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| BReP-SNAP-I-5 | An Empirical Comparison of Weka Classifiers for Outcome Prediction Using An Imaging Habitats Definition and Feature Extraction Method On MRI Q Han1*, R Palm2, K Latifi2, E Moros2, A Naghavi2, G Zhang2, (1) University of South Florida, Tampa, FL, (2) H. Lee Moffitt Cancer Center, Tampa, FL |
| BReP-SNAP-I-20 | Evaluation of CT-Based Radiomics Features for Predicting Parameters Measured Using a Pulmonary Function Test Y Ieko1,2*, N Kadoya1, K Abe1,3, S Tanaka1, H Takagi4, T Kanai5, K Ichiji6, T Yamamoto1, H Ariga2, K Jingu1, (1) Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan, (2) Department of Radiation Oncology, Iwate Medical University School of Medicine, Iwate, Japan, (3) Department of Radiology, Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital, Tokyo, Japan, (4) Department of Radiological Technology, School of Health Sciences, Faculty of Medicine, Tohoku University, Sendai, Japan, (5) Department of Radiation Oncology, Yamagata University Faculty of Medicine, Yamagata, Japan, (6) Department of Radiological Imaging and Informatics, Tohoku University Graduate School of Medicine, Sendai, Japan. |
| BReP-SNAP-I-41 | Preoperative Non-Invasive Grading of Parotid Gland Cancer Malignancy Using Radiomic MR Features H Kamezawa1*, H Arimura2, R Yasumatsu2, K Ninomiya2, (1) Teikyo University, Omuta, JP, (2) Kyushu University, Fukuoka, JP |
| BReP-SNAP-M-22 | Automatic Detection of Dental Artifact in a Fully-Automated Treatment Planning Workflow S Hernandez*, C Sjogreen, S Gay, T Netherton, A Olanrewaju, C Nguyen, D Rhee, J Mendez, L Court, C Cardenas, MD Anderson Cancer Center, Houston, TX |
| BReP-SNAP-M-36 | Classification and Segmentation of Tumor Cells and Nuclei On Biopsy Slides Using Deep Learning for Microdosimetry Applications L Weishaupt*, J Torres, S Camilleri-Broët, S Côté Maldonado, S Abbasinejad Enger, McGill University, Montreal, QC, CA |
| BReP-SNAP-M-39 | Combining Radiomics and Convolutional Neural Network to Predict Tumor Growth of Vestibular Schwannoma K Wang*, L Chen, N George-jones, J Hunter, J Wang, UT Southwestern Medical Center, Dallas, TX |
| BReP-SNAP-M-52 | Deep Neural Network for Survival Analysis Using Intratumoral Radiomics and Dosiomics in the RTOG 0617 Clinical Trial S Lee*, H Geng, H Zhong, Y Fan, M Rosen, Y Xiao, University of Pennsylvania, Philadelphia, PA |
| BReP-SNAP-M-85 | Image Quality of Tomographic Thermal Imaging Reconstruction J McCorkindale1*, Y Liao1, K Jones1, J Sun2, A Templeton1, J Chu1, J Turian1, (1) Rush University, Chicago, IL, (2) Argonne National Lab, Lemont, IL |
| BReP-SNAP-M-104 | Multicenter Characterization of MRIs Used for Radiation Therapy: The Collaborative Quality Assurance Program L Conroy1*, C Foottit2, B Zhang1,3, A Elzibak4, R Hunter5, P Rapley6, J Kraus Himmelman7, E Gutierrez7, T Tadic1, A McNiven1, D Letourneau1, (1) The Princess Margaret Cancer Centre, University Health Network, Toronto, ON, CA, (2) The Ottawa Hospital, Ottawa, ON, CA, (3) Stronach Regional Cancer Centre, Newmarket, ON, CA (4) Sunnybrook Health Sciences Centre, Odette Cancer Centre, Toronto, ON, CA, (5) Juravinski Cancer Centre, Hamilton, ON, CA, (6) Thunder Bay Regional Health Sciences Center, Thunder Bay, ON, CA, (7) Ontario Health (Cancer Care Ontario), Toronto, ON, CA |
