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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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-11-01
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| Series: | npj Precision Oncology |
| Online Access: | https://doi.org/10.1038/s41698-024-00745-0 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Siddhi Ramesh |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3ee6eb5342a14a0d87b21a668f03f699 |
| institution | Kabale University |
| issn | 2397-768X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Precision Oncology |
| spelling | doaj-art-3ee6eb5342a14a0d87b21a668f03f6992024-11-10T12:04:54ZengNature Portfolionpj Precision Oncology2397-768X2024-11-01811610.1038/s41698-024-00745-0Artificial intelligence-based morphologic classification and molecular characterization of neuroblastic tumors from digital histopathologySiddhi Ramesh0Emma Dyer1Monica Pomaville2Kristina Doytcheva3James Dolezal4Sara Kochanny5Rachel Terhaar6Casey J. Mehrhoff7Kritika Patel8Jacob Brewer9Benjamin Kusswurm10Arlene Naranjo11Hiroyuki Shimada12Nicole A. Cipriani13Aliya N. Husain14Peter Pytel15Elizabeth A. Sokol16Susan L. Cohn17Rani E. George18Alexander T. Pearson19Mark A. Applebaum20Department of Medicine, University of ChicagoDepartment of Medicine, University of ChicagoDepartment of Pediatrics, The Children’s Hospital of PhiladelphiaAnatomic Pathology Department of Oklahoma University Medical CenterGeisinger Cancer InstituteDepartment of Medicine, University of ChicagoPritzker School of Medicine, University of ChicagoIntermountain Primary Children’s Hospital, Huntsman Cancer Institute, Spencer Fox Eccles School of Medicine at the University of UtahCancer and Blood Disorders Center, Seattle Children’s HospitalApplied Data Science Program, University of ChicagoApplied Data Science Program, University of ChicagoChildren’s Oncology Group Statistics and Data Center, Department of Biostatistics, University of FloridaDepartment of Pathology and Pediatrics, Stanford UniversityDepartment of Pathology, University of ChicagoDepartment of Pathology, University of ChicagoDepartment of Pathology, University of ChicagoDivision of Hematology, Oncology, and Stem Cell Transplantation, Ann & Robert H. Lurie Children’s Hospital of Chicago, Northwestern University Feinberg School of MedicineDepartment of Pediatrics, University of ChicagoDepartment of Pediatric Hematology/Oncology, Dana-Farber Cancer Institute and Boston Children’s HospitalDepartment of Medicine, University of ChicagoDepartment of Pediatrics, University of ChicagoAbstract 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.https://doi.org/10.1038/s41698-024-00745-0 |
| spellingShingle | 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 Artificial intelligence-based morphologic classification and molecular characterization of neuroblastic tumors from digital histopathology npj Precision Oncology |
| title | Artificial intelligence-based morphologic classification and molecular characterization of neuroblastic tumors from digital histopathology |
| title_full | Artificial intelligence-based morphologic classification and molecular characterization of neuroblastic tumors from digital histopathology |
| title_fullStr | Artificial intelligence-based morphologic classification and molecular characterization of neuroblastic tumors from digital histopathology |
| title_full_unstemmed | Artificial intelligence-based morphologic classification and molecular characterization of neuroblastic tumors from digital histopathology |
| title_short | Artificial intelligence-based morphologic classification and molecular characterization of neuroblastic tumors from digital histopathology |
| title_sort | artificial intelligence based morphologic classification and molecular characterization of neuroblastic tumors from digital histopathology |
| url | https://doi.org/10.1038/s41698-024-00745-0 |
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