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Frontiers in Oncology|March 17, 2023
On the importance of interpretable machine learning predictions to inform clinical decision making in oncologySheng-Chieh Lu, Christine L Swisher, Caroline Chung, et al.
Radiotherapy and Oncology : Journal of the European Society for Therapeutic Radiology and Oncology|November 7, 2016
Assessment of organs-at-risk contouring practices in radiosurgery institutions around the world - The first initiative of the OAR Standardization Working GroupHelena Sandström, Caroline Chung, Hidefumi Jokura, et al.
Medical Physics|January 20, 2020
An artificial neural network to model response of a radiotherapy beam monitoring systemYoung-Bin Cho, Makan Farrokhkish, Bern Norrlinger, et al.
International Journal of Radiation Oncology, Biology, Physics|December 27, 2005
Spinal cord planning risk volumes for intensity-modulated radiation therapy of head-and-neck cancerStephen L Breen, Tim Craig, Andrew Bayley, et al.
Physics in Medicine and Biology|April 8, 2024
Numerical optimization of longitudinal collimator geometry for novel x-ray fieldBenjamin Insley, Dirk Bartkoski, Peter Balter, et al.
International Journal of Radiation Oncology, Biology, Physics|December 3, 2014
Automated voxel-based analysis of volumetric dynamic contrast-enhanced CT data improves measurement of serial changes in tumor vascular biomarkersCatherine Coolens, Brandon Driscoll, Caroline Chung, et al.
Tomography (Ann Arbor, Mich.)|June 18, 2020
4D-CT Attenuation Correction in Respiratory-Gated PET for Hypoxia Imaging: Is It Really Beneficial?Brandon Driscoll, Douglass Vines, Tina Shek, et al.
International Journal of Radiation Oncology, Biology, Physics|September 13, 2006
Assessment of residual error in liver position using kV cone-beam computed tomography for liver cancer high-precision radiation therapyMaria A Hawkins, Kristy K Brock, Cynthia Eccles, et al.
Physica Medica : PM : an International Journal Devoted to the Applications of Physics to Medicine and Biology : Official Journal of the Italian Association of Biomedical Physics (AIFB)|February 24, 2020
Machine learning helps identifying volume-confounding effects in radiomicsAlberto Traverso, Michal Kazmierski, Ivan Zhovannik, et al.
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