| BReP-SNAP-M-145 | Two-Step Subspace Mapping Based Diaphragm Displacement Prediction by Markerless Abdominal Surface Measurement H Yu*1,2, E Zhang2, S Yu2, Z Yang2, L Ma2, M Chen2, X Gu2, W Lu2, (1) Xidian University, Shaanxi, China, (2) UT Southwestern Medical Center, Dallas, TX |
| MO-CD-TRACK 1-2 | First-Time Imaging of Light Generation in the Eye During Radiotherapy I Tendler1*, A Hartford2,3, M Jermyn1,4, E Larochelle1, X Cao1, V Borza1, D Alexander1, P Bruza1, J Hoopes3,5, K Moodie3, B Marr6, B Williams2,3, B Pogue1,4,5, D Gladstone1,2,3, L Jarvis2,3, (1) Thayer School of Engineering, Dartmouth College, Hanover, NH (2) Department of Medicine, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire (3) Norris Cotton Cancer Center, Dartmouth-Hitchcock Medical Center, Lebanon, NH (4) DoseOptics LLC, Lebanon, NH (5) Department of Surgery, Geisel School of Medicine, Dartmouth College, Hanover, NH (6) Department of Ophthalmic Oncology, Columbia University Medical Center, New York, NY |
| MO-F-TRACK 1-6 | Automated Quantification of Spatially Abnormal 129-Xe MRI Ventilation and Perfusion: Implications for Lung Cancer, Asthma, and COPD Interventions M Mcintosh*, R Eddy, J Macneil, A Matheson, G Parraga, Robarts Research Institute, Western University, London, ON, CA |
| MO-F-TRACK 2-3 | Intensity-Based Thresholding of Probability Maps in Deep-Learning-Based Segmentation N Bice*, N Kirby, R Li, T Bahr, J Rembish, M Agarwal, S Stathakis, M Fakhreddine, UT Health San Antonio, San Antonio, TX |
| PO-GeP-I-11 | A Multifunction Software Tool for ACR MRI QC Evaluation and Accreditation Program J-X Wang*, W T Sobol, University of Alabama at Birmingham, Birmingham, AL |
| PO-GeP-I-13 | A Novel Linear Model for Quantitative Magnetic Resonance Imaging Based On Spin Echo Sequence: A Feasibility Study S Li*, Y Zhang, H Wu#, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Beijing, BeijingCN, #corresponding author: Hao Wu; email: hao.wu@bjcancer.org |
| PO-GeP-I-15 | A Phantom-Based Assessment of Low-Contrast Performance Comparing Iterative Reconstruction Algorithms in CT S Leon*, C Schaeffer, E Olguin, M Arreola, University of Florida, Gainesville, FL |
| PO-GeP-I-29 | Altered Volume of Hippocampus Subfield in Pediatric Bipolar Disorder Patients Revealed by Structural MRI L Kuang1, Y Guo2, D Cui3, Q Jiao4*, W Cao5, W Gao6, L Su7, G Lu8, (1) ,,,(2) Shandong First Medical University, ,,(3) Shandong First Medical University, Taian, 37, CN, (4) Shandong First Medical University, Taian, 37, CN, (5) Shandong First Medical University &Shandong Academy of Medical Sciences, Tai'an, 37, CN, (6) Zhejiang University School of Medicine, ,,CN, (7) Key Laboratory of Psychiatry and Mental Health of Central South University, ,,CN, (8) Clinical School of Medical College, Nanjing University, ,,CN |
| PO-GeP-I-72 | Deep Learning-Based Model Observer for Image Quality Evaluation of Digital Breast Tomosynthesis S Choi1*, S Choi2, H Kim3, (1) Yonsei University, Wonju, Gangwon, KR, (2) Emory University School of Medicine, Atlanta, GA, (3) Yonsei University, Wonju, Gangwon, KR |
| PO-GeP-I-87 | Digital Whole Slides-Based Deep Learning for the Prediction of Treatment Outcome in Head and Neck Squamous Cell Carcinoma H Yu1, D Jing2, W Lu1, W Lu1, J Qiu1, L Shi1*, (1) Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, CN, (2) Xiangya Hospital, Central South University, Changsha, CN |
