New York approves AI-driven breast cancer diagnostic

With Clinical Laboratory Evaluation Program assay validation, the company’s Clinical Laboratory Improvement Amendments-certified lab can begin testing patient samples.

WHY IT MATTERS

Along with New York State Department of Health clinical and analytical validation of this use of array morphology, the results of the PDxBr test were reproducible.

“Consistent with our mission of improving healthcare, PDxBr has shown to be an effective prognostic tool to further improve risk stratification over current histopathology methods,” said Wayne Brinster, CEO of PreciseDx, in an announcement.

The company says the AI technology has now proven its ability to enhance invasive breast cancer pathology interpretation with objective, quantifiable and accurate data.

“This data, which was also part of the submission to New York State, provides more robust information regarding patient disease status, and represents the next generation of pathology analysis.”

THE LARGER TREND

AI is being used to provide greater accuracy in oncology care from image analysis to matching patient and tumor attributes in order to better identify appropriate clinical trials, as well as to improve speed to treatment.

At HCA Healthcare, identifying newly diagnosed cancer patients proved to be time-consuming with manual searches through pathology, physician schedules and referrals.

The AI program surfaced positive cancer patients from pathological and radiological reports, and alerted nurse navigators to possible cancer diagnoses. The results were integrated into HCA’s Meditech electronic health records.

For HCA, there was a decrease in time from cancer diagnosis to first treatment by six days and a patient-retention increase of 15%, said Kristina Rua, RN, former director of oncology navigation services in HCA’s East Florida division and now president of the Florida Academy of Oncology Nurse and Patient Navigators.

At a HIMSS21 Europe session on exploring new frontiers in AI for cancer care, Dr. Tufia Haddad with the Mayo Clinic shared that the use of a clinical trial-matching tool led not only to greater efficiencies, but also left more time for patient care in clinical practice.

“There was an immediate and sustained impact,” she said.