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A venous computed tomography radiomic model based on the PT, LSLN, and IT radiomic features represents a novel noninvasive tool for prediction LN metastasis in ESCC. The nonresponse weighting adjustment of the PHIA surveys employs the weighting class method in combination with a tree analysis to identify predictors significant to response propensity. Variable selection for this type of nonresponse adjustment identifies auxiliary variables correlated with response propensity alone and produces one set of weights applicable for all analys
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