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Taxonomy: IM/TH- Formal Quality Management Tools: Machine Learning
| MO-E115-GePD-F5-1 | Convolutional Neural Networks for Identifying Correlation Between Dose Patterns Associated with Poor Survival and Early Local Recurrence After Metastatic Liver SBRT B Ibragimov1*, Y Yuan3, D Toesca2 , D Chang4 , A Koong5 , L Xing6 , (1) ,,,(2) Stanford University, Stanford, California, (3) Stanford University, Stanford, CA, (4) Stanford University, Stanford, CA, (5) The University of Texas MD Anderson Cancer Center, Houston, TX, (6) Stanford Univ, School of Medicine, Stanford, CA |
| MO-J430-CAMPUS-F1-3 | Machine Learning of Tumor Cluster Dosi-Radiomics to Predict Regional Changes On Early-Response FDG PET/CT Imaging of FLARE-RT Protocol Patients C Duan1,2,4 , W Chaovalitwongse2 , K Puk3 , P Thammasorn2 , S Wang3 , D Hippe4 , L Pierce4 , X Liu2 , J You1 , R Miyaoka4 , H Vesselle4 , P Kinahan4 , R Rengan4 , J Zeng4 , S Bowen4*, (1) Tongji University School of Economics & Management, Yangpu District, Shanghai, (2) University of Arkansas, Fayetteville, AR, (3) University of Texas, Austin, TX, (4) University of Washington School of Medicine, Seattle, WA |
| MO-K-DBRA-6 | Spatial, Anatomically-Localized Mapping of Dose-Toxicity Associations Following Prostate Radiotherapy M Marcello1 , A Kennedy2 , J Dowling3 , A Haworth4 , L Holloway5 , S Gulliford6 , D Dearnaley7 , J Denham8 , M Ebert9*, (1) University of Western Australia, Perth, Western Australia, (2) Sir Charles Gairdner Hospital, Perth, 6009, (3) CSIRO, Brisbane, Queensland, (4) University of Sydney, Sydney, New South Wales, (5) Liverpool and Macarthur cancer therapy centres and ingham Institute, Sydney, NSW, (6) Institute of Cancer Research & Royal Marsden, Sutton, ,(7) Institute for Cancer Research, Sutton, Surrey, (8) University of Newcastle, Newcastle, New South Wales, (9) The University of Western Australia, Nedlands, |
| MO-K-KDBRC-1 | Automated Standardization of Organ Labeling in Head and Neck Using Deep Learning T Rozario*, D Nguyen , M Lin , T Long , M Chen , W Lu , S Jiang , UT Southwestern Medical Center, Dallas, TX |
| MO-K-KDBRC-3 | Automated Detection of Vertebral Body Metastases for Fully-Automated Palliative Radiotherapy Using Transfer Learning T Netherton*, C Cardenas , A Klopp , P Balter , C Chung , C Peterson , R Howell , L Court , The University of Texas MD Anderson Cancer Center, Houston, TX |
| MO-K-KDBRC-6 | JACK KROHMER JUNIOR INVESTIGATOR COMPETITION WINNER: Selecting Predictive Genomic Biomarkers for Oropharyngeal Cancer Treatment Prediction by Use of Advanced Machine Learning Method J Wu1*, C Lian2, S Ruan2, S Mutic1, M Anastasio1, H Gay1, W Thorstad1, X Wang1, H Li1, (1) Washington University in St. Louis, Saint Louis, MO, USA (2) University of Rouen, Rouen, France |
| SU-F-207-2 | Deriving Ventilation Imaging From 4DCT by Deep Convolutional Neural Network Y Zhong1*, Y Vinogradskiy2 , L Chen1 , N Myziuk3 , R Castillo4 , E Castillo3 , T Guerrero3 , S Jiang1 , J Wang1 , (1) UT Southwestern Medical Center, Dallas, TX, (2) University of Colorado Denver, Aurora, CO, (3) Beaumont Health System, Royal Oak, MI, (4) Emory Univ, Atlanta, GA |
| SU-F-209-3 | Predicting Motion Compensation Errors for CyberKnife SBRT Liver Patients Using a Machine Learning Algorithm M Liu1*, D Granville2 , J E Cygler1,2, E Vandervoort1,2 (1) Carleton University, Ottawa, ON (2) The Ottawa Hospital Cancer Centre, Ottawa, ON |
| SU-F-KDBRA1-6 | Pancreatic Cancer Risk Prediction Through An Artificial Neural Network W Muhammad*, G Hart , K Johung , Y Liang , B Nartowt , I Ali , J Deng , Yale university School of Medicine, New Haven, CT |
| SU-H300-GePD-F9-6 | Reproducibility of CT Iterative Reconstruction Algorithms From Analytic Reconstitutions with Convolutional Neural Networks for Pediatric Brain Imaging R MacDougall*, Y Zhang , H Yu , UMass Lowell, Lowell, MA |
| SU-H330-GePD-F9-1 | A Population Based Statistical Model for Dose Distribution in Nasopharynx Cancer Gang Liu1,2,3 , Xuanfeng Ding2, Zhiyong Yang1, Zhiwen Liang1, Jing Yang1, Xin Nie1, Jun Han1, Hongyuan Liu1, Mi chen1,Ting Cao1, Xiaohui Zhu1, Hong Quan3, Qin Li1* 1. Cancer Center, Union Hospital, Huazhong University of Science and Technology, Tongji Medical College, Wuhan, 430023, China; 2. Beaumont Health System, Royal Oak, MI, 48304, USA. 3. School of Physics and Technology, Wuhan University, Hubei, Wuhan, 430072, China; |
| SU-H400-GePD-F8-4 | Radiomics-Based Prediction of Malignant Glioma Grades Using T2-Weighted Magnetic Resonance Images T Nakamoto1*, W Takahashi1 , A Haga1, 2 , S Takahashi1 , K Nawa1 , T Ohta1 , S Ozaki1 , S Tanaka1 , A Mukasa3 , K Nakagawa1 , (1) The University of Tokyo Hospital (2) Tokushima University (3) Kumamoto University |
| SU-H430-GePD-F5-6 | Prediction of Tumor Progression in Locally Advanced Non-Small Cell Lung Cancer Using Standardized Uptake Values From Thoracic PET/CT Images J M Luna1*, D Kontos1 , K A Cengel1 , C B Simone II 2 , E S Diffenderfer1 , (1) University of Pennsylvania, Philadelphia, Pennsylvania, (2) University of Maryland School of Medicine, Baltimore, Maryland |
| SU-I-GPD-I-6 | Impact of Image Pre-Processing On Radiomics Feature Prediction Power in Recurrence Glioblastoma Patients G Hajianfar1*, I Shiri2 ,M Oveisi3 , H Maleki4 , A Haghparast1 , (1) Kermanshah University of Medical Sciences,Kermanshah,Iran, (2)(3)(4) Rajaie Cardiovascular Medical and Research Center, Tehran,Iran |
| SU-I-GPD-J-12 | Prediction of 3D Dose Distribution and PTV Contour Via Deep Multi-Task Learning Network F Guo1*, F Kong1 , Y Li2 , L Zhou1 , T Song1 , (1) Southern Medical University, Guangzhou, Guangdong, (2) SUN YAT-SEN UNIVERSITY CANCER CENTER, Guangzhou, Guangdong, |
| SU-I-GPD-J-55 | Comparation of Machine Learning Methods for NSCLC Overall Survival Time Prediction Based On Radiomics Analysis W Sun1*, M Jiang2 , J Dang3 , F Yin4 , (1) School of Information Science and Engineering, Shandong University, Jinan, Shandong, (2) School of Information Science and Engineering, Shandong University, Jinan, Shandong, (3) Chongqing Medical University, Chongqing, Chongqing, (4) Duke University Medical Center, Durham, NC |
| SU-I-GPD-J-61 | Transfer Learning-Based Tongue Gestures Classification Using Ultrasound Images Kele Xu1*, Dawei Feng2 , Shun Zou3 , (1) National University of Defense Technology, Wuhan, Hubei, (2) National University of Defense Technology, Changsha, Hunan, (3) National University of Defense Technology, Wuhan, Hubei |
| SU-I-GPD-J-67 | Densely Connected Semantic Segmentation Network for Liver Tumor Segmentation J Kwon*, E Shim , Y Kim , K Choi , Korea Institute of Science and Technology (KIST), Seoul, Seoul |
| SU-I-GPD-T-84 | Using Machine Learning to Predict GI Toxicity in Unresectable Pancreatic Cancer Patients Treated with Stereotactic Body Radiation Therapy T Wu*, S Liauw , University of Chicago Hospitals, Chicago, IL |
| SU-K-205-1 | Convolutional Neural Network Fiducial Detection for Position Reconstruction Using a High-Efficiency Cadmium Tungstate Portal Imager C Lindsay1*, W Ansbacher1 , I Gagne1 , J Star-Lack2 , M Bazalova-Carter3 , (1) British Columbia Cancer Agency - Vancouver Island Centre, Victoria, BC (2) Varian Medical Systems, Palo Alto, CA, (3) University of Victoria, Victoria, BC |
| SU-K-205-6 | Radiomics Characteristics Correlate with Immune Activation: A TCIA/TCGA Data Analysis in Head and Neck Squamous Cell Carcinoma J Oh1*, E Katsoulakis1 , J Leeman1 , Y Yu2 , J Tsai1 , S McBride1 , N Katabi1 , A Apte1 , N Lee1 , V Hatzoglou1 , N Riaz1 , J Deasy1 , (1) Memorial Sloan Kettering Cancer Center, New York, NY, (2) University of California San Francisco, San Francisco, CA |
| SU-K-DBRA-4 | Evaluating the Linearity of Risk Functions Across Radiotherapy Outcomes Using Deep Learning C Ahern1 , T Pheiffer1*, C Berlind1 , W Lindsay1 , Y Xiao2 , C Simone3 , (1) Oncora Medical, Inc., Philadelphia, PA, (2) University of Pennsylvania, Philadelphia, PA, (3) University of Maryland School of Medicine, Baltimore, Maryland |
| SU-L-205-2 | Impact of Image Preprocessing Methods On the Robustness of MRI-Based Radiomic Classifiers for Glioblastoma H Um*, F Tixier , D Bermudez , A Iyer , A Apte , J Deasy , I Mellinghoff , R Young , H Veeraraghavan , Memorial Sloan-Kettering Cancer Center, New York, NY |
| SU-L-KDBRC-5 | MRI-Based Radiomics Distinguishes Treatment Effect From True Progression After Stereotactic Radiosurgery for Brain Metastases L Peng1 , J Lee1 , K Sheikh1* , P Huang1 , V Parekh1 , B Baker1 , T Kirschbaum1 , F Silvestri1 , J Son1 , A Robinson1 , H Ames1 , J Grimm1 , L Chen1 , C Shen1 , M Soike2 , E McTyre2 , K Redmond1 , M Lim1 , M Jacobs1 , L Kleinberg1 , (1) Johns Hopkins University, Baltimore, MD, (2) Wake Forest School of Medicine, Winston Salem, NC |
| TH-AB-DBRB-4 | A Multi-Objective Based Feature Selection Method for Lung Nodule Malignancy Classification Z Zhou1*, S Li2 , H Hao3 , X Chen4 , M Folkert5 , S Jiang6 , J Wang7 , (1) UT Southwestern Medical Center, Dallas, Texas, (2) Southern Medical University, Guangzhou, Guangdong, (3) Xidian University, Dallas, TX, (4) Xi'an Jiaotong University, Dallas, Texas, (5) UT Southwestern Medical Center, Dallas, Texas, (6) UT Southwestern Medical Center, Dallas, TX, (7) UT Southwestern Medical Center, Dallas, TX |
| TH-AB-DBRB-8 | Radio-Morphology: Parametric Shape-Based Features for Outcome Prediction in Radiotherapy P Lakshminarayanan1*, W Jiang1 , S Robertson2 , Z Cheng1 , P Han1 , M Bowers1 , J Moore1 , J Lee1 , H Quon1 , R Taylor1 , T McNutt1 , (1) Johns Hopkins University, Baltimore, Maryland, (2) WellSpan York Hospital, York, Pennsylvania |
| TH-AB-KDBRB1-4 | Deep Learning Based PET Image Noise Reduction Using Both PET and CT Information X Jin1*, J Fan1 , X Rui2 , (1) GE Healthcare, Waukesha, Wisconsin, (2) GE Global Research Center, Niskayuna, New York |
| TH-AB-KDBRB1-8 | A Stacking Method for Predicting Patient QA Passing Rates Using Machine Learning D Lam*, T Dvergsten , T Zhao , D Yang , S Mutic , B Sun , Washington University in St Louis, St Louis, MO |
