Objectives: To stratify patients with multiple sclerosis (pwMS) based on brain MRI-derived volumetric features using unsupervised machine learning. Methods: The 3-T brain MRIs of relapsing-remitting pwMS including 3D-T1w and FLAIR-T2w sequences were retrospectively collected, along with Expanded Disability Status Scale (EDSS) scores and long-term (10 ± 2 years) clinical outcomes (EDSS, cognition, and progressive course). From the MRIs, volumes of demyelinating lesions and 116 atlas-defined gray matter regions were automatically segmented and expressed asz-scores referenced to external populations. Following feature selection, baseline MRI-derived biomarkers entered the Subtype and Stage Inference (SuStaIn) algorithm, which estimates subgroups characterized by distinct patterns of biomarker evolution and stages within subgroups. The trained model was then applied to longitudinal MRIs. Stability of subtypes and stage change over time were assessed via Krippendorf’s α and multilevel linear regression models, respectively. The prognostic relevance of SuStaIn classification was assessed with ordinal/logistic regression analyses. Results: We selected 425 pwMS (35.9 ± 9.9 years; F/M: 301/124), corresponding to 1129 MRI scans, along with healthy controls (N = 148; 35.9 ± 13.0 years; F/M: 77/71) and external pwMS (N = 80; 40.4 ± 11.9 years; F/M: 56/24) as reference populations. Based on 11 biomarkers surviving feature selection, two subtypes were identified, designated as “deep gray matter (DGM)-first” subtype (N = 238) and “cortex-first” subtype (N = 187) according to the atrophy pattern. Subtypes were consistent over time (α = 0.806), with significant annual stage increase (b = 0.20; p < 0.001). EDSS was associated with stage and DGM-first subtype (p ≤ 0.02). Baseline stage predicted long-term disability, transition to progressive course, and cognitive impairment (p ≤ 0.03), with the latter also associated with DGM-first subtype (p = 0.005). Conclusions: Unsupervised learning modelling of brain MRI-derived volumetric features provides a biologically reliable and prognostically meaningful stratification of pwMS.

Stratification of multiple sclerosis patients using unsupervised machine learning: a single-visit MRI-driven approach / Pontillo, Giuseppe; Penna, Simone; Cocozza, Sirio; Quarantelli, Mario; Gravina, Michela; Lanzillo, Roberta; Marrone, Stefano; Costabile, Teresa; Inglese, Matilde; Morra, Vincenzo Brescia; Riccio, Daniele; Elefante, Andrea; Petracca, Maria; Sansone, Carlo; Brunetti, Arturo. - In: EUROPEAN RADIOLOGY. - ISSN 0938-7994. - (2022). [10.1007/s00330-022-08610-z]

Stratification of multiple sclerosis patients using unsupervised machine learning: a single-visit MRI-driven approach

Pontillo, Giuseppe
Primo
;
Cocozza, Sirio;Gravina, Michela;Lanzillo, Roberta;Marrone, Stefano;Costabile, Teresa;Morra, Vincenzo Brescia;Riccio, Daniele;Elefante, Andrea;Petracca, Maria;Sansone, Carlo;Brunetti, Arturo
Ultimo
2022

Abstract

Objectives: To stratify patients with multiple sclerosis (pwMS) based on brain MRI-derived volumetric features using unsupervised machine learning. Methods: The 3-T brain MRIs of relapsing-remitting pwMS including 3D-T1w and FLAIR-T2w sequences were retrospectively collected, along with Expanded Disability Status Scale (EDSS) scores and long-term (10 ± 2 years) clinical outcomes (EDSS, cognition, and progressive course). From the MRIs, volumes of demyelinating lesions and 116 atlas-defined gray matter regions were automatically segmented and expressed asz-scores referenced to external populations. Following feature selection, baseline MRI-derived biomarkers entered the Subtype and Stage Inference (SuStaIn) algorithm, which estimates subgroups characterized by distinct patterns of biomarker evolution and stages within subgroups. The trained model was then applied to longitudinal MRIs. Stability of subtypes and stage change over time were assessed via Krippendorf’s α and multilevel linear regression models, respectively. The prognostic relevance of SuStaIn classification was assessed with ordinal/logistic regression analyses. Results: We selected 425 pwMS (35.9 ± 9.9 years; F/M: 301/124), corresponding to 1129 MRI scans, along with healthy controls (N = 148; 35.9 ± 13.0 years; F/M: 77/71) and external pwMS (N = 80; 40.4 ± 11.9 years; F/M: 56/24) as reference populations. Based on 11 biomarkers surviving feature selection, two subtypes were identified, designated as “deep gray matter (DGM)-first” subtype (N = 238) and “cortex-first” subtype (N = 187) according to the atrophy pattern. Subtypes were consistent over time (α = 0.806), with significant annual stage increase (b = 0.20; p < 0.001). EDSS was associated with stage and DGM-first subtype (p ≤ 0.02). Baseline stage predicted long-term disability, transition to progressive course, and cognitive impairment (p ≤ 0.03), with the latter also associated with DGM-first subtype (p = 0.005). Conclusions: Unsupervised learning modelling of brain MRI-derived volumetric features provides a biologically reliable and prognostically meaningful stratification of pwMS.
2022
Stratification of multiple sclerosis patients using unsupervised machine learning: a single-visit MRI-driven approach / Pontillo, Giuseppe; Penna, Simone; Cocozza, Sirio; Quarantelli, Mario; Gravina, Michela; Lanzillo, Roberta; Marrone, Stefano; Costabile, Teresa; Inglese, Matilde; Morra, Vincenzo Brescia; Riccio, Daniele; Elefante, Andrea; Petracca, Maria; Sansone, Carlo; Brunetti, Arturo. - In: EUROPEAN RADIOLOGY. - ISSN 0938-7994. - (2022). [10.1007/s00330-022-08610-z]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/878044
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