Your Daily digest for NIH Director's Blog Daily Digest (Unofficial)

[Article Digests for Psychology & Social Work]

NIH Director's Blog Daily Digest (Unofficial)

 

(https://directorsblog.nih.gov/2024/10/03/ai-model-takes-new-approach-to-performing-diagnostic-tasks-in-multiple-cancer-types/) AI Model Takes New Approach to Performing Diagnostic Tasks in Multiple Cancer Types
Oct 3rd 2024, 09:00

Credit: Donny Bliss/NIH, Adobe Stock

In recent years, medical researchers have been looking for ways to use artificial intelligence (AI) technology for diagnosing cancer. So far, most AI models have been developed to perform specific tasks in cancer diagnosis, such as detecting cancer presence or predicting a tumor’s genetic profile in certain cancer types. But what if an AI system could be more flexible, like a large language model such as ChatGPT, performing a variety of diagnostic tasks across multiple cancer types?

As reported in the journal (https://www.nature.com/articles/s41586-024-07894-z) Nature, researchers have developed an AI system that can perform a wide range of cancer evaluation tasks and outperforms current AI methods in tasks like cancer cell detection and tumor origin identification. It was tested on 19 cancer types, leading the researchers to refer to it as “ChatGPT-like” in its flexibility. According to the research team, whose work is supported in part by NIH, this is also the first AI model based on analyzing slide images to not only accurately predict if a cancer is likely to respond to treatment, but also to validate these predictions across multiple patient groups around the world.

Today, when doctors order a biopsy to find out if cancer is present, those samples are sent to a pathologist, who examines the tissues or cells under a microscope to determine if they are cancerous. The team behind this AI model, led by (https://yulab.hms.harvard.edu/yu/) Kun-Hsing Yu, Harvard Medical School, Boston, recognized that pathologists must analyze a wide variety of disease samples. To make accurate diagnoses in different cancer types, they must take many subtle factors into account.

Most earlier attempts to devise an AI model to analyze tissue samples have depended on training computers to recognize one cancer type at a time. In the new work, the researchers developed a more general-purpose pathology AI system that could analyze a broader range of tissues and sample types. To develop their Clinical Histopathology Imaging Evaluation Foundation (CHIEF) model, the researchers used an AI approach known as self-supervised learning. In this method, a computer is given large volumes of data, in this case 15 million pathology images, to allow it to identify intrinsic patterns and structures. This process allows a computer to “learn” from experience to identify informative features in a vast data set.

The tool was then trained further on more than 60,500 whole-slide images in tissues collected from 19 different parts of the body—such as the lungs, breast, prostate, kidney, brain, and bladder—to bolster the model’s ability to capture similarities and differences among cancer types. This training data was in part comprised of data from The Cancer Genome Atlas (TCGA) program and the Genotype-Tissue Expression (GTEx) Project, both NIH-supported resources. The researchers directed the model to consider both the image as a whole and its finer details, enabling it to interpret the image in a broader context than one region. They then put CHIEF to the test, using another 19,491 whole-slide images from 32 independent slide sets collected from 24 hospitals around the world.

They found that CHIEF worked equally well no matter how the samples were collected (biopsy or surgical excision) and in different clinical settings. In addition to detecting cancers and predicting a cancer’s tissue of origin, CHIEF also predicted with 70% accuracy whether a tissue carried one among dozens of genetic mutations that are commonly seen in cancers. CHIEF showed an ability to predict whether a sample contained mutated copies of 18 genes that oncologists use to make treatment decisions. CHIEF could predict better than earlier models how long a patient was likely to survive following a cancer diagnosis and how aggressively a particular cancer would grow.

This is all good news, but there’s much more work ahead before an AI model like this could be used in the clinic. Next steps for the researchers include training the model on images of tissues from rare cancers, as well as from pre-cancerous and non-cancerous conditions. With continued development and validation, the researchers aim to enable the system to identify cancers most likely to benefit from targeted or experimental therapies in hopes of improving outcomes for more people with cancer in diverse clinical settings around the world.

Reference:

Wang X, et al. (https://pubmed.ncbi.nlm.nih.gov/39232164/) A pathology foundation model for cancer diagnosis and prognosis prediction. Nature. DOI: 10.1038/s41586-024-07894-z (2024).

NIH Support: National Institute of General Medical Sciences, National Cancer Institute

Forwarded by:
Michael Reeder LCPC
Baltimore, MD

This information is taken from free public RSS feeds published by each organization for the purpose of public distribution. Readers are linked back to the article content on each organization's website. This email is an unaffiliated unofficial redistribution of this freely provided content from the publishers. 

 

(#) unsubscribe from this feed
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://lists.clinicians-exchange.org/pipermail/article-digests-clinicians-exchange.org/attachments/20241004/681b1a32/attachment.htm>