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Taxonomy: IM- Ultrasound : Radiomics

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-17	Assessing Longitudinal CT Perfusion Changes in Pancreas and Pelvic Node Tumors Treated with SBRT On Prospective Phase I Dose Escalation Trials T Patton1*, A Santoso1, T Reinicke1, Y Vinogradskiy1, Q Diot1, C Fisher1, K Goodman2, B Jones1, (1) University of Colorado Anschutz Medical Campus, Aurora, CO, (2) Mount Sinai, New York, NY
BReP-SNAP-M-18	Assessment of Texture Feature Robustness Using a Novel 3D-Printed Phantom K Spuhler*, J Teruel, P Galavis, NYU Langone Health, New York, NY
BReP-SNAP-M-46	CT-Based Radiomics Analysis: A New Imaging Biomarker in Chronic Obstructive Pulmonary Disease? R Au1*, V Liu1, M Koo1, W Tan2, J Bourbeau3, J Hogg2, H Coxson2, M Kirby1, (1) Ryerson University, Toronto, Ontario, Canada, (2) Centre For Heart Lung Innovation, University Of British Columbia, Vancouver, British Columbia, Canada, (3) McGill University, Montreal, Quebec, Canada
BReP-SNAP-M-113	Predicting Glioblastoma Cell Motility with Radiomics K Mulford*, M McMahon, D Odde, C Wilke, University of Minnesota, Minneapolis, Minnesota
MO-F-TRACK 1-7	Multi Time-Point Radiomics Data for Pathological Complete Response (pCR) Prediction After Neo-Adjuvant Chemoradiation for Locally Advanced Rectal Cancer M diMayorca1*, Y Zhang2, L Shi3, X Sun4, S Jabbour5, Y Zhang6, N Yue7, K Nie8, (1) Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, (2) University of California Irvine, Irvine, CA, (3) Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, CN, (4) Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, CN, (5) Rutgers Cancer Institute Of New Jersey, New Brunswick, NJ, (6) Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, (7) Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, (8) Rutgers Cancer Institute of New Jersey, New Brunswick, NJ
PO-GeP-I-61	Comparison of Radiomic Feature Variability Between Different MR Pulse Sequences in Brain Metastases D Mitchell*, S Buszek, B Tran, H Liu, S Ferguson, C Chung, UT MD Anderson Cancer Center, Houston, TX
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-118	Evaluation of Radiomic Feature Stability for CT Imaging Parameters in Lung Nodules S Koizumi1*, (1) Komazawa University, Setagaya, 13, JP
PO-GeP-I-134	Imaging Biomarker Analysis for Grading Malignant Gliomas Based On a Few Conventional Magnetic Resonance Imaging Sequences T Nakamoto1*, W Takahashi1, A Haga2, S Takahashi1, S Kiryu3, K Nawa1, T Ohta1, S Ozaki1, Y Nozawa1, S Tanaka1, A Mukasa4, K Nakagawa1, (1) The University of Tokyo Hospital, Tokyo, JP, (2) Tokushima University, Tokushima, JP, (3) International University of Health and Welfare Hospital, Nasushiobara, JP, (4) Kumamoto University, Kumamoto, JP
PO-GeP-I-142	Integrating Gross Tumor Volume and Margin Features to Predict Treatment Response for Locally Advanced Rectal Cancer Patients M diMayorca1*, Y Zhang2, L Shi3, X Sun4, S Jabbour5, Y Zhang6, N Yue7, K Nie8, (1) Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, (2) University of California Irvine, Irvine, CA, (3) Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, CN, (4) Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, CN, (5) Rutgers Cancer Institute Of New Jersey, New Brunswick, NJ, (6) Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, (7) Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, (8) Rutgers Cancer Institute of New Jersey, New Brunswick, NJ
