume for head and neck radiation therapy. Pract Radiat Oncol. 2014 Jan-Feb; 4(1):e31-7.
    View in: PubMed
    Score: 0.028
  16. WE-E-213CD-09: Multi-Atlas Fusion Using a Tissue Appearance Model. Med Phys. 2012 Jun; 39(6Part27):3961.
    View in: PubMed
    Score: 0.026
  17. Assessment of shoulder position variation and its impact on IMRT and VMAT doses for head and neck cancer. Radiat Oncol. 2012 Feb 08; 7:19.
    View in: PubMed
    Score: 0.025
  18. Quantification of volumetric and geometric changes occurring during fractionated radiotherapy for head-and-neck cancer using an integrated CT/linear accelerator system. Int J Radiat Oncol Biol Phys. 2004 Jul 15; 59(4):960-70.
    View in: PubMed
    Score: 0.015
Connection Strength

The connection strength for concepts is the sum of the scores for each matching publication.

Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.