Breast – PATHOMIQ

Breast

The PATHOMIQ AI Platform

PATHOMIQ AI In Practice

PATHOMIQ_BRCA

PATHOMIQ_BRCA uses cross-modality AI to translate genomics biomarkers into H&E-based predictors of distant metastasis, providing outcome stratification beyond Oncotype DX in early-stage ER+ breast cancer. It further identifies high-risk patients within both endocrine therapy (HT) and combination therapy (HT+CT) groups who may develop metastasis despite respective standard treatments. The PATHOMIQ_BRCA AI score is independently prognostic, offering insights beyond ODX and conventional clinicopathologic variables.

Read more about this study.

PATHOMIQ_TNBC

PATHOMIQ_TNBC is a deep learning AI model that predicts the likelihood of a triple-negative breast cancer (TNBC) patient responding to neoadjuvant chemotherapy (NAC) directly from routine H&E-stained biopsy images. The AI score identifies patients likely to achieve a pathological complete response, supporting more precise, individualized treatment decisions. This is one of the first tools to use histopathology images to guide TNBC therapy , with strong performance (AUC 0.75; PPV 73.7%; NPV 76.2%).

Read more about this study.

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.