Smarter Risk Stratification for Active Surveillance
A lightweight, interpretable AI engine that combines pathology, MRI, PSA, and clinical data to identify which men on active surveillance are at highest risk of adverse reclassification.
Active Surveillance Needs Better Decision Support
Roughly half of men diagnosed with low-risk prostate cancer are managed with active surveillance — watchful waiting rather than immediate treatment. But current tools cannot reliably identify which patients will progress to clinically significant disease, leading to either over-treatment or missed windows for timely intervention.
~50%
of newly diagnosed prostate cancers managed with active surveillance
30–40%
of active surveillance patients reclassify to higher-risk disease within 5 years
Single-modal
current tools rely on pathology or PSA alone — missing the full clinical picture
A Multimodal Risk Engine, Not Just an Image Classifier
We combine expert-defined quantitative pathology features from whole-slide imaging with MRI findings, PSA density, clinical variables, and longitudinal change signals — producing a calibrated risk score that is interpretable by clinicians and actionable at the point of care.
Pathology Intelligence Layer
Expert GU pathologists prospectively define clinically meaningful morphologic variables from whole-slide images — anchoring the model in interpretable biology rather than opaque embeddings.
Multimodal Fusion
Pathology features are combined with MRI findings, PSA/PSA density, clinical factors, and longitudinal change signals to capture the full risk picture.
Lightweight & Interpretable
The model uses structured, clinically meaningful features — not a massive foundation model — making it fast, auditable, and deployable in real clinical workflows.
Expandable Platform
The same infrastructure supports future outputs: adverse pathology at prostatectomy, biochemical recurrence prediction, and treatment-intensification risk scoring.
Expert-Guided Pathology as a Competitive Moat
The defensible differentiation is not institution versus institution. It is the specific asset stack: expert-defined quantitative pathology combined with a multimodal clinical endpoint, developed by a multidisciplinary NYU team with an existing TOV technology disclosure.
NYU-Disclosed Technology
An existing NYU Technology Opportunities & Ventures disclosure provides a meaningful commercialization foundation and IP posture.
Multidisciplinary Development
GU pathology, urology, radiation oncology, medical oncology, AI engineering, and biomedical informatics — all contributing to a single clinical decision.
Versioned, Reproducible Pipeline
Pathology feature extractors are versioned and frozen at stability milestones, enabling rigorous validation and transparent model updates.
Building Toward Clinical Validation
Confirm Clinical Co-Investigator
Expert NYU urologist confirmed as clinical co-investigator; initial population and primary outcome defined.
Pathology Feature Dictionary
First expert-defined quantitative morphology dictionary specified, including conventional pathology variables and optional learned representations.
WSI Feature Stability
Objective technical criterion for whole-slide image feature stability defined at the ~800 WSI stage; v1 feature extractor frozen.
Multimodal Prototype
Baseline clinical-only model built, then pathology + clinical, then MRI + clinical, then full multimodal — quantifying incremental value at each step.
Validation Framework
Patient-level temporal and external validation planned from the start; calibration and provenance tracking built into the pipeline.
Interested in Collaborating or Investing?
We are actively seeking clinical co-investigators, AI engineering collaborators, and early-stage investors aligned with our commercialization pathway.