Peer-Reviewed Research

See the Evidence

Published papers, journal articles, and conference abstracts from the Valar Labs research team and collaborators.

Journal Articles

Development and validation of computational histology artificial intelligence (CHAI)-powered prognostic and predictive biomarkers in metastatic hormone-sensitive prostate cancer (mHSPC) using ENZAMET and CHAARTED prospective randomized phase 3 trials

Development and Validation of a Computational Histology Artificial Intelligence–Powered Predictive Biomarker for Selection of Chemotherapy in Advanced Pancreatic Cancer

Artificial Intelligence Biomarkers Predict Poor Efficacy of Bacillus Calmette-Guérin Rechallenge in Previously Bacillus Calmette-Guérin–Treated Nonmuscle-Invasive Bladder Cancer

Computational Histology Artificial Intelligence Enhances Risk Stratification of High-grade Ta Non–muscle-invasive Bladder Cancer in a Multicenter Cohort: Comparison to Current EAU and AUA Stratification Schemes

Presence of an Artificial Intelligence–powered Predictive Biomarker Is Associated with a Poor Response to Intravesical Bacillus Calmette-Guerin but Not to Intravesical Sequential Gemcitabine/Docetaxel in High-grade Non–muscle-invasive Bladder Cancer

Predicting Response to Intravesical BCG in High-Risk NMIBC Using an Artificial Intelligence–Powered Pathology Assay: Development and Validation in an International 12-Center Cohort

Valar Labs debuts AI-powered cancer care prediction tool and secures $22M

Precision Medicine Online

Valar Labs Using $4M in Seed Financing to Advance AI Tool for Personalizing Cancer Treatment

Papers & Conference Abstracts

Computational histology artificial intelligence (CHAI) predicts benefit of first-line chemotherapy regimen in advanced pancreatic ductal adenocarcinoma (PDAC)

Development and Validation of Generalizable Interpretable AI Biomarker to Predict Clinical Outcomes in BCG-Treated Patients with Non-Muscle Invasive Bladder Cancer

Predicting Response to Intravesical BCG in High Risk Non-Muscle Invasive Bladder Cancer Using an Artificial Intelligence-Powered Pathology Assay: Development and Validation in an International 12 Center Cohort

Society of Urologic Oncology

Prediction of recurrence and response to BCG with an artificial intelligence pathology platform in high risk non-muscle invasive bladder cancer

Society of Urologic Oncology

Complementary Pilot Analysis of Tumor Microenvironment and Nuclei in Recurrent vs. Non-recurrent Low-Grade Noninvasive Bladder Cancer Using Pathologic Review and Artificial Intelligence

Development of an artificial intelligence-derived histologic signature associated with adjuvant gemcitabine treatment outcomes in pancreatic cancer

Development of artificial intelligence–derived histological biomarkers for first-line treatment selection in metastatic pancreatic ductal adenocarcinoma (mPDAC)

Int. J. Gynecological Cancer

Assessing robustness of an artificial intelligence derived histological biomarker across different sites of disease and in serial sections in tubo-ovarian high-grade serous carcinoma

Int. J. Gynecological Cancer

Predicting response to platinum-based chemotherapy for tubo-ovarian high-grade serous carcinoma using an artificial intelligence histopathology platform

Annals of Oncology

Prediction of neoadjuvant chemotherapy response in muscle-invasive bladder cancer: A machine learning approach

Validation of an artificial intelligence derived histological biomarker for gemcitabine response in resected pancreatic ductal adenocarcinoma (PDAC)

Gemcitabine response prediction in the adjuvant treatment of resected pancreatic ductal adenocarcinoma using an AI histopathology platform.

Scientific Data | Nature

DLBCL-Morph: Morphological features computed using deep learning for an annotated digital DLBCL image set