James P. Long

In meta‐analytic modeling, the functional relationship between a primary and surrogate endpoint is estimated using summary data from a set of completed clinical trials. Parameters in the meta‐analytic model are used to assess the quality of the proposed surrogate. Recently, meta‐analytic models have been employed to evaluate whether late‐stage cancer incidence can serve as a surrogate for cancer mortality in cancer screening trials. A major challenge in meta‐analytic models is that uncertainty of trial‐level estimates affects the evaluation of surrogacy, since each trial provides only estimates of the primary and surrogate endpoints rather than their true parameter values. In this work, we show via simulation and theory that trial‐level estimate uncertainty may bias the results of meta‐analytic models toward positive findings of the quality of the surrogate. We focus on cancer screening trials and the late‐stage incidence surrogate. We reassess correlations between primary and surrogate endpoints in Ovarian cancer screening trials. Our findings indicate that completed trials provide limited information regarding quality of the late‐stage incidence surrogate. These results support restricting meta‐analytic regression usage to settings where trial‐level estimate uncertainty is incorporated into the model.

Deep learning models deployed in clinical imaging frequently encounter distribution shifts, yet most out-of-distribution (OOD) detection methods are evaluated only on controlled research datasets. As a result, it is unclear whether existing approaches can reliably identify segmentation failures that arise in real-world clinical practice. We evaluated six OOD detection methods on a deployed liver CT segmentation model (3D nnU-Net) using internal data from 400 patients and external data from 100 patients collected across nearly 70 sites in 7 countries. One method was Pairwise Surface DSC, a surface-based extension of Pairwise DSC, that we introduced. OOD performance was measured using sensitivity, AUROC, and balanced accuracy, with thresholds determined on an independent cohort of 400 patients using the Youden J statistic. Statistical significance was assessed using McNemar tests and stratified bootstraps ( α = 0.05) with Benjamini–Hochberg correction. Pairwise Surface DSC was the top-performing method, with perfect sensitivities (1.00), near-perfect AUROCs (0.97 internal; 1.00 external), and the highest balanced accuracies (0.94 internal; 0.88 external; p < 0.001). These results show that automated failure detection for liver CT segmentation is clinically feasible and that Pairwise Surface DSC is a promising candidate for deployment. Our code is available at https://github.com/mckellwoodland/liver_ct_ood_translation .

Serious adverse events related to Delta-24-RGD in Delta-24-RGD via cannula cohort

Summary of serious adverse events related to Delta-24-RGD across all cohorts

195 VirScan peptides for similarity matrix

Supplementary Figure 3 displays the ROC curves for two models applied to the test set of an imbalanced dataset, where SMAGS was fitted at different specificity thresholds. The top-left panel shows the sensitivity at 98.5% specificity, the top-right panel at 80% specificity, the bottom-left panel at 50% specificity, and the bottom-right panel at 5% specificity.

Summary of serious adverse events across all cohorts

Serious adverse events related to Delta-24-RGD in Delta-24-RGD (standard) + IFN-γ cohort

Dot plot with patients grouped by <= 9 month and > 9 month survival and assessed Anti-Adeno IgG levels (Wilcoxon P-value = 0.33. B. Scatterplot of Survival (continuous) versus Anti-Adeno IgG levels (continuous; Spearman rank correlation between variables is -0.23. P-value = 0.203).