PATHOMIQ TRANSFORMING DRUG DEVELOPMENT EMPOWERING PRECISION MEDICINE

Mission

Harness the power of AI to revolutionize personalized medicine and drug development, improving clinical outcomes

Approach

We have developed a one-of-a-kind phenotype atlas which has learned high level information from across all landscapes of cancer pathology, allowing us to quickly and efficiently deploy to new use cases in order to predict outcome, treatment response, and even genotype mutations.

PATHOMIQ AI PLATFORM

Patient Outcome

Identifies the best cancer treatments for improved clinical outcomes.

Clinical Trials

Accelerates clinical trials and increases success rates with guided patient selection.

Biomarker Discovery

Provides a unique approach for quick identification of new biomarkers for precision medicine development.

Phenotype Atlas

One of a kind deep tissue phenotype library developed from billion-scale tissue samples

“far reaching clinical implications”

“At present there is no biomarker that can predict triple negative breast cancer (TNBC) response to standard-of-care neoadjuvant chemotherapy. A biomarker that can reliably predict response (or resistance) to chemotherapies at the time of the initial diagnosis would have far reaching clinical implications such as the personalization of cytotoxic and immunotherapy regimens that would optimize both safety and efficacy. We are very excited about the performance of PATHOMIQ’s AI-enabled morphometric biomarker from H&E slides that could be translated into the clinic for personalized treatment selection for each patient in the near future.” – Debasish Tripathy, MD, Chairman, Department of Breast Medical Oncology, MD Anderson Cancer Center

“high accuracy”

“This is the first demonstration of using an AI tool on digitized H&E tissue images to predict response to NAC in patients with TNBC with high accuracy.” – Savitri Krishnamurthy, MD, Professor, Department of Pathology, MD Anderson Cancer Center

Peer-Reviewed Publications

In Review - An AI powered tool to accurately identify prostate cancer patients at high-risk of progression among clinically intermediate risk groupIn Review

2023 - Predicting Response of Triple-Negative Breast Cancer to Neoadjuvant Chemotherapy Using a Deep Convolutional Neural Network–Based Artificial Intelligence ToolJCO Clinical Cancer Informatics–Read more.

2022 - A Novel Artificial Intelligence–Powered Method for Prediction of Early Recurrence of Prostate Cancer After Prostatectomy and Cancer DriversJCO Clinical Cancer Informatics–Read more

2021 - Development and Validation of an Artificial Intelligence–Powered Platform for Prostate Cancer Grading and QuantificationJAMA Network Open–Read more

Clinical Conferences

2023 - AI-enabled analysis of H&E-stained prostate cancer tissue images: Assessing risk for metastasis prior to apalutamide (APA) treatment of patients with non-metastatic castration-resistant prostate cancer (nmCRPC).ASCO–Download

2023 - A novel, AI-generated morphologic biomarker accurately predicts prostate cancer recurrence in patients with intermediate-risk of progression.ASCO–Read more

2023 - AI powered-platform to predict gene modifications from prostate and breast cancer whole slide imagesAACR–Download

2023 - Predicting response of triple negative breast cancer to neoadjuvant chemotherapy using a deep convolutional neural network-based artificial intelligence toolSan Antonio Breast Cancer Symposium–Download

2020 - Using Artificial Intelligence to Reduce Inter-Observer Variability in Gleason Scoring/Grading of Prostate CancerUSCAP–Download

2020 - AI Powered Platform to Identify Primary Prostate Cancer Patients with High Risk of RecurrenceAACR–Download

2019 - Prostate Cancer Diagnosis and quantification using AI-enabled Software (SW)USCAP–Download