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

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.

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Publication Details

Journal
npj Precision Oncology
Published
2024-11-07
DOI
https://doi.org/10.1038/s41698-024-00745-0
Citations
11
Primary Topic
Neuroblastoma Research and Treatments
Type
article
Field-Weighted Citation Impact
4.06

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article

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

Arlene Naranjo, Rani E. George, Nicole A. Cipriani, Kritika Patel et al.
11 citations
npj Precision Oncology
Neuroblastoma Research and Treatments
4.06
article

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

Arlene Naranjo, Rani E. George, Nicole A. Cipriani, Kritika Patel, Hiroyuki Shimada, Elizabeth Sokol, James M. Dolezal, Alexander T. Pearson, Susan L. Cohn, Peter Pytel, Sara Kochanny, Monica Pomaville, Kristina Doytcheva, Rachel TerHaar, Mark A. Applebaum, Siddhi Ramesh, Aliya N. Husain, Benjamin Kusswurm, Casey J. Mehrhoff, Emma Dyer, Jacob Brewer
article en
11 citations

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.

npj Precision OncologyVol. 8(1)
Intermountain Healthcare (US), Northwestern University (US), Boston Children's Hospital (US), Lurie Children's Hospital (US), Seattle Children's Hospital (US), Primary Children's Hospital (US), Children's Hospital of Philadelphia (US), Harvard University (US), University of Washington (US), University of Utah (US), Huntsman Cancer Institute (US), University Medical Center (US), University of Oklahoma Medical Center (US), University of Florida (US), University of Chicago (US), Center for Cancer and Blood Disorders (US), Dana-Farber Cancer Institute (US), Children's Oncology Group (US), Geisinger Neuroscience Institute (US), Stanford University (US)
University of Chicago, National Institutes of Health, Pritzker School of Medicine, National Cancer Institute
Openalex Percentile: Top 5%
Neuroblastoma Research and Treatments
4.06
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