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AI-Assisted Pathology Poised to Transform Liver Disease Care


 

More Effective Clinical Trials

The limitations of conventional pathology may be responsible, at least in part, for the repeated failure of novel compounds to move from phase 2 to phase 3 clinical trials, and from clinical trials to approval, the sources agreed.

“In clinical trials, patients are subject to enrollment criteria using liver biopsies, which are scored with a composite scoring system involving four different histologic components to grade and stage the disease,” Dr. Wack noted.

However, there is wide variability between pathologists on biopsy scoring, and an individual pathologist presented with the same sample may give it a different score after some time has passed, she said.

That means “we are using a nonstandardized and inconsistent scoring system to determine whether a patient can be enrolled or not into a trial,” Dr. Wack said.

The change in the composite score over a follow-up period, usually 1-2 years, determines whether a patient has responded to the candidate drug and, ultimately, whether that drug could be considered for approval, Dr. Wack said.

Because scores at the baseline and follow-up timepoints are not precise and inconsistent across pathologist readers, and even the same reader over time, there are often many “false-positive” and “false-negative” responses that can result in potential therapeutics either failing or succeeding in clinical trials, she said.

To address this variability in biopsy scoring as it relates to clinical trials, regulatory bodies have recommended a consensus approach, in which multiple pathologists read the same biopsy independently and a median score is used, or pathologists convene to come to an agreement, Dr. Wack said.

“This is a very costly and burdensome approach and is still subject to interconsensus panel variation,” she said.

The introduction of digital pathology using validated digital viewers, where pathologists can view a glass slide digitally and pan and zoom over the image as they can with a microscope, means that many pathologists can read the same slide in parallel, she explained.

“If they need to discuss, they can do so efficiently over a phone call, each using their own computer screen and shared annotation tools to facilitate their discussion.”

Although this consensus approach can improve consistency, it still leads to variability in scoring across different groups of pathologists, Dr. Wack said.

This is where AI-assisted pathology comes into play.

“With this approach, a pathologist still views the image digitally, but an algorithm has predicted and highlighted key features and recommended quantitative scores,” she said.

This approach has been shown to increase precision for pathologists, thereby increasing reproducibility and standardizing scoring across timepoints and clinical trials.

What’s Ahead

These AI tools could address pathology’s lack of scalability, the result of a limited number of trained pathologists capable of doing liver biopsy assessments, Dr. Tai said.

“Digital pathology workflows enable the transformation of conventional histologic glass slides into large digital images using scanners, allowing significant productivity gains in terms of workflow and collaboration,” he said.

Although AI-assisted pathology tools are still being validated, their promise for improving diagnoses and uncovering new treatments is clear, the interviewees agreed.

Extending its use to stage fibrosis in other liver diseases, such as primary biliary cholangitis, primary sclerosing cholangitis, and alcoholic liver disease, is also in progress on an experimental basis but will take time to validate.

“The landscape will evolve quickly in the coming 3-4 years,” Dr. Petitjean predicted. “To start, their intended use will likely be limited to a decision-support tool to enhance the performance of pathologists and perhaps stratify or triage cases sent for routine vs expert review.”

Dr. Petitjean even suggested that the increasing role of NITs and the amount of data being generated prospectively and retrospectively around liver biomarkers could mean that liver biopsies might not be needed one day.

A version of this article appeared on Medscape.com.

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