PO-GeP-I-147	Machine Learning Based On CT Radiomic Features Can Predict Residual Tumor From Radiation Changes in Head and Neck Cancer Patients Treated with Definitive Chemoradiotherapy E Florez*, T Thomas, C M. Howard, H Khosravi, J Storrs, S Lirette, A Fatemi, University of Mississippi Medical Center, Jackson, MS
PO-GeP-I-173	Prospective Study Using Delta-Radiomics and MRI Biomarkers to Assess Tumor Response of the Dominant Intraprostatic Lesion (DIL) at Multiple Treatment Time Points M Dumas*, E Mohamed, E Carver, A Feldman, M Pantelic, D Hearshen, B Movsas, I Chetty, N Wen, Henry Ford Health System, Detroit, MI
PO-GeP-I-184	Radiomic Analysis Performed Preoperatively of Radical Prostatectomy to Predict Lymph Node Metastases of High-Grade Prostate Cancers D LeBlanc*, F Rasekh, G Couture, P Despres, J Beauregard, F Pouliot, L Archambault, CHUQ Pavillon Hotel-Dieu de Quebec, Quebec, QCCA,
PO-GeP-I-198	Structural MRI-Based Radiomics and Machine Learning for the Classification of Attention-Deficit/Hyperactivity Disorder Subtypes C Lin1, J Qiu2, K Hou3, W Lu3, W Lu3, X Liu3, J Qiu3, L Shi3*, (1) Taian Disabled Soldiers' Hospital Of Shandong Province, Taian, CN, (2) Taian Municipal Center For disease control and prevention, Taian, CN, (3) Shandong First Medical University & Shandong Academy Of Medical Sciences, Taian, CN
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-97	Classification of TI-RADS Class-4 Thyroid Nodules Based On Shape and Texture Features From Ultrasound Images Q Meng, T Liu, W Lu, L Shi, J Qiu, W Lu*, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, Shandong, CN
PO-GeP-M-156	Differentiating the Pathological Subtypes of Primary Lung Cancer for Patients with Brain Metastases Based On Radiomics Features From Brain CT Images X Jin1*, Z Ji2, C Xie3, (1) Wenzhou Medical University First Hospital, Wenzhou, ,CN, (2) ,,,(3) ,Wenzhou, ,CN
PO-GeP-M-180	Effect of Using Harmonized Radiomic Features On Predicting Volume of Radiographic Changes Following Stereotactic Body Radiotherapy in Lung R N Mahon1*, M Ghita1, G D Hugo2, N Kalman3, N Mukhopadhyay1, E Weiss1, (1) Virginia Commonwealth University, Richmond, VA, (2) Washington University School of Medicine, St. Louis, MO, (3) Miami Cancer Institute, Miami, FL.
PO-GeP-M-189	Estimation of Radiation Dose to Korean Population by Cardiovascular SPECT-CT Examinations YJ Yun1, HW Nam2, WJ Kim3, KP Kim4*, (1) Kyunghee University, Yongin-si,Gyeonggi,KR, (2) Kyunghee University,Yongin-si,Gyeonggi,KR, (3) Korea Institute Of Nuclear Safety,Yuseong-gu,Daejeon,KR, (4) Kyung Hee University, Yongin-si,Gyeonggi,KR
PO-GeP-M-204	Evaluation of MRI Radiomics Feature Robustness Using a Virtual Radiomics Phantom C Ma*, X Wang, K Qing, N Yue, K Nie, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ
PO-GeP-M-215	Evaluation of the Stability of Radiomics Features Using 4D-CT and Across Radiomics Platforms for Lung and Liver Tumors X Wang1*, C Ma1, H Wang2, Y Zhang1, N Yue1, K Nie1, (1) Rutgers-Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, (2) Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China
PO-GeP-M-258	Individualized Prediction of Local Recurrence After Radical Surgery for Esophageal Squamous Cell Carcinoma: Development and Validation of Radiomics Nomogram Z Li1, B Li2*, (1) University of Jinan, Jinan, Shandong, CN, (2) Shandong Cancer Hospital and Institute,Jinan, Shandong,CN;Shandong First Medical University and Shandong Academy of Medical Sciences,Jinan, Shandong,CN.
