AI-Powered Tissue Analysis for Personalized Cancer Diagnostics

This invention is a computational tool that uses AI algorithms to analyze features and cluster them into distinct biological regions from H&E-stained digital pathology images. This technology provides an objective, high-resolution method to reveal subtle, hidden biological patterns in tissue, enabling the development of predictive biomarkers for personalized cancer therapy.

Description

The core of the technology is a computational tool designed for semi-supervised extraction and analysis of features from digital pathology images, specifically those stained with Hematoxylin and Eosin (H&E). It utilizes AI algorithms to identify subtle patterns and features within the images that are not detectable by the human eye. The innovation lies in the subsequent clustering of these features to delineate distinct "neighborhoods" or regions within the tissue, each representing different biological characteristics. This clustering approach provides deeper insights into tissue heterogeneity and cellular architecture than traditional methods. Once these neighborhoods are identified, the tool can classify tissue regions into different malignancy subtypes, from premalignant to malignant states. The system facilitates the early detection of cancer markers and can be leveraged to develop predictive biomarkers for therapy response by linking the image-based features and neighborhood clusters with clinical data. This capability is critical for personalizing treatment strategies.

Applications

- Clinical Diagnostics & Pathology Labs: Implementing the tool for objective, high-resolution analysis of H&E slides to improve diagnostic accuracy and reduce inter-pathologist variability.
- Personalized Oncology/Therapeutics: Using the predictive biomarkers developed by the tool to tailor treatment strategies for cancer patients based on underlying tissue characteristics.
- Pharmaceutical and Biotech Research: Applying the tool in clinical trials and research studies to develop predictive markers for therapy outcomes across different cancer types.
- Screening Programs: Utilizing the ability to identify early markers of premalignancy to enable earlier intervention.
- Digital Pathology Platforms: Integration into existing digital pathology systems as a value-added module for advanced feature extraction and tissue heterogeneity mapping.

Advantages

- Objective and High-Resolution Analysis: Automates feature extraction and clustering, offering an objective method to uncover tissue patterns invisible to the human eye, reducing variability between pathologists.
- Early Detection of Premalignancy: Provides a more sensitive and accurate diagnosis by identifying subtle, early markers of premalignancy in tissue architecture.
- Development of Predictive Biomarkers: Links image-based neighborhood classifications with patient clinical data to develop markers that predict cancer therapy outcomes.
- Enhanced Diagnostic Accuracy: Improves diagnostic certainty, particularly in distinguishing between malignant and non-malignant regions and detecting subtle features.
- Highly Scalable and Versatile: The automated nature makes it scalable for large datasets and clinical trials, and it's versatile for use across various cancer types.

Invention Readiness

The technology is beyond the theoretical phase, as the concept has been defined, and a prototype exists. The foundational libraries used in this technology were sourced from open-source repositories. Further studies would involve validation in larger and more diverse clinical datasets and potentially incorporating the technology into a clinical workflow for real-time diagnostic support.

IP Status

Research Tool

Related Publication(s)

Johnson DB, Bao R, Ancell KK, et al. Response to Anti-PD-1 in Uveal Melanoma Without High-Volume Liver Metastasis. J Natl Compr Canc Netw. 2019;17(2):114-117. doi:10.6004/jnccn.2018.7070

Shah, Shalin et al. “Clinical Response of a Patient to Anti-PD-1 Immunotherapy and the Immune Landscape of Testicular Germ Cell Tumors.” Cancer immunology research vol. 4,11 (2016): 903-909. doi:10.1158/2326-6066.CIR-16-0087