Using histological whole slide images (WSIs), how can we stratify patients into meaningful risk groups by leveraging data from both patients with known survival times and event indicators and those without, while requiring no manual post-hoc thresholding of predicted risk scores? Existing survival stratification methods in computational pathology train a supervised model on patients with known survival times and event indicators, then apply it to a test set to generate risk scores. These scores are typically thresholded, often at the median, to assign patients to high- or low-risk groups. Such inductive pipelines overlook the large pool of unlabelled patients even though number of cases with known survival times are typically limited and observed events are even rarer. As a result, existing methods often fail to uncover meaningful risk groups. In this work, we introduce the first Transductive Survival Ranking (TSR) model for WSIs, designed to leverage both labelled and unlabelled data for improved survival prediction. Given a dataset where only a subset of WSIs have associated survival time and event information, our approach (1) ranks patients by predicted survival times, (2) automatically discovers risk groups without requiring manual thresholding, and (3) transduces differential survival patterns from patients with observed events to those without events. We evaluate the proposed approach on multiple pan-cancer datasets, demonstrating that it achieves statistically significant risk subgrouping in cases where classical approaches fail while producing high concordance indices. Unlike traditional survival models that rely solely on labelled data for training and manual cutoffs, this work leverages transductive learning to make data-driven, threshold-free risk stratification possible, offering a more reliable and data-efficient framework for survival analysis in computational pathology.