PO-GeP-M-290	Machine Learning of MAA SPECT Lung Perfusion Radiomics to Predict Radiation and Immune-Mediated Pneumonitis in Patients with Locally Advanced Non-Small Cell Lung Cancer H Thomas T1, J Zeng2, P Kinahan2, R Miyaoka2, H Vesselle2, R Rengan2, S Bowen2*, (1) Christian Medical College Vellore, Vellore, TN, IN, (2) University of Washington, School of Medicine, Seattle, WA
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-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-358	Radiomics Feature Robustness Under Different Image Perturbation Combinations and Intensities: A Study On Nasopharyngeal Carcinoma CT Images J Zhang1, X Teng1*, Z Ma1, T Yu1, S Lam1, F Lee2, K Au2, W Yip2, J Cai1, (1) The Hong Kong Polytechnic University, Hung Hom, Kowloon, HKSAR, (2) Queen Elizabeth Hospital, HKSAR
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-415	Using Radiomics to Study Statin Use and Omega-3 Use in Prostate Cancer Patients D Zheng1*, Y Shi2, E Wahle1, L Krajewski1, X Liang3, Q Du2, C Zhang2, S Zhou1, M Baine1, (1) University of Nebraska Medical Center, Omaha, NE, (2)University of Nebraska Lincoln, Lincoln, NE,(3)University of Florida, Jacksonville, FL
PO-GeP-M-416	Using Raman Spectroscopy and Machine Learning to Predict and Monitor Cellular Radiation Responses X Deng*, K Milligan, R Ali-Adeeb, P Shreeves, S Van Nest, J Andrews, A Brolo, J Lum, A Jirasek, University of British Columbia, Kelowna, BC, CA, University of Victoria, Victoria, BC, CA, Deeley Research Centre, BC Cancer, Victoria, BC, CA, Weill Cornell Medicine, New York, NY, USA
PO-GeP-T-800	Towards An Image-Informed Mathematical Model of Response to Fractionated Radiation Therapy D Hormuth,II1,5*, A Jarrett1,5, T Yankeelov1-5, (1) Oden Institute for Computational Engineering and Sciences, Departments of (2) Biomedical Engineering, (3) Diagnostic Medicine, and (4) Oncology, (5) Livestrong Cancer Institutes . The University of Texas at Austin, Austin, TX USA
SU-CD-TRACK 2-7	A Random Forest Machine-Enabled Diagnostic Algorithm Combing Quantitative CT Radiomics and Clinical Factors for the Identification of Patients with Corona Virus Disease-19 (COVID-19): A Discovery and Validation Study X Li1,2*, J Li3, X Zhao4, Z Ding1, B Yang1, Q Deng1, S Ma2, Y Kuang5, (1) Hangzhou Cancer Hospital, Hangzhou First People's Hospital Group, Hangzhou, China, (2) Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China, (3) Zhejiang University of Traditional Chinese Medicine, Hangzhou, China, (4) Hangzhou Municipal Health Commission, Hangzhou, China, (5) University of Nevada, Las Vegas, NV
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-E-TRACK 1-4	Modeling and Recovering Gray-Level Co-Occurrence-Based Radiomics in the Presence of Blur and Noise G Gang1*, J Stayman2, (1) Johns Hopkins University, Baltimore, MD, (2) Johns Hopkins University, Baltimore, MD
TU-CD-TRACK 2-3	Radiomic Prediction of Radiation Pneumonitis On Pretreatment Planning CT Images of Lung Cancer Patients Receiving Stereotactic Body Radiation Therapy T Hirose1*, H Arimura2, K Ninomiya2, T Yoshitake2, J Fukunaga1, Y Shioyama2, (1)Kyushu University Hospital, Fukuoka, FukuokaJP,(2)Kyushu University, Fukuoka, FukuokaJP,
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
TU-CD-TRACK 2-8	Normalizing Delta Radiomics for Early Prediction of Treatment Response During Chemoradiation Therapy of Pancreatic Cancer H Nasief*, W Hall, B Erickson, X Li, Medical College of Wisconsin, Milwaukee, WI
WE-CD-TRACK 2-0	Advances of Radiomics and Genomics in Cancer Management M Giger1*, J Deasy2*, I Tai3*, F Yin4*, (1) University of Chicago, Chicago, IL, (2) Memorial Sloan Kettering Cancer Center, New York, NY, (3) BCCancer Agency At Vancouver, Vancouver, BC, CA, (4) Duke University Medical Center, Chapel Hill, NC
WE-E-TRACK 2-2	From CT Scans to 3D Prints: Feasibility of 3D Printing CT Radiomic Phantoms for Standardization and Validation of Quantitative CT Measurements U Mahmood1*, A Apte1, C Kanan2, D Bates1, G Corrias1, L Mannelli1, J Oh1, Y Erdi1, J Deasy1, A Dave1, (1) Memorial Sloan Kettering Cancer Center, (2) Rochester Institute Of Technology
WE-E-TRACK 2-4	Classification of LGG Tumor IDH1 Gene Mutation Status Using T2/FLAIR MRI Texture Information M Safari1, 2*, L Archambault1, 2, A Ameri3, A Fatemi4, M Beigi5, (1) Department of Physics, Universite Laval, Quebec, QC, CA, (2) CHU de Quebec - Universite Laval, Quebec, QC, CA, (3) Department Of Clinical Oncology, Shahidbeheshti University, (4) University of Mississippi Med. Center, Jackson, MS, (5) Novin Medical Radiation Institute, Haft Tir Martyrs Hospital, Tehran, Iran.