Cleveland Clinic: AI could help personalize treatment for lung cancer patients

Pre-treatment scans were input into a deep-learning model, which analyzed the scans to create an image signature that predicts treatment outcomes.
By Nathan Eddy
03:21 PM

Artificial intelligence and machine learning networks could help personalize radiation therapy for lung cancer, according to a new study by the Cleveland Clinic.

The research, published in The Lancet Digital Health, centers around an artificial neural network built with a large dataset of patients receiving lung radiotherapy.

That network, which allows each clinical center to utilize their own CT datasets to customize the framework and tailor it to their specific patient population, was built using CT scans and the electronic health records of nearly a thousand lung cancer patients treated with high-dose radiation.

The company's framework uses probability estimates to select an optimized dose that prevents treatments failures to a set level, for instance a five percent probability of failure.

Pre-treatment scans were input into a deep-learning model, which analyzed the scans to create an image signature that predicts treatment outcomes.

This image signature was combined with data from patient health records, to generate a personalized radiation dose using advanced mathematical modeling.

"AI can learn from imaging and electronic health records and make predictions about the likelihood an individual patient could fail radiation treatments," lead author Dr. Mohamed Abazeed, a radiation oncologist at Cleveland Clinic's Taussig Cancer Institute and a researcher at the Lerner Research Institute, told HealthcareITNews.

"Therefore," he said, "AI can help individualize radiotherapy treatments for patients with cancers in the lung."

Dr. Abazeed explained they will assess the transportability of the model across varied hospital systems via local implementation or using large-scale federated datasets.

In the future AI-models could be optimized based on different target populations based on ethnicities, gender or age, medical settings (community hospital or academic center) geographical locales  or even include temporally distinct populations.

"We will also test the putative supremacy of iGray--individualized dose--recommendations head-to-head with standard of care recommendations in a prospective clinical trial," Dr. Abazeed said.

In reference to those who believe AI technology still has much farther to go before it has practical applications for the medical and healthcare sectors Dr. Abazeed noted a prerequisite for scientific progress is the willful suspension of disbelief.

"In large part driven by this work, we are on the precipice for practical and innovative implementations in the highly standardized and data-replete discipline of radiation oncology," he said. 

The study follows news that French biopharmaceutical company Sanofi and tech giant Google are partnering to leverage machine learning, AI and deep analytics technologies across data sets to better understand major diseases.

Meanwhile, a new study from Innovaccer explores the ways its AI algorithms could be put to work to improve risk scoring and stratification and enhance value-based care initiatives.

Nathan Eddy is a healthcare and technology freelancer based in Berlin.
Email the writer:
Twitter: @dropdeaded209

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