| TH-CD-KDBRC-4 | Semi-Supervised GANs for Head and Neck Organ Recognition with Small Labeled Datasets T Rozario*, D Nguyen , M Lin , X Jia , W Lu , S Jiang , UT Southwestern Medical Center, Dallas, TX |
| TH-CD-KDBRC-12 | Segmentation of the Prostate and Organs at Risk in Male Pelvic CT Images Using Deep Learning S Kazemifar*, A Balagopal , D Nguyen , S McGuire , R Hannan , S Jiang , A Owrangi , UT Southwestern Medical Center, Dallas, TX |
| TH-EF-202-4 | CBCT Projection-Domain Scatter Correction with a Residual Convolutional Neural Network Y Nomura1*, Q Xu2 , H Shirato1 , S Shimizu1 , L Xing2 , (1) Hokkaido University, Sapporo, Hokkaido, (2) Stanford University, Palo Alto, CA |
| TH-EF-202-5 | Cone-Beam CT Scatter Artifact Removal with Deep Residual Generative Adversarial Network N Qin1*, Y Gonzalez1 , C Shen1 , C Shieh2 , X Jia1 , (1) University of Texas Southwestern Medical Center, Dallas, TX, (2) The University of Sydney, Sydney, NSW |
| TH-EF-202-9 | Noise Subtraction for CT Images Acquired at Multiple Dose Levels Using a Deep Convolutional Neural Network A Missert*, S Leng , Mayo Clinic, Rochester, MN |
| TU-C1000-GePD-F1-1 | Comparison of Gravitational Search Algorithm and Error Back Propagation Algorithm: Applying to CT Ring Artifact Removal Based On Interpolation Z CHAO*, H Lee , D Kim , H Kim , Yonsei University, Wonju, Gangwon |
| TU-C1030-GePD-F1-3 | Comparison of Noise Analysis and Deep Learning-Based Image Quality Assessment (IQA) Methods for Thoracic Computed Tomography (CT) B Grant1*, J Lee2 , J Chung3 , I Reiser2 , L Lan2 , J Papaioannou3 , M Giger2 , (1) Western Kentucky University, Bowling Green, KY, (2) The University of Chicago, Chicago, IL, (3) University of Chicago Medicine, Chicago, IL, |
| TU-C930-GePD-F5-5 | Automatic Localization and Segmentation of the Pancreas in Motion Artifact-Free CBCT Reconstructions Using Fully Convolutional Networks P Jordan*, A Wang , J Star-Lack , J Van Heteren , Varian Medical Systems, Palo Alto, CA |
| TU-E115-GePD-F3-1 | A Machine Learning Process for Delta Radiomics H Nasief*, X Li , Froedtert Hospital and the Medical College of Wisconsin, Milwaukee, WI |
| TU-I345-GePD-F9-2 | Identifying Predictors of Unplanned Hospitalizations After Radiotherapy Using Regularized Survival Models C Ahern1*, T Pheiffer1 , C Berlind1 , W Lindsay1 , Y Xiao2 , C Simone3 , (1) Oncora Medical, Inc., Philadelphia, PA, (2) University of Pennsylvania, Philadelphia, PA, (3) University of Maryland School of Medicine, Baltimore, Maryland |
| TU-I345-GePD-F9-5 | Risk-Index of Colorectal Cancer to Triage for Screening B. Nartowt*, G. Hart , D. Roffman , I. Ali , W. Muhammad , Y. Liang , J. Deng , Yale university School of Medicine, New Haven, CT |
| TU-I345-GePD-F9-6 | Deep Neural Network-Based Modeling of Radiation Pneumonitis Following Lung Stereotactic Body Radiotherapy S Wang*, A Smith , J Ryckman , M Baine , D Zheng , Y Lei , C Zhang , N Bennion , Q Fan , C Lin , C Enke , S Zhou , University of Nebraska Medical Center, Omaha, NE |
| TU-K-202-4 | Automated Segmentation of Malignant Pleural Mesothelioma Tumor On Computed Tomography Scans Using Deep Convolutional Neural Networks E Gudmundsson1*, C Straus2 , A Nowak3 , H Kindler4 , S Armato5 , (1) ,,,(2) University of Chicago, Chicago, Illinois, (3) University of Western Australia, Crawley, WA, (4) University of Chicago, Chicago, Illinois, (5) The University of Chicago, Chicago, IL |
