Advantages of digital technology in the assessment of bone marrow involvement in Gaucher's disease
Mostra el registre complet Registre parcial de l'ítem
- dc.contributor.author Valero-Tena, Esther
- dc.contributor.author Roca-Espiau, Mercedes
- dc.contributor.author Verdú-Díaz, José
- dc.contributor.author Diaz-Manera, Jordi
- dc.contributor.author Andrade-Campos, Marcio
- dc.contributor.author Giraldo, Pilar
- dc.date.accessioned 2024-03-05T07:32:28Z
- dc.date.available 2024-03-05T07:32:28Z
- dc.date.issued 2023
- dc.description.abstract Gaucher disease (GD) is a genetic lysosomal disorder characterized by high bone marrow (BM) involvement and skeletal complications. The pathophysiology of these complications is not fully elucidated. Magnetic resonance imaging (MRI) is the gold standard to evaluate BM. This study aimed to apply machine-learning techniques in a cohort of Spanish GD patients by a structured bone marrow MRI reporting model at diagnosis and follow-up to predict the evolution of the bone disease. In total, 441 digitalized MRI studies from 131 patients (M: 69, F:62) were reevaluated by a blinded expert radiologist who applied a structured report template. The studies were classified into categories carried out at different stages as follows: A: baseline; B: between 1 and 4 y of follow-up; C: between 5 and 9 y; and D: after 10 years of follow-up. Demographics, genetics, biomarkers, clinical data, and cumulative years of therapy were included in the model. At the baseline study, the mean age was 37.3 years (1-80), and the median Spanish MRI score (S-MRI) was 8.40 (male patients: 9.10 vs. female patients: 7.71) (p < 0.001). BM clearance was faster and deeper in women during follow-up. Genotypes that do not include the c.1226A>G variant have a higher degree of infiltration and complications (p = 0.017). A random forest machine-learning model identified that BM infiltration degree, age at the start of therapy, and femur infiltration were the most important factors to predict the risk and severity of the bone disease. In conclusion, a structured bone marrow MRI reporting in GD is useful to standardize the collected data and facilitate clinical management and academic collaboration. Artificial intelligence methods applied to these studies can help to predict bone disease complications.
- dc.format.mimetype application/pdf
- dc.identifier.citation Valero-Tena E, Roca-Espiau M, Verdú-Díaz J, Diaz-Manera J, Andrade-Campos M, Giraldo P. Advantages of digital technology in the assessment of bone marrow involvement in Gaucher's disease. Front Med (Lausanne). 2023 May 12;10:1098472. DOI: 10.3389/fmed.2023.1098472
- dc.identifier.doi http://dx.doi.org/10.3389/fmed.2023.1098472
- dc.identifier.issn 2296-858X
- dc.identifier.uri http://hdl.handle.net/10230/59323
- dc.language.iso eng
- dc.publisher Frontiers
- dc.relation.ispartof Front Med (Lausanne). 2023 May 12;10:1098472
- dc.rights © 2023 Valero-Tena, Roca-Espiau, Verdú-Díaz, Diaz-Manera, Andrade-Campos and Giraldo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (http://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri http://creativecommons.org/licenses/by/4.0/
- dc.subject.keyword Gaucher disease
- dc.subject.keyword Bone disease
- dc.subject.keyword Bone marrow MRI
- dc.subject.keyword Predictive factors
- dc.subject.keyword Random forest machine-learning study
- dc.title Advantages of digital technology in the assessment of bone marrow involvement in Gaucher's disease
- dc.type info:eu-repo/semantics/article
- dc.type.version info:eu-repo/semantics/publishedVersion