AI model to predict survival factors in lung cancer for better trial recruitment

By Maggie Lynch contact

- Last updated on GMT

(Image: Getty/metamorworks)
(Image: Getty/metamorworks)

Related tags: Artificial intelligence, Lung cancer, AI, trial recruitment, Clinical trial data, Clinical trial, Concerto HealthAI

Concerto HealthAI launches model to predict survival rates in lung cancer patients in an aim to garner disease insights that could improve enrollment criteria.

The company, which specializes in using oncology-specific real-world data (RWD) and artificial intelligence (AI), built the Gradient Boosting model using de-identified structured data from 55,000 lung cancer patients that were in the American Society of Clinical Oncology’s (ASCO) database, CancerLinQ Discovery.

The database is considered to be one of the most comprehensive RWD sets available, and was previously used in a collaboration between Concerto HealthAI and Pfizer​.

Using this data, Concerto HealthAI examined 4,000 unique variables, including diagnostic and therapeutic codes, biomarkers, and lab test results. The model was validated on a set of 8,468 patients, according to the company.

Smita Agrawal, senior director of product management at Concerto HealthAI said in a statement that a model that can predict mortality in patients at different time points can enable clinical development researchers to narrow the clinical trial enrollment criteria for pre- and post-approval studies.

Concerto HealthAI’s CEO Jeff Elton noted that high costs are associated with patients recruited into a clinical trial. He added in a statement, “The stakes are high but researchers just haven’t had the tools to give them the insights to change how they work and design trials with speed and efficiency.

“Researchers can now determine with a high degree of confidence which patients are expected to survive the next three months and recruit accordingly to ensure a successful clinical trial at a faster pace.”

Bristol-Myers Squibb tapped Concerto HealthAI​ last month to use the company’s machine learning platform to establish protocol design for precision oncology treatment.

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