Artificial intelligence-based morphologic classification and molecular characterization of neuroblastic tumors from digital histopathology

Artificial intelligence-based morphologic classification and molecular characterization of neuroblastic tumors from digital histopathology

Abstract A deep learning model using attention-based multiple instance learning (aMIL) and self-supervised learning (SSL) was developed to perform pathologic classification of neuroblastic tumors and assess MYCN-amplification status using H&E-stained whole slide images from the largest reported...

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Main Authors: Siddhi Ramesh, Emma Dyer, Monica Pomaville, Kristina Doytcheva, James Dolezal, Sara Kochanny, Rachel Terhaar, Casey J. Mehrhoff, Kritika Patel, Jacob Brewer, Benjamin Kusswurm, Arlene Naranjo, Hiroyuki Shimada, Nicole A. Cipriani, Aliya N. Husain, Peter Pytel, Elizabeth A. Sokol, Susan L. Cohn, Rani E. George, Alexander T. Pearson, Mark A. Applebaum
Format: Article
Language:English
Published: Nature Portfolio 2024-11-01
Series:npj Precision Oncology
Online Access:https://doi.org/10.1038/s41698-024-00745-0
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Summary:Abstract A deep learning model using attention-based multiple instance learning (aMIL) and self-supervised learning (SSL) was developed to perform pathologic classification of neuroblastic tumors and assess MYCN-amplification status using H&E-stained whole slide images from the largest reported cohort to date. The model showed promising performance in identifying diagnostic category, grade, mitosis-karyorrhexis index (MKI), and MYCN-amplification with validation on an external test dataset, suggesting potential for AI-assisted neuroblastoma classification.
ISSN:2397-768X

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