Memorial Sloan Kettering Cancer Center shared a post on LinkedIn:

“NEW in Nature Medicine:

A study led by Memorial Sloan Kettering Cancer Center (MSK) and the Icahn School of Medicine at Mount Sinai suggests that AI could significantly improve how tumor samples are analyzed in the lab using a model trained to identify lung cancer mutations. The approach cut the number of molecular tests needed by up to 43% while maintaining clinical standards.

‘Our findings demonstrate the real-world clinical utility of this computational biomarker,’ says MSK pathologist Dr. Chad Vanderbilt, a co-senior author of the study.”

Title: Real-world deployment of a fine-tuned pathology foundation model for lung cancer biomarker detection

Authors: Gabriele Campanella, Neeraj Kumar, Swaraj Nanda, Siddharth Singi, Eugene Fluder, Ricky Kwan, Silke Muehlstedt, Nicole Pfarr, Peter J. Schüffler, Ida Häggström, Noora Neittaanmäki, Levent M. Akyürek, Alina Basnet, Tamara Jamaspishvili, Michel R. Nasr, Matthew M. Croken, Fred R. Hirsch, Arielle Elkrief, Helena Yu, Orly Ardon, Gregory M. Goldgof, Meera Hameed, Jane Houldsworth, Maria Arcila, Thomas J. Fuchs, Chad Vanderbilt

Read the Full Article on Nature Medicine

AI Could Significantly Improve How Tumor Samples are Analyzed in the Lab - Memorial Sloan Kettering Cancer Center

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AI Could Significantly Improve How Tumor Samples are Analyzed in the Lab – Memorial Sloan Kettering Cancer Center

‘Our findings demonstrate the real-world clinical utility of this computational biomarker,’ says MSK pathologist Dr. Chad Vanderbilt, a co-senior author of the study.”