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

Applebaum M, Ramesh S, Dyer E, Pomaville M, Doytcheva K, Dolezal J, Kochanny S, Terhaar R, Mehrhoff C, Patel K, Brewer J, Kusswurm B, Naranjo A, Shimada H, Sokol E, Cohn S, George R, Pearson A. Artificial intelligence-based morphologic classification and molecular characterization of neuroblastic tumors from digital histopathology. Res Sq. 2024 Jun 4;. doi: 10.21203/rs.3.rs-4396782/v1. PubMed PMID: 38883758; PubMed Central PMCID: PMC11177984.

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 digital images. The model demonstrated strong performance in identifying diagnostic category, grade, mitosis-karyorrhexis index (MKI), and MYCN-amplification on an external test dataset. This AI-based approach establishes a valuable tool for automating diagnosis and precise classification of neuroblastoma tumors.