Prostate

The PATHOMIQ AI Platform

PATHOMIQ AI In Practice

PATHOMIQ_PRAD

Our product analyzes digitized H+E radical prostectomy whole slide images using our Al platform to produce a report which assesses risk of biochemical relapse and risk of metastasis. Patients determined to be intermediate risk were stratified into PATHOMIQ_PRAD High and PATHOMIQ_PRAD low, identifying high risk patients who may have previously been overlooked. The PATHOMIQ_PRAD score was used to identify patients at highest risk of metastatic progression in the non-metastatic castration resistant prostate cancer setting in the SPARTAN clinical trial. A high score is also indicative of much better response to anti-androgen apalutamide treatment. Not only do we provide a logistical benefit when compared to genomic tests, we also outperform them.

Read more about this study.

PATHOMIQ_AS

PATHOMIQ_AS is an AI-based decision support model to personalize management for patients on active surveillance (AS) for low- and intermediate-risk prostate cancer. The AI-alone model predicts disease progression, while an integrated multi-modal model combining histopathology, PSA, and clinical data predicts progression, grade reclassification, and adverse pathology, providing individualized, time-based risk estimates that enable tailored follow-up schedules and earlier intervention for high-risk patients. Validated across independent cohorts, the model outperformed standard clinical factors and has the potential to reduce unnecessary biopsies and overtreatment while improving precision in AS management.

AUA 2026, abstract in review.

Digitize Slides

Scan your H+E-stained RP slides or send tissue to one of our CLIA-certified lab partners to do it for you.

Upload Images

Upload the images to our AI platform to begin analysis.

Download Report

Once our AI platform is done analyzing the images, you will receive the report.

Testimonials

Hear From the Experts

The AI-powered PATHOMIQ tool holds promise to change that and identify those at highest risk of recurrence by image-based analysis of image-based analysis of initial biopsy or prostatectomy samples. This technology is non-destructive, has fast turn-around time, identifies “hot spots” that will allow deeper proteogenomic analysis to help identify the drivers of progression, and as such is a promising approach to define more personalized outcome predictions and therapies.

Eric A. KleinChair of the Glickman Urological & Kidney Institute, Professor of Surgery, Lerner College of Medicine, Cleveland Clinic

I am excited to see the high degree of accuracy in PATHOMIQ AI-powered tool for identifying these patients from images of their primary biopsies that will immensely help better decision making for each patient. Additionally, PATHOMIQ’s image based, fast turn-over and non-destructive analysis will allow deeper genomic analysis of the cancer driving areas to refine personalized therapies at an earlier stage of the disease leading to better success rate with lesser side effects.

Janak D. Desai, MS, MCh, FRCSUrologist, Samved Urology Hospital

I am excited to co-develop with the PATHOMIQ team, the PATHOMIQ_PRAD AI model for prostate cancer treatment response prediction and identify high-risk prostate cancer patients who experience early metastasis post-surgery. This AI model has outperformed currently used markers in both Caucasian and African American patient cohorts for a much-improved outcome.

Ashutosh Tewari, MD, MBBSDirector of Robotic Prostatectomy, Precision Urology Director, Prostate Cancer Program

A biomarker that can reliably predict response 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, MDChairman, Department of Breast Medical Oncology, MD Anderson Cancer Center

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, MDProfessor, Department of Pathology, MD Anderson Cancer Center

The Minds Behind Our Mission.

Meet the Team

PATHOMIQ AI, headquartered in Silicon Valley, USA, was founded by a multidisciplinary team of outstanding biomarker discovery researchers, data scientists, and clinicians with 100+ man-years of experience in their areas of specialty.