Computational pathology models can fail silently when they encounter out-of-distribution data that differ from the training distribution. StaDis introduces a plug-and-play OOD detection method tailored to this setting by measuring the feature gap between an image and its perturbed counterpart. Without retraining the underlying predictor, it improves deployment safety and helps flag unreliable predictions in real clinical environments.