| PO-GeP-I-97 | Dynamic Oxygen-Enhanced MRI (dOE-MRI): A Method to Detect Tumour Oxygenation Changes After VEGF-Ablation Therapy F Moosvi1*, JHE Baker2, AI Minchinton2, SA Reinsberg1, (1) The University of British Columbia, Vancouver, BC, CA, (2) BC Cancer Research Centre, Vancouver, BC, CA |
| PO-GeP-I-102 | Enhanced 4DCT Combined with Image Histology Screening and Quantitative Analysis of Left Ventricular Myocardial Function Changes and Stability Characteristics M Su*, G Gong, Y Yin, Shandong Cancer Hospital Affiliated to Shandong University, Jinan, CN |
| PO-GeP-I-118 | Evaluation of Radiomic Feature Stability for CT Imaging Parameters in Lung Nodules S Koizumi1*, (1) Komazawa University, Setagaya, 13, JP |
| PO-GeP-I-133 | Image Quality in the Slice-Plane of Half-Reconstructed Computed Tomography in Filtered Back Projection and Iterative Reconstruction Methods N Tsuda1*, K Mitsui2, S Oda3, N Tanaka4, H Aibe5, (1) Division of Radiology, Saga-ken Medical Centre Koseikan, Saga-shi, ,JP, (2) Division of Radiology, Saga-ken Medical Centre Koseikan, Saga-shi, ,JP, (3) Division of Radiology, Saga-ken Medical Centre Koseikan, Saga-shi, ,JP, (4) Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, Fukuoka-shi, ,JP, (5) Department of Radiology, Saga-ken Medical Centre Koseikan, Saga-shi, ,JP |
| PO-GeP-I-137 | Impact of Quantization Parameters On Radiomics Feature Variation in Low Field Strength Magnetic Resonance Images G Simpson1*, J Ford2, F Yang2, N Dogan2, (1) University of Miami, Coral Gables, FL,(2) University of Miami Miller School of Medicine, Miami, FL |
| PO-GeP-I-157 | NRsim: Normal Resolution Simulations Using High Resolution Acquisitions On a Commercial CT Scanner A Hernandez1*, D Shin2, C Abbey3, J Seibert1, N Akino4, T Goto4, J Vaishnav2, K Boedeker4, J Boone1, (1) University of California Davis, Sacramento, CA, (2) Canon Medical System USA, Tustin, CA, (3) University of California Santa Barbara, Santa Barbara, CA, (4) Canon Medical Systems Corporation, Otawara, Japan |
| PO-GeP-I-176 | Quantification of the HU Variation On KV CBCT for Direct Dose Calculation in Adaptive Radiotherapy N Givehchi*, A Strzelecki, M Lehmann, M Plamondon, S Scheib |
| PO-GeP-I-178 | Quantitative Evaluation of Image Quality of Deep-Learning-Based CT Reconstruction Using Structural SIMilarity (SSIM) K Yang*, A Parakh, R Gupta, A Kambadakone, X Li, B Liu, Massachusetts General Hospital, Harvard Medical School, Boston, MA |
| PO-GeP-I-182 | Quantitative MR Imaging Features Associated with Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Patients J Jimenez*, A Abeer, N Elshafeey, J Yung, J Hazle, G Rauch, UT MD Anderson Cancer Center, Houston, TX |
| PO-GeP-I-190 | Shape Analysis in PET Images Using Convolutional Neural Nets: Limitations of Standard Architectures I Klyuzhin1,2*, A Rahmim1,2, (1) BC Cancer Research Centre, Vancouver, BC, CA, (2) University of British Columbia, Vancouver, BC, CA |
| PO-GeP-I-205 | Temperature Dependence of T1, T2 and T1rho in Agarose Phantoms P Hardy1*, X Li2, (1) University of Kentucky, Lexington, KY, (2) Cleveland Clinic Foundation, |
| PO-GeP-I-212 | The Relationship of T1rho and T2 to Muscle Health and Injury P Hardy*, A Andersen, C Fry, B Noehren, University of Kentucky, Lexington, KY |
| PO-GeP-I-223 | Upright Dedicated Cone-Beam Breast CT: Short-Scan, Non-Uniform, Sparse-View Angular Sampling for Radiation Dose Reduction H Tseng, S Vedantham*, A Karellas, University of Arizona, Tucson, AZ |
