Oncology clinical trials face a high risk of discontinuation, creating financial loss, delaying evidence generation, and slowing patient access to effective therapies. Discontinuation also raises ethical concerns when patients, investigators, and sponsors invest effort in trials that may not reach interpretable endpoints. Existing trial risk models often rely on static regression frameworks and limited feature sets. They are poorly suited to model time-to-discontinuation, dynamic toxicity emergence, or non-linear interactions among target class, safety burden, and protocol design complexity. This article proposes a conceptual deep survival modeling framework for predicting oncology trial discontinuation over time. The model would use drug target class, toxicity signals, and protocol complexity features to generate trial-specific survival curves. The proposed architecture would encode structured trial registry variables, target-class representations, normalized safety signals, and protocol complexity measures as covariates. A deep survival model, such as a DeepSurv-style network or a discrete-time neural survival architecture, would estimate the conditional probability that a trial remains active over time. Conceptually, the model would produce survival functions for individual oncology trials, update risk estimates as new toxicity evidence becomes available, and identify features most associated with elevated discontinuation risk. These outputs would support portfolio triage, operational monitoring, and protocol redesign. A deep survival approach could improve oncology development risk analytics by combining time-to-event modeling with flexible feature learning. Such models could support earlier identification of vulnerable trials and more informed drug development decisions.