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<p>To provide a useful and practical Machine Learning framework to facilitate the diagnosis of Neuropsychiatric Systemic Lupus Erythematosus (NPSLE) and Systemic Lupus Erythematosus (SLE) from Magnetic Resonance Imaging (MRI) derived features. Twenty-seven SLE patients (14 NPSLE, 13 SLE; 24 females and 3 males; average age:&#xa0;43&#xa0;years, age range: 21 to 62) and 20 healthy controls (17 females and 3 males; average age 41, age range: 21 to 56), were included in this cross-sectional study. VolBrain online platform was used to quantitatively assess brain structural features (regional cortical thickness) which were used as input for the Machine Learning models. Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF) and XGBoost were trained and tested, using a fivefold cross validation in the process. The Random Forest model demonstrated superior performance with an accuracy of 90% on the test, distinguishing itself as the most effective classifier between the three classes without the need for L1 or L2 regularization. The most relevant volumetric features for the classification were Precentral gyrus right thickness norm, angular gyrus thickness asymmetry and Parietal thickness asymmetry. The Random Forest algorithm was the most effective model for classifying between NPSLE, SLE and controls patients in the context of this study, highlighting a potential path for clinical diagnosis support through machine learning.</p>

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Machine Learning for MRI Classification of Systemic Lupus Erythematous Patients with and without Neuropsychiatric Events

  • 期刊论文

  • 发表日期：

    2026年2月12日

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  • Sebastiano Vacca,
  • Gianluca Chabert,
  • Matteo Piga,
  • Lorenzo Mannelli,
  • Elisabetta Chessa,
  • Jasjit S. Suri,
  • Massimo Caulo,
  • Abdelkader Mahammedi,
  • Antonella Balestrieri,
  • Matteo Fraschini,
  • Alberto Cauli,
  • Luca Saba

摘要

To provide a useful and practical Machine Learning framework to facilitate the diagnosis of Neuropsychiatric Systemic Lupus Erythematosus (NPSLE) and Systemic Lupus Erythematosus (SLE) from Magnetic Resonance Imaging (MRI) derived features. Twenty-seven SLE patients (14 NPSLE, 13 SLE; 24 females and 3 males; average age: 43 years, age range: 21 to 62) and 20 healthy controls (17 females and 3 males; average age 41, age range: 21 to 56), were included in this cross-sectional study. VolBrain online platform was used to quantitatively assess brain structural features (regional cortical thickness) which were used as input for the Machine Learning models. Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF) and XGBoost were trained and tested, using a fivefold cross validation in the process. The Random Forest model demonstrated superior performance with an accuracy of 90% on the test, distinguishing itself as the most effective classifier between the three classes without the need for L1 or L2 regularization. The most relevant volumetric features for the classification were Precentral gyrus right thickness norm, angular gyrus thickness asymmetry and Parietal thickness asymmetry. The Random Forest algorithm was the most effective model for classifying between NPSLE, SLE and controls patients in the context of this study, highlighting a potential path for clinical diagnosis support through machine learning.