| PO-GeP-M-13 | A Deep Transfer Learning-Based Radiomics Model for Prediction of Local Recurrence in Laryngeal Cancer Y Jia12*, X Qi2, J Du2, R Chin2, E McKenzie2, K Sheng2, (1) Shaanxi Key Laboratory of Network Data Intelligent Processing; UCLA School of Medicine, Xi'an, Shaanxi, CN, (2) UCLA School of Medicine, Los Angeles, CA |
| PO-GeP-M-30 | A Novel Semi-Supervised Learning Method Using Soft-Label for Lung Segmentation On CT J Zhou1*, Z Yan2, Y Zhang1, N Yue1, (1) Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, (2) SenseBrain Technology Limited LLC, Princeton, NJ |
| PO-GeP-M-63 | Application of Kilovoltage Cone-Beam CT Images in Extracting Radiomics Signature for Predicting Radiation-Induced Pneumonitis Y Huang*, H Liu, R Yu, Y Pu, Y Zhang#, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Beijing, BeijingCN, #corresponding author: Yibao Zhang; email: zhangyibao@pku.edu.cn |
| PO-GeP-M-68 | Assessing the Dosimetric Links Between Organ-At-Risk Delineation Variability and Treatment Planning Variability W Choi1,2, V Leandro Alves2*, E Aliotta2, H Nourzadeh2, J Siebers2, (1) Virginia State University, Petersburg, VA, (2) University of Virginia, Charlottesville, VA |
| PO-GeP-M-93 | Characterization of CT Hounsfield Units Uniformity of 3D-Printed Materials for Proton Therapy E Orton1*, C Engelberts1, R Orbovic1, M Crocker1,2, B Basaric1, D Sobczak3, L Zhao3, (1) Adaptiiv Medical Technologies, Halifax, NS, CA, (2) Dalhousie University, Halifax, NS, CA, (3) St. Jude Children's Research Hospital, Memphis, TN |
| PO-GeP-M-120 | Cone-Beam CT Radiomics for Patients with Liver Tumors Treated by Stereotactic Body Radiation Therapy: A Pilot Study P Yang1*, J Shan2, Q Zhou2, L Xu1, Z Cao1, T Niu3, M Huang4, X Sun2, (1) Zhejiang University, Hangzhou, ,CN, (2) Department of Radiation Oncology, Sir Run Run Shaw Hospital, Zhejiang Univ., Hangzhou, ,CN,(3) Georgia Institute Of Technology,Woodruff School of Mechanical Engineering,Atlanta,GA(4) Duke University, Department of Radiation Oncology, Durham, NC, |
| PO-GeP-M-125 | Creation of An Automated Hand-Crafted Radiomics Methodology and Assessment of Its Potential to Contribute to a Prospective Trial E Carver*, J Snyder, D Bergman, M Shah, S Siddiqui, N Wen, Henry Ford Health System, Detroit, MI |
| PO-GeP-M-165 | Dose Prediction and Customized Optimization Settings by Learning From Previous Cases: Application to SBRT Treatment Planning E Schreibmann*, Department of Radiation Oncology and Winship Cancer Institute of Emory University |
| PO-GeP-M-214 | Evaluation of the Relationship Between Positioning Accuracy and Scanning Volume Using Optical Surface Scanning System and Surface Image Features H Kojima1,4,*, A Takemura2, S Ueda1, K Noto1, H Yokoyama1,4, H Adachi1, S Takamatsu3, (1) Department of Radiology, Kanazawa University Hospital, Kanazawa, JP, (2) Faculty of Health Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, Kanazawa, JP, (3) Department of Radiation Therapy, Kanazawa University Hospital, Kanazawa, JP, (4) Division of Health Sciences, Graduate School of Medical Sciences, Kanazawa University, Kanazawa, JP |
| PO-GeP-M-235 | Head Neck Cancer Locoregional Recurrence Prediction Using Delta-Radiomics Feature K Wang1*, Z Zhou2, L Chen3, R Wang4, D Sher5, J Wang6, (1) UT Southwestern Medical Center, Dallas, TX, (2) University Of Central Missouri, Warrensburg, MO, (3) UT Southwestern Medical Center, Dallas, TX, (4) UT Southwestern Medical Center, Dallas, TX, (5) UT Southwestern Medical Center, Dallas, TX, (6) UT Southwestern Medical Center, Dallas, TX |
