| Title: |
Deep Learning Modeling to Differentiate Multiple Sclerosis From MOG Antibody–Associated Disease |
| Authors: |
Cortese, Rosa; Sforazzini, Francesco; Gentile, Giordano; de Mauro, Anna; Luchetti, Ludovico; Amato, Maria Pia; Apóstolos-Pereira, Samira Luisa; Arrambide, Georgina; Bellenberg, Barbara; Bianchi, Alessia; Bisecco, Alvino; Bodini, Benedetta; Calabrese, Massimiliano; Camera, Valentina; Celius, Elisabeth G.; de Medeiros Rimkus, Carolina; Duan, Yunyun; Durand-Dubief, Françoise; Filippi, Massimo; Gallo, Antonio; Gasperini, Claudio; Granziera, Cristina; Groppa, Sergiu; Grothe, Matthias; Gueye, Mor; Inglese, Matilde; Jacob, Anu; Lapucci, Caterina; Lazzarotto, Andrea; Liu, Yaou; Llufriu, Sara; Lukas, Carsten; Marignier, Romain; Messina, Silvia; Müller, Jannis; Palace, Jacqueline; Pastó, Luisa; Paul, Friedemann; Prados, Ferran; Pröbstel, Anne-Katrin; Rovira, Àlex; Rocca, Maria Assunta; Ruggieri, Serena; Sastre-Garriga, Jaume; Sato, Douglas Kazutoshi; Schneider, Ruth; Sepulveda, Maria; Sowa, Piotr; Stankoff, Bruno; Tortorella, Carla; Barkhof, Frederik; Ciccarelli, Olga; Battaglini, Marco; De Stefano, Nicola |
| Contributors: |
Cortese, Rosa; Sforazzini, Francesco; Gentile, Giordano; De Mauro, Anna; Luchetti, Ludovico; Amato, Maria Pia; Apóstolos-Pereira, Samira Luisa; Arrambide, Georgina; Bellenberg, Barbara; Bianchi, Alessia; Bisecco, Alvino; Bodini, Benedetta; Calabrese, Massimiliano; Camera, Valentina; Celius, Elisabeth G.; De Medeiros Rimkus, Carolina; Duan, Yunyun; Durand-Dubief, Françoise; Filippi, Massimo; Gallo, Antonio; Gasperini, Claudio; Granziera, Cristina; Groppa, Sergiu; Grothe, Matthia; Gueye, Mor; Inglese, Matilde; Jacob, Anu; Lapucci, Caterina; Lazzarotto, Andrea; Liu, Yaou; Llufriu, Sara; Lukas, Carsten; Marignier, Romain; Messina, Silvia; Müller, Janni; Palace, Jacqueline; Pastó, Luisa; Paul, Friedemann; Prados, Ferran; Pröbstel, Anne-Katrin; Rovira, Àlex; Rocca, Maria Assunta; Ruggieri, Serena; Sastre-Garriga, Jaume; Sato, Douglas Kazutoshi; Schneider, Ruth; Sepulveda, Maria; Sowa, Piotr; Stankoff, Bruno; Tortorella, Carla; Barkhof, Frederik; Ciccarelli, Olga; Battaglini, Marco; De Stefano, Nicola |
| Publisher Information: |
Wolters Kluwer |
| Publication Year: |
2025 |
| Description: |
Background and objectives: Multiple sclerosis (MS) is common in adults while myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) is rare. Our previous machine-learning algorithm, using clinical variables, ≤6 brain lesions, and no Dawson fingers, achieved 79% accuracy, 78% sensitivity, and 80% specificity in distinguishing MOGAD from MS but lacked validation. The aim of this study was to (1) evaluate the clinical/MRI algorithm for distinguishing MS from MOGAD, (2) develop a deep learning (DL) model, (3) assess the benefit of combining both, and (4) identify key differentiators using probability attention maps (PAMs). Methods: This multicenter, retrospective, cross-sectional MAGNIMS study included scans from 19 centers. Inclusion criteria were as follows: adults with non-acute MS and MOGAD, with high-quality T2-fluid-attenuated inversion recovery and T1-weighted scans. Brain scans were scored by 2 readers to assess the performance of the clinical/MRI algorithm on the validation data set. A DL-based classifier using a ResNet-10 convolutional neural network was developed and tested on an independent validation data set. PAMs were generated by averaging correctly classified attention maps from both groups, identifying key differentiating regions. Results: We included 406 MRI scans (218 with relapsing remitting MS [RRMS], mean age: 39 years ±11, 69% F; 188 with MOGAD, age: 41 years ±14, 61% F), split into 2 data sets: a training/testing set (n = 265: 150 with RRMS, age: 39 years ±10, 72% F; 115 with MOGAD, age: 42 years ±13, 61% F) and an independent validation set (n = 141: 68 with RRMS, age: 40 years ±14, 65% F; 73 with MOGAD, age: 40 years ±15, 63% F). The clinical/MRI algorithm predicted RRMS over MOGAD with 75% accuracy (95% CI 67-82), 96% sensitivity (95% CI 88-99), and specificity 56% (95% CI 44-68) in the validation cohort. The DL model achieved 77% accuracy (95% CI 64-89), 73% sensitivity (95% CI 57-89), and 83% specificity (95% CI 65-96) in the training/testing cohort, and 70% accuracy ... |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| Relation: |
info:eu-repo/semantics/altIdentifier/pmid/40906978; info:eu-repo/semantics/altIdentifier/wos/WOS:001567550600001; volume:105; issue:6; numberofpages:13; journal:NEUROLOGY; https://hdl.handle.net/20.500.11768/188017 |
| DOI: |
10.1212/wnl.0000000000214075 |
| Availability: |
https://hdl.handle.net/20.500.11768/188017; https://doi.org/10.1212/wnl.0000000000214075 |
| Rights: |
info:eu-repo/semantics/openAccess ; license:Creative commons ; license uri:http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| Accession Number: |
edsbas.4911DBFD |
| Database: |
BASE |