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DISTINGUISHING HYPERPROGRESSION FROM OTHER RESPONSE PATTERNS TO PD1/PD-L1 INHIBITORS IN NON-SMALL CELL LUNG CANCER WITH PRE-THERAPY RADIOMIC FEATURES
DISTINGUISHING HYPERPROGRESSION FROM OTHER RESPONSE PATTERNS TO PD1/PD-L1 INHIBITORS IN NON-SMALL CELL LUNG CANCER WITH PRE-THERAPY RADIOMIC FEATURES
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机译:具有治疗前放射学特征的非小细胞肺癌中区分PD1 / PD-L1抑制剂与其他反应方式的过度进展
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摘要
Embodiments access a pre-immunotherapy image of tissue demonstrating NSCLC including a tumor and a peritumoral region; extract a first set of radiomic features from the image; provide the first set of radiomic features to a first machine learning classifier; receive a first probability from the first classifier that the tissue is hyperprogressor (HP) or non-responder (R); if the first probability that the tissue is within a threshold: generate a first classification of the ROT as HP or non-R based on the first probability; if the first probability is not within the threshold: extract a second set of radiomic features from the peritumoral region and provide the second set to a second machine learning classifier; receive a second probability from the second classifier that the tissue is HP or R; generate a second classification of the tissue as HP or R based on the second probability; and display the classification.
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