Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer's dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis.

De Francesco, S., Crema, C., Archetti, D., Muscio, C., Reid, R.I., Nigri, A., et al. (2023). Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA. SCIENTIFIC REPORTS, 13(1), 1-19 [10.1038/s41598-023-43706-6].

Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA

Lodi, Raffaele;Lodi, Raffaele;Testa, Claudia;Tonon, Caterina;
2023

Abstract

Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer's dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis.
2023
De Francesco, S., Crema, C., Archetti, D., Muscio, C., Reid, R.I., Nigri, A., et al. (2023). Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA. SCIENTIFIC REPORTS, 13(1), 1-19 [10.1038/s41598-023-43706-6].
De Francesco, Silvia; Crema, Claudio; Archetti, Damiano; Muscio, Cristina; Reid, Robert I.; Nigri, Anna; Bruzzone, Maria Grazia; Tagliavini, Fabrizio;...espandi
File in questo prodotto:
File Dimensione Formato  
De Francesco 2023.pdf

accesso aperto

Tipo: Versione (PDF) editoriale
Licenza: Licenza per Accesso Aperto. Creative Commons Attribuzione (CCBY)
Dimensione 2.67 MB
Formato Adobe PDF
2.67 MB Adobe PDF Visualizza/Apri
41598_2023_43706_MOESM1_ESM.docx

accesso aperto

Tipo: File Supplementare
Licenza: Licenza per Accesso Aperto. Creative Commons Attribuzione (CCBY)
Dimensione 11.49 MB
Formato Microsoft Word XML
11.49 MB Microsoft Word XML Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/958189
Citazioni
  • ???jsp.display-item.citation.pmc??? 0
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact