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Taxonomy: TH- Dataset analysis/biomathematics: Machine learning techniques
| BReP-SNAP-M-4 | A Comprehensive Framework for Radiotherapy Treatment Plan Quality Evaluation in Large Multiple Institution Dataset L Yuan*, R Kapoor, W Sleeman, M Hagan, J Palta, M Rosu-Bubulac, Virginia Commonwealth University, Richmond, VA |
| BReP-SNAP-M-6 | A Deep Learning Model to Predict a Diagnosis of MCI by Using Static and Dynamic Brain Connectomics D Cui1,2, J Jin2, Z Liu2, T Yin2*, (1) Shandong First Medical University, Taian, 37, CN, (2) Institute Of Biomedical Engineering, Chinese Academy Of Medical Sciences, Tianjin, CN |
| BReP-SNAP-M-55 | Detecting Pathological Complete Response in Esophageal Cancer After Neoadjuvant Therapy Based On Survival-Weighted Deep Learning: A Pilot Study S Cheng1*, W Yap2, E Tu3, (1) Taiwan AI Labs, ,,(2) Chang Gung Memorial Hospital, ,,(3) Taiwan Ai Labs, |
| BReP-SNAP-M-120 | Quantum-Inspired Approach to Predicting Geometric Changes in Head and Neck Cancer J Pakela*, R Ten Haken, D McShan, M Matuszak, I El Naqa, University of Michigan, Ann Arbor, MI |
| BReP-SNAP-T-107 | Multivariable Dosimetric Models for Urinary and Rectal Toxicity Prediction Assessed From Patient Reported Outcome After Prostate Stereotactic Body Radiotherapy X Pan1*, J Huang2, R Levin-epstein3, Z Wang4, D Low5, X Qi6, (1) Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing,, Xi'an, ,CN, (2) ,Xi'an, 61, CN, (3) ,,,(4) ,,,(5) UCLA, Los Angeles, CA, (6) UCLA School of Medicine, Los Angeles, CA |
| PO-GeP-M-9 | A Deep Learning Method for Prediction of 3D Dose Distribution in Clinical Case S Ahn1*, E Kim2, K Kim3, C Kim4, S Lee5, Y Lim6, H Kim7, D Shin8, J Jeong9, (1) National Cancer Center, Goyang-si Gyeonggi-do,KR |
| 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-202 | Evaluation of Machine Learning Algorithms for Treatment Planning Parameter Calculation J Chow1*, R Jiang2, F Ng3, (1) Princess Margaret Cancer Centre, Toronto, ON, CA, (2) Grand River Hospital, Kitchener, ON, CA, (3) Ryerson University, Toronto, ON, CA |
| PO-GeP-M-355 | Race-Telltales From Blinded Notes? Clinical Evidence Or Implicit Bias H Zhou1, D Ruan12*, (1) Dept. of Bioengineering, UCLA, Los Angeles, CA, (2) Dept. of Radiation Oncology, UCLA, Los Angeles, CA |
| PO-GeP-M-366 | Reducing IMRT QA Workload by 95% and Keeping the Same Level of Quality Control T Nano1*, M Descovich1, E Hirata1, Y Interian2, G Valdes1, (1) University of California, San Franisco, San Francisco, CA, (2) USF, San Francisco, CA |
| TH-AB-TRACK 4-8 | Dosimetric Validation of An Artificial Intelligence Encapsulated Knowledge Transfer Across Medical Domains Applied to Heart Contouring for Treatment Planning C V Guthier1*, R Zeleznik1, J Weiss1, J Taron2, C Hancox1, D Bitterman1, D Kim1, R Punglia1, B Foldyna2, M Lu2, B Kann1, J Bredfeldt1, U Hoffmann2, H Aerts1, R Mak1, (1) Dana-Farber/Brigham and Women's Cancer Center and Harvard Medical School, Boston, MA, (2) Massachusetts General Hospital and Harvard Medical School, Boston, MA, |
| WE-E-TRACK 2-6 | Deciphering Metabolic Features to Target Neuroblastoma Using Machine Learning R Wang1,2,3*, Y Zhang1,4, P Pachnis4, H Vu4, K Wang1,3, R Deberardinis4, J Wang1,3, (1) Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX. (2) School of Artificial Intelligence, Xidian University, Xi'an, People's Republic of China. (3) Medical Artificial Intelligence and Automation (MAIA) Lab, University of Texas Southwestern Medical Center, Dallas, TX. (4) Children's Research Institute, University of Texas Southwestern Medical Center, Dallas, TX. |