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
“Currently used clinical methods of identifying prostate cancer patients whose tumor will remain indolent from those who are at a high risk of cancer progression to aggressive metastatic disease in a short time have low accuracy rate. This results in uncertainty in clinical decision making leading to unnecessary intervention for many patients leading to considerable and needless side effects, morbidity, and expense, while missing some high-risk patients, who could have benefitted from early intervention.
I am excited to co-develop with 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.”
“Currently available clinical and pathological tools are limited in their ability to predict disease recurrence after initial therapy for early stage and locally advance prostate cancer. 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”
“At present there is no biomarker that can predict triple negative breast cancer response to standard-of-care
“With the aging of the world population, the prostate cancer incidence is on the rise. About 70% of the men diagnosed with prostate cancer will have indolent tumors, who can be kept under observation without any intervention or be cured after the first line therapy with surgery and/or ionizing radiation. Inaccuracy of the current clinical methods in identifying these men at the time of diagnosis leads to unnecessary intervention or missing some patients, who could have benefitted with early, aggressive therapies. 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.”
“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.”
Peer-Reviewed Publications
Clinical Conferences
