The digital pathology platform provides patient-specific risk estimates to help clinicians tailor treatment for metastatic hormone-sensitive disease.
Artera announced the clinical availability of the ArteraAI Prostate Test, a digital pathology-based prognostic test designed to help inform treatment planning for patients with metastatic hormone-sensitive prostate cancer (mHSPC).
The disease state of mHSPC often involves variable outcomes and complex treatment decisions. While traditional clinical factors provide general prognostic insight, they do not offer individualized risk data. The ArteraAI Prostate Test aims to address this gap by delivering a patient-specific estimate of five-year prostate cancer-specific mortality in patients treated with androgen deprivation therapy and an androgen receptor pathway inhibitor.
“Artera’s platform was built to bring greater clarity to cancer treatment decisions,” says Andre Esteva, CEO of Artera, in a release. “With our expansion into metastatic hormone-sensitive prostate cancer, we are excited to leverage our validated MMAI approach for a more complex stage of disease, helping guide treatment decisions with greater confidence.”
The test builds on the company’s established biopsy-based multimodal artificial intelligence model used in localized prostate cancer. The platform was further validated in patients with mHSPC receiving ADT plus ARPI as significantly prognostic for mortality specific to prostate cancer.
“As treatment options for metastatic hormone-sensitive prostate cancer have evolved, tools to accurately assess individual patient risk in the context of modern combination therapy have not fully evolved alongside them,” says Calvin Chao, MD, PhD, vice president of medical science at Artera, in a release. “This clinically validated test is poised to help clinicians better tailor treatment intensity and duration to the individual patient.”
The ArteraAI Prostate Test is available through Artera and its commercial partner, Tempus, to support access within existing clinical workflows.
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