| PO-GeP-M-259 | Individuals in the Prediabetes Stage Exhibit Reduced Hippocampus and Amygdala Subregion Volumes D CUI, W Cao, Q Jiao, J Qiu, Y Guo*, Shandong First Medical University, Taian, 37CN, |
| PO-GeP-M-261 | Initial Experience in MRI-Based Brain Metastases Detection Using Deep Learning J Teruel1*, K Bernstein1, P Galavis1, K Spuhler1, J Silverman1, D Kondziolka1,2, K Osterman1, (1) Department of Radiation Oncology, NYU Langone Health, New York, NY, (2) Center for Advanced Radiosurgery, NYU Langone Health, New York, NY |
| PO-GeP-M-270 | Investigating Stability and Reproducibility of Deep Inspiration Breath Hold for Liver Stereotactic Body Radiotherapy D Parsons*, Z Iqbal, N N Sanford, R Reynolds, S Stojadinovic, T A Aguilera, T Chiu, W Lu, M R Folkert, X Gu, UT Southwestern Medical Center, Dallas, TX |
| PO-GeP-M-280 | Learned Delineation of Gross Tumor Volume Incorporating Intra-Observer Variability T Marin*, C Ma, R Lahoud, F Xing, P Wohlfahrt, M Moteabbed, J Woo, X Ma, K Grogg, Y Chen, G El Fakhri, Massachusetts General Hospital, Boston, MA |
| PO-GeP-M-282 | Lesion Insertion Tool to Assess PET-MR Attenuation Correction Methods: Matched Contralateral Uptake Lesion Insertions in Pelvis PET-MR Data Y Natsuaki1*, A Leynes1, K Wangerin2, M Hamdi3, A Rajagopal1, R Laforest3, P E Z Larson1, T A Hope1, S St. James1, (1) University of California at San Francisco, San Francisco, CA, (2) GE Healthcare, Waukesha, WI (3) Washington University School of Medicine, Saint Louis, MO |
| PO-GeP-M-301 | MRI Radiomics for Predicting a Poor Prognosis in Patients with GBM P Borges1, J Lizar1, G Viani2, J Pavoni1*, (1) Department of Physics, Faculty of Philosophy, Sciences and Letters at Ribeirao Preto - University of Sao Paulo,BR, (2) Radiotherapy Department, Ribeirao Preto Medical School Hospital and Clinics, University of Sao Paulo, BR |
| PO-GeP-M-304 | Multi-Modality Brain Tumor Segmentation Using a Modified Cascaded 3D U-Net for Imbalanced Classes W Kong1*, D Li1, YM Yang2, (1) Shandong Normal University, Jinan, 37, CN, (2) UCLA, Los Angeles, CA |
| PO-GeP-M-309 | Multi-Section CT Radiomics Can Accurately Predict Postoperative Recurrence in Esophageal Squamous Cell Cancer Patients Achieving PCR After Neoadjuvant Chemoradiotherapy Followed by Surgery Q Qiu1*, J Duan1, H Deng2, Z Han3, J Gu1, Y Yin1, (1) Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, CN, (2) Second Affiliated Hospital, Nanjing Medical University, Nanjing, Jiangsu, CN (3) Yantai Yuhuangding Hospital, Yantai, Shandong, CN |
| PO-GeP-M-327 | Phantom with Randomly Distributed Anechoic Spheres for Assessing Lesion Detectability and Scan Setup of Ultrasound Transducers Z Li1*, C Baiu2, J Chen3, J Zagzebski4, (1) Northwestern Memorial Hospital, Chicago, IL, (2) Sun Nuclear Corp, Middleton, WI, (3) Northwestern Univ, Chicago, IL, (4) University of Wisconsin, Madison, WI |
| PO-GeP-M-331 | Predicting Treatment Outcome After Immunotherapy Based On Delta-Radiomic Model in Metastatic Melanoma X Chen1*, M Zhou1, K Wang2, Z Wang4, Z Zhou4, (1) Xi'an Jiaotong University, Xi'an, Shaanxi, CN, (2) UT Southwestern Medical Center, Dallas, TX, (3) Peking University Cancer Hospital, Beijing, CN, (4) University Of Central Missouri, Warrensburg, Missouri |
