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2021 AAPM Virtual 63rd Annual Meeting - Session: Quantitative Imaging


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Predicting PD-L1 Expression Level in Non-Small Cell Lung Cancer On Computed tomography Using Machine Learning
Takehiro Shiinoki, PhD Yamaguchi University
shiinoki@yamaguchi-u.ac.jp


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All videos in this session:
A Quantitative Analysis of Lung Elastography Performance Using Large-Deformation CT Scans Acquired at Residual Volume (RV) and Total Lung Capacity (TLC) - Brad Stiehl University of California, Los Angeles
Localized GBM Recurrence Prediction On Post-Operative Multi-Modal MRIs Through Stem Cell Niches Proximity Estimation Coupled SVM - Yi Lao University of California, Los Angeles
Principal Component Analysis of Quantitative Computed Tomography Features and Visual Emphysema Scores: Association with Lung Function Decline - Meghan Koo Ryerson University
Quantitative Relaxometry for Ultra-Hypofractionated MR-Guided Radiotherapy to the Prostate and DIL: A Feasibility Study - Ergys Subashi, PhD Memorial Sloan Kettering Cancer Center
BEST IN PHYSICS (MULTI-DISCIPLINARY): Treatment Response Prediction Using Extracellular Volume Fraction Derived From Dual-Source DECT for Chemoradiation Therapy of Pancreatic Cancer - George Noid, PhD Medical College of Wisconsin
Use of Short Duration Dynamic [18F]DCFPyL PET and CT Perfusion Imaging to Localize Dominant Intraprostatic Lesions in Prostate Cancer: Validation Against Digital Histopathology - Dae-myoung Yang Fox Chase Cancer Center
Q & A -
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