Deep Learning Modeling to Differentiate Multiple Sclerosis From MOG Antibody-Associated Disease.
Обработка статьи · этапы, ошибки и управление
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
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, MAGNIMS Study Group
Аннотация
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 (95% CI 63-77), 67% sensitivity (95% CI 55-79), and 73% specificity (95% CI 61-83) in the validation cohort without retraining. When combined, the classifiers reached 86% accuracy (95% CI 81-92), 84% sensitivity (95% CI 75-92), and 89% specificity (95% CI 81-96). PAMs identified key region volumes: corpus callosum (1872 mm3), left precentral gyrus (341 mm3), right thalamus (193 mm3), and right cingulate cortex (186 mm3) for identifying RRMS and brainstem (629 mm3), hippocampus (234 mm3), and parahippocampal gyrus (147 mm3) for identifying MOGAD.
DISCUSSION: Both classifiers effectively distinguished RRMS from MOGAD. The clinical/MRI model showed higher sensitivity while the DL model offered higher specificity, suggesting complementary roles. Their combination improved diagnostic accuracy, and PAMs revealed distinct damage patterns. Future prospective studies should validate these models in diverse, real-world settings.
CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that both a clinical/MRI algorithm and an MRI-based DL model accurately distinguish RRMS from MOGAD.