| PO-GeP-M-332 | Predicting Tumor Control Using Geometric Features of Hypoxia Measured with EPRI H Smith1*, I Gertsenshteyn2,3, B Epel2,3, E Barth2,3, M Maggio3, S Sundramoorthy2,3, H Halpern2,3, (1) Department of Radiology, University of Chicago (2) Department of Radiation and Cellular Oncology, University of Chicago (3) National Institutes of Health Center for EPR Imaging In Vivo Physiology, University of Chicago, Chicago,IL |
| PO-GeP-M-334 | Prediction of Uterus Volume Shrinkage for Cervical Cancer Patients During Radiotherapy Using Machine-Learning Approach with Treatment Planning-CT Radiomic Features M Nakano1*, T Nakamoto2, Y Kumai1, Y Koizumi1, M Sumi1, K Nawa2, T Imae2, Y Yoshioka1, M Oguchi1, (1) Cancer Institute Hospital of JFCR, Koto-ku, Tokyo, JP, (2) The University of Tokyo Hospital, Bunkyo-ku, Tokyo, JP |
| PO-GeP-M-374 | Segmentation Accuracy and Radiomics Feature Stability of Multiple U-Net Based Automatic Segmentations On Ultrasound Images for Patients with Ovarian Cancer X Jin1*, J Jin2, C Xie3, (1) Wenzhou Medical University First Hospital, Wenzhou, ,CN, (2) ,,,(3) ,Wenzhou, ,CN |
| PO-GeP-M-392 | Target Tracking in Kilovoltage Images Using Templates of Fiducial Constellations G Angelis1*, B Zwan1,2, A Briggs1, D Nguyen3, P Keall3, J Booth1,4, (1) Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, Australia, (2) School of Mathematical and Physical Sciences, University of Newcastle, Newcastle, Australia, (3) ACRF Image X Institute, The University of Sydney, Sydney, NSW, Australia, (4) School of Physics, University of Sydney, Sydney, Australia |
| PO-GeP-M-398 | The Feasibility of Using Radiomics to Detect T-Spine Lytic Bone Metastases in Simulation-CT Images H Naseri*, J Kildea, McGill University, Montreal, QC, CA, |
| PO-GeP-M-402 | The Use of Image Reject Analysis to Improve Imaging Within a Radiation Therapy Department L Buckley*, The Ottawa Hospital, Ottawa, ON, CA |
| PO-GeP-M-436 | Why MAE Alone Is Not Enough for SCT Model Comparisons P Klages*, N Tyagi, H Veeraraghavan, Memorial Sloan-Kettering Cancer Center, New York, NY |
| PO-GeP-P-11 | Luminance and Non-Uniformity Changes in Primary Monitors Over the 4-Year Period of QA Measurements A Ruuge*, Y Erdi, Memorial Sloan Kettering Cancer Center, New York, NY |
| PO-GeP-P-26 | A Simple Test Tool to Optimize Protocol in Real Time Fluoroscopic Imaging R Livingstone*, B Pearline, A Singh, N Ahirwar, S Devasahayam, Christian Medical College, Vellore, TN IN, |
| PO-GeP-T-566 | Liver Contouring Technique Comparison for Y90 Microspheres Liver Radioembolization for Patients with Non-HCC Liver Disease D Alvarez1*, R Gandhi2, M Chuong3, A Gutierrez4, (1) Miami Cancer Institute, Miami, FL, (2) Miami Cardiac & Vascular Institute, Miami, FL,(3) Miami Cancer Institute, Miami, FL, (4) Miami Cancer Institute, Miami, FL |
| SU-B-TRACK 2-5 | A Quantitative Analysis of Lung Elastography Using Free Breathing Fast Helical CT Scans B Stiehl*, M Lauria, I Barjaktarevic, D Low, A Santhanam, UCLA, Los Angeles, CA |
| SU-CD-TRACK 2-8 | Multi-Block Discriminant Analysis of Integrative 18F-FDG-PET/CT Radiomics for Predicting Circulating Tumor Cells in Early Stage Non-Small Cell Lung Cancer Treated with Stereotactic Body Radiation Therapy S Lee*, G Kao, S Feigenberg, J Dorsey, M Frick, S Jean-baptiste, C Uche, Y Fan, Y Xiao, University of Pennsylvania, Philadelphia, PA |
