Artificial intelligence-based morphologic classification and molecular characterization of neuroblastic tumors from digital histopathology
Siddhi Ramesh, Emma Dyer, Monica Pomaville, Kristina Doytcheva, James M. 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 Sokol, Susan L. Cohn, Rani E. George, Alexander T. Pearson, Mark A. Applebaum
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.