| TU-K-202-7 | Machine Learning On Quality Control of Chest CT Chest Exams: Scan Length Optimization D Huo*, A Scherzinger , University Colorado Denver, School of Medicine, Aurora, CO |
| TU-K-207-3 | Medical Image Annotation with a New Low-Rank Modeling-Based Multi-Label Active Learning Method J Wu1, S Ruan2 , C Lian2 , S Mutic1 , M Anastasio1 , H Li1* , (1) Washington University in St. Louis, Saint Louis, MO, (2) University of Rouen, Rouen, Normandy |
| TU-K-207-4 | Multi-Scale V-Net: A Deep Learning Framework for Brain Tumor Segmentation in Multiparametric MRI Y Liu*, X Shi , Y Xia , Y Lei , L Tang , T Liu , W Curran , H Mao , X Yang , Emory University, Atlanta, GA |
| WE-AB-KDBRC-6 | BEST IN PHYSICS (JOINT IMAGING-THERAPY): Variogram-Weighted Generalized Least Squares Regression to Predict Spatially Variant Tumor Voxel Response On Longitudinal FDG-PET/CT Imaging of FLARE-RT Protocol Patients D Hippe1 , W Chaovalitwongse2 , C Duan3 , P Thammasorn2 , X Liu2 , R Miyaoka1 , H Vesselle1 , P Kinahan1 , R Rengan1 , J Zeng1 , S Bowen1*, (1) University of Washington School of Medicine, Seattle, WA, (2) University of Arkansas, Fayetteville, AR, (3) Tongji University School of Economics & Management, Yangpu District, Shanghai |
| WE-C1030-GePD-F4-6 | Multi-Classification of Clinical Notes for Natural Language Processing-Based Information Aggregation in Radiotherapy D Ruan*, Y Min , D Low , M Steinberg , UCLA, Los Angeles, CA |
| WE-C1030-GePD-F7-1 | A Machine Learning Framework for Predicting the Tumor Control Probability of Radiation Therapy Plans E Lee1*, Y Cao2 , A Templeton3 , R Yao4 , J Chu5 , (1) Georgia Institute of Technology, Atlanta, GA, (2) Georgia Institute of Technology, Atlanta, GA, (3) Rush University Medical Center, Chicago, IL, (4) Columbus Regional Healthcare, Columbus, GA, (5) Rush University Medical Center, Oak Brook, IL |
| WE-FG-DBRA-3 | Ensemble Learning: A Case Study with Knowledge Based Treatment Planning J Zhang1*, T Xie2 , Y Sheng3 , Q Wu4 , F Yin5 , Y Ge6 , (1) Duke University Medical Center, Durham, NC, (2) Duke University Medical Center, Durham, NC, (3) Duke University Medical Center, Durham, NC, (4) Duke University Medical Center, Durham, NC, (5) Duke University Medical Center, Durham, NC, (6) UNC Charlotte, Charlotte, NC |
| WE-FG-DBRA-7 | Modeling of Multiple Planning Target Volumes (PTVs) in Knowledge-Based Planning (KBP) J Zhang1*, Y Sheng2 , C Wang3 , T Xie4 , F Yin5 , Y Ge6 , Q Wu7 , (1) Duke University Medical Center, Durham, NC, (2) Duke University Medical Center, Durham, NC, (3) Duke University Medical Center, Durham, NC, (4) Duke University Medical Center, Durham, NC, (5) Duke University Medical Center, Durham, NC, (6) UNC Charlotte, Charlotte, NC, (7) Duke University Medical Center, Durham, NC |
| WE-FG-DBRA-9 | Performance Comparison of Knowledge-Based Dose Prediction Techniques A Landers*, R Neph , F Scalzo , D Ruan , K Sheng , UCLA School of Medicine, Los Angeles, CA |
| WE-J-KDBRA1-1 | A Method to Detect Errors in Radiation Therapy Physician Orders Using Association Rules X Chang*, H Li , Y Fu , B Sun , D Yang , Washington University School of Medicine, St Louis, MO |
| WE-J-KDBRA1-6 | Statistical Shape Models for the Automated Detection of OAR Segmentation Abnormalities E Schreibmann*, H Shim , T Liu , N Esiashvili, Emory University, Atlanta, GA |