OncoHelper AI: Neural Target Tracking for Cancer in Motion

Problem
Pediatric oncology still relies heavily on static snapshots such as imaging, lab values, and periodic clinic visits. These isolated data points arrive late and do not capture how quickly a child’s physiology and tumor biology can change under treatment. Existing guidelines and many artificial intelligence tools treat patient data as fixed input to be classified, rather than as a time-dependent process unfolding over weeks and months. This mismatch between how we measure and how the disease behaves leads to delayed interventions, avoidable toxicity, and missed opportunities to adjust therapy in time.

Solution
This project turns pediatric cancer care into a tracked and forecasted process. OncoHelper AI ingests multi-modal clinical data from hospitals, builds individualized risk trajectories, and updates them continuously as new information appears. Its Interceptor module focuses on where risk is heading, not only where it was, and surfaces clear, clinician-friendly signals about when to intensify, de-escalate, or change therapy. In practice, this supports earlier warning of relapse or complications, better timing of key decisions, and more days at home rather than in hospital for children and their families.

In pediatric oncology, care teams facefast-changing conditions. Children are growing, tumors evolve under therapy,and toxicity accumulates over time. Yet much of today’s evidence still comesfrom static snapshots such as imaging and lab tests that lag behind biologicalreality.

Most current AI tools classify data as ifit were fixed, while real-world oncology behaves like an unfolding process that must be tracked and forecast, not just labeled. OncoHelper AI addresses this gap by focusing on movement over time, helping clinicians stay ahead of the disease instead of reacting to it late.

Tracking a missile, eliminating a tumor

A tumor behaves like a moving target. It can be intercepted, but only if we detect it, predict its path, and respond in time.

Early detection – the drone layer
Non-invasive screening of saliva, blood, imaging, and genomics to identify hidden risk long before symptoms appear. Interceptor AI acts like a high-altitudedrone, constantly scanning biological signals to spot emerging threats.

Midcourse interception – forecasting trajectories
If the tumor is already active, Interceptor AI switches mode and models its evolution. Using Bayesian methods and neural dynamical models, it tracks how the tumor grows, hides, and adapts, and forecasts where it is likely to be next. This supports more precise and timely interventions.

Persistent defense – adaptive learning
If the first strike does not fully solve the problem, Interceptor AI continues to learn. It updates its internal models after every biopsy, scan, and treatment cycle, adjusting tactics as the patient’s condition changes. Overtime, it builds a personalized defense system that tracks cancer as a moving target instead of a static object.

Interception intelligence

The architecture is designed to think like a drone operator scanning from above, a radar analyst calculating trajectories in motion, and a field commander deciding when and how to act. The difference is that the battlefield is a child’s body, and the enemy is cancer in motion.

This program focuses on adaptive risk monitoring and individualized therapy guidance for pediatric oncology. It brings together advanced AI modeling, multi-modal clinical data, and clinician-first interfaces in a single operational platform.

The scope includes multi-center validation, integration with standard hospital systems, and testing across several pediatric tumor types. The broader aim is to improve survival and quality of life for young patients by giving clinicians a tool that learns and adapts as the disease changes.

Initial pilot budget:350,000 dollars for the first 12 months, with a total planned budget of 5,850,000 dollars over three years.

Earlier detection of relapse or metastasis in pediatric patients.
Reduced treatment delays through better forecasting of risk and disease dynamics.
Improved coordination between participating pediatric centers.
Better toxicity management and more days spent at home rather than in hospital.
Clinical and economic evidence to support compliant pilot deployment and futurescale-up.

Core engine: Interceptor AI based on Bayesian inference, neural ordinary differential equation models, and real-timedata assimilation.

Supporting modules:
– Tactician, an AI module that supports therapy timing and risk-adaptivedecisions.
– Sentry, a continuous monitoring layer that tracks risk over time.
– A meta-intelligence layer that coordinates models and reconciles signals from different data sources.

Security and data handling: a HIPAA-ready architecture with privacy protection and options for federated learning, so that sensitive data remain under institutional control.

Integration: connection to hospital systems using standard interfaces and formats, withmulti-site dashboards designed for oncologists and care teams.

The core architecture of OncoHelper AI, including the Interceptor AI engine and its supporting modules, has been designed and specified. We have defined how the neural network will ingest multi-modal clinical data, update risk trajectories over time, and connect to hospital systems.

The next phase is to implement and train the first working prototypes using retrospective pediatric oncology datasets from partner centers. After that, we will move to formal algorithm validation and co-design of clinical interfaces together with pediatric oncologists and nurses.

We invite hospitals, research teams, and donors who share our vision of more adaptive, intelligent pediatric cancer care.

Partnerships can support data pilots, clinical validation, and deployment in real-world care settings. Every contribution helps build a future in which no child’s tumor can outrun our ability to detect, predict, and respond.