| SU-D-TRACK 1-3 | Image Quality Assessment of a MR-Compatible E4D Ultrasound Probe for Image Guided Radiation Therapy S Jupitz1*, J Zagzebski1, J Holmes1, D Mills2, W Lee2, H Chan2, A Patel2, L Smith2, B Bednarz1, (1) University of Wisconsin, Madison, WI, (2) GE Global Research |
| SU-F-TRACK 1-7 | The Application of Partial Domain Adaptation Transfer Learning in the Classification of Retinopathy Using OCT Images From Different Datasets J Wu1*, D Li1, YM Yang2, (1) Shandong Normal University, Jinan, Shandong, CN, (2) UCLA, Los Angeles, CA |
| TH-AB-TRACK 4-13 | Attention Guided Lymph Node Malignancy Prediction in Head and Neck Cancer L Chen1*, M Dohopolski1, Z Zhou2, K Wang1, R Wang1, D Sher1, J Wang1, (1) UT Southwestern Medical Center, Dallas, TX, (2) University Of Central Missouri, Warrensburg, MO |
| TU-A-TRACK 1-0 | The Robots Are Coming! AI for Breast Imaging Interpretation L Hadjiiski1*, I Sechopoulos2*, (1) University of Michigan, Ann Arbor, MI, (2) Radboud University Medical Centre, Nijmegen, NL |
| TU-A-TRACK 1-1 | Breast cancer detection and characterization in the era of AI L Hadjiiski1*, I Sechopoulos2*, (1) University of Michigan, Ann Arbor, MI, (2) Radboud University Medical Centre, Nijmegen, NL |
| TU-A-TRACK 1-2 | Evaluation and Optimization of AI for breast imaging: What can it do, and how good is it? L Hadjiiski1*, I Sechopoulos2*, (1) University of Michigan, Ann Arbor, MI, (2) Radboud University Medical Centre, Nijmegen, NL |
| TU-CD-TRACK 2-7 | Subregion-Based Radiomic Analysis of Preoperative Multi-Modal MR Images for Improving Glioblastoma Survival Outcome Prediction J Fu1*, K Singhrao1, D Ruan1, X Qi1, J Lewis2, (1) Department of Radiation Oncology, UCLA, Los Angeles, CA, (2) Cedars-Sinai Medical Center, Beverly Hills, CA |
| WE-D-TRACK 1-1 | Predicting Net 90Y Administered Activity in 90Y-Radioembolization From Post-Therapy 90Y-SPECT/CT Images M. Allan Thomas1*, Benjamin P. Lopez1, Adam Neff3, Armeen Mahvash2, S. Cheenu Kappadath1, (1) Department of Imaging Physics, UT MD Anderson Cancer Center, Houston, TX, (2) Department of Interventional Radiology, UT MD Anderson Cancer Center, Houston, TX, (3) MIM Software, Inc., Cleveland, OH |
| WE-D-TRACK 1-6 | Comparison of Automated Methods for Detection and Prognostication in Metastatic Prostate Cancer Using 18F-NaF PET/CT Images B Schott*1, A J Weisman1, T G Perk1, A R Roth1, S Yip2, G Liu1, R Jeraj13, (1) University of Wisconsin, Madison, WI, (2) AIQ Solutions, Madison, WI, (3) University of Ljubljana, Slovenia |
| WE-E-TRACK 2-5 | Correlation Between Hematocrit and Blood CT Number Changes During Radiation Therapy X Chen*, H Saeed, X Li, Medical College of Wisconsin, Milwaukee, WI |
| WE-F-TRACK 1-2 | Deep Learning Based Image Reconstruction Applied to An Accelerated Brain Protocol K Hwang1*, X Wang2, M Lebel2, E Bayram2, S Banerjee2, JM Johnson1, (1) UT MD Anderson Cancer Center, Houston, TX, (2) GE Healthcare, Waukesha, WI |
| WE-F-TRACK 1-4 | Deep-Learning for Differentiation of Benign From Malignant Parotid Lesions On MR Image B Feng1,2*, X Xia3, L Xu4, C Hu1,2, J Wang1,2, Z Zhang1,2, W Hu1,2, (1) Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, CN (2) Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, CN (3) Department of Radiology, Municipal Hospital Affiliated to Medical School of Taizhou University, Taizhou, CN (4) Department of Radiology, the Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, CN |