Slide Selection Introduces Sampling-Induced Prediction Uncertainty in Digital Pathology AI: Evidence from Multi-Slide...

Background/Objectives: Digital pathology models increasingly infer molecular phenotypes from hematoxylin and eosin whole-slide images (WSIs), but most assume that one slide adequately represents patient-level tumor biology. We quantified prediction instability caused by slide selection and evaluated multi-WSI aggregation. Methods: Six multiple-instance learning architectures were developed in task-specific subsets of TCGA-BRCA (source cohort: 1006 patients and 1065 WSIs) and independently evaluated in 122 patients (400 WSIs) from CPTAC-BRCA. The clinically realistic comparison was random one-slide-per-patient inference versus patient-level aggregation of all available WSIs. Label-conditioned best- and worst-slide analyses were used only as retrospective oracle bounds. The recurrence-risk endpoint was a research-derived 21-gene recurrence-score surrogate calculated from RNA sequencing rather than a clinically reported Oncotype DX result. Results: Random single-slide selection yielded AUCs of 0.77-0.86 for recurrence-risk prediction and 0.65-0.77 for HER2 status. Patient-level multi-WSI aggregation yielded AUCs of 0.81-0.89 and 0.71-0.80, respectively. The architecture-controlled AUC improvement from random selection to aggregation ranged from 0.026 to 0.041 for recurrence risk and from 0.036 to 0.073 for HER2. Aggregation also reduced Brier scores by 0.010-0.028 and 0.009-0.106, respectively. The label-conditioned oracle analyses demonstrated wider theoretical performance ranges of 0.46-0.97 and 0.40-0.92, respectively. Slide-selection sensitivity persisted after excluding tumor-evidence-negative and low-tumor-content WSIs. Conclusions: Slide selection is a material source of sampling-dependent prediction uncertainty. Patient-level multi-WSI aggregation mitigated selection-dependent degradation across architectures, although validation using routine institutional material and clinically reported molecular assays remains necessary.