If you have mesothelioma, your cancer tumors will be made up of different types of cells, some of which are easier for doctors to treat than others. That said, doctors sometimes have trouble determining mesothelioma cell type.

Scientists in the United Kingdom have developed MesoGraph, which uses artificial intelligence to precisely identify which mesothelioma cells are present so patients can get the right treatments. Learn more about MesoGraph and how it could help patients below.

Why It’s Hard to Determine Mesothelioma Cell Type

Doctors classify mesothelioma tumor cells into three types:

  • Epithelioid (or epithelial)
  • Sarcomatoid
  • Biphasic (tumor with both epithelioid and sarcomatoid cells)

It’s crucial to know which mesothelioma cell type is present in a patient’s tumor, as it will impact which treatments will work best. The sarcomatoid and biphasic cell types are more aggressive and harder to treat than the epithelial cell type.

Unfortunately, it’s often hard for doctors to determine the cell type. This is because many mesothelioma tumors may actually contain both epithelial and sarcomatoid cells and it’s hard to know exactly how much of each cell is present.

MesoGraph hopes to solve this problem and make it easier to determine the cell type.

What Does MesoGraph Do?

MesoGraph is a machine learning model — an AI program that can make decisions since it’s been trained to recognize patterns.

MesoGraph determines how many sarcomatoid cells are present in a tumor sample. With it, doctors can assess a sample much more quickly and consistently.

The MesoGraph model looks at several features of the different cell types before determining the sample’s category. The tool was able to accurately determine cell type for 85% of the samples examined.

“With the increasing use of digital pathology, this model represents a first point of entry for an AI-based clinical tool … to more accurately define epithelioid and sarcomatoid components in mesothelioma … and potentially create future opportunities to improve clinical decisionmaking and prognosis,” the Cell Reports Medicine study on MesoGraph reported.

Doctors can then use the information from MesoGraph to determine the best treatment plan for a patient and provide a more accurate assessment of a patient’s prognosis.

This isn’t the first use of artificial intelligence tools to improve mesothelioma diagnosis or the outcomes of treatments. Several other studies have used computer models to provide more consistent analysis of imaging scans and more.

Get Help Finding a Mesothelioma Specialist for Effective Treatment

The best mesothelioma specialists carefully craft a patient’s treatment plan based on the unique factors impacting their diagnosis — including mesothelioma cell type.

The MesoGraph tool could further help specialists provide the best possible treatment for their patients sooner and more effectively.

If you have been diagnosed with mesothelioma, Mesothelioma Hope’s Free Doctor Match program can help you find a specialist near you. Or, contact our Patient Advocates at (866) 608-8933 now to get help finding doctors, treatments, and financial aid for medical care.

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beth swantekWritten by:

Contributing Author at Mesothelioma Hope

Beth Swantek has been writing about the dangers of asbestos since 2013. Beth served as a media professional for over 30 years and began her career as a broadcast journalist. After her daughter suffered a traumatic brain injury at birth, Beth has devoted her life to helping men and women experiencing deep loss — such as those living with mesothelioma.

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  1. Eastwood M., et al., (2023). MesoGraph: Automatic profiling of mesothelioma subtypes from histological images. Cell Reports Medicine. Retrieved October 24, 2023, from https://www.cell.com/cell-reports-medicine/pdf/S2666-3791(23)00403-2.pdf.
  2. Meyerhoff, R. R., Yang, C. F., Speicher, P. J., et al. (2015). Impact of mesothelioma histologic subtype on outcomes in the Surveillance, Epidemiology, and End Results database. The Journal of Surgical Research. Retrieved October 24, 2023, from https://pubmed.ncbi.nlm.nih.gov/25791825/.

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