Background
Despite major advancements in quantitative medical imaging, accurately forecasting patient-specific treatment outcomes remains a critical unmet need—especially in oncology, where early insights into therapeutic efficacy can profoundly impact survival. The complexity of cancer progression and variability among individuals challenge current treatment paradigms, which are largely built around generalized clinical trial data. Personalized predictions are essential for optimizing care, minimizing ineffective interventions, and improving long-term outcomes.
Conventional imaging approaches and response models often rely on post-treatment evaluation, delaying the identification of ineffective therapies. These methods lack the spatial and temporal resolution required to capture subtle, patient-specific tumor dynamics. In practice, clinicians may order additional imaging sessions to compensate, increasing healthcare costs and patient burden while still failing to generate accurate forecasts of therapeutic impact.
Technology overview
This technology integrates multiparametric MRI with a U-Net–based deep learning framework to predict individual response to breast cancer treatment prior to therapy initiation. The system ingests patient-specific apparent diffusion coefficient (ADC) maps and dynamic contrast-enhanced (DCE) MRI data, processing them through a convolutional neural network with contracting and expanding paths that extract both local and global tumor features. A specialized branch estimates normalized global parameters, which are then fed into a biological mathematical model simulating tumor growth, migration, and cell death under treatment.
This unique fusion of deep learning and mechanistic modeling allows for accurate pre-treatment predictions without the need for post-treatment imaging or empirical response measurements. By moving beyond population-based assessments, the system offers clinicians a powerful tool to guide custom therapeutic strategies. It also lays the foundation for extending predictive modeling to other solid tumors where similar imaging data is available.
Benefits
- Provides patient-specific treatment response predictions prior to therapy onset
- Reduces reliance on post-treatment imaging and empirical response assessments
- Combines quantitative MRI, deep learning, and biological modeling in a single workflow
- Normalizes outputs to improve model calibration and consistency across patients
- Facilitates early intervention and personalized treatment planning
Applications
- Breast cancer therapy planning
- Solid tumor response prediction
- Quantitative MRI-based diagnostics
- AI-driven medical imaging tools
- Clinical decision support in oncology
Opportunity
- Replaces population-level models with personalized tumor response simulations
- Improves predictive accuracy while reducing imaging frequency and patient burden
- Available for licensing to partners in medical imaging, oncology diagnostics, and AI healthcare platforms
