Radiogenomic models using MRI features from multiple public datasets predicted the M0 macrophage immune signature in IDH-wildtype glioblastoma with mean balanced accuracy 0.67 and precision 0.89 on held-out cohorts.
Assessing Model Selection Uncertainty Using a Bootstrap Approach: An update
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
representative citing papers
Reformulating covariance structure into self- and cross-components with a relative-error constraint enables stable sign and direction recovery in SEM for p > n settings.
A latent class Cox PH model is fitted to CHF survival data with class number selected by BIC, evaluated via AUC on cross-validated and bootstrapped samples, and shown in simulation to outperform the standard single-class Cox model.
citing papers explorer
-
Predictive Radiomics for Evaluation of Cancer Immune SignaturE in Glioblastoma: the PRECISE-GBM study
Radiogenomic models using MRI features from multiple public datasets predicted the M0 macrophage immune signature in IDH-wildtype glioblastoma with mean balanced accuracy 0.67 and precision 0.89 on held-out cohorts.
-
Covariance-Based Structural Equation Modeling in Small-Sample Settings with $p>n$
Reformulating covariance structure into self- and cross-components with a relative-error constraint enables stable sign and direction recovery in SEM for p > n settings.
-
Application of Cox Model to predict the survival of patients with Chronic Heart Failure: A latent class regression approach
A latent class Cox PH model is fitted to CHF survival data with class number selected by BIC, evaluated via AUC on cross-validated and bootstrapped samples, and shown in simulation to outperform the standard single-class Cox model.