Abstract
Introduction/PurposeSystemic lupus erythematosus (SLE) is a chronic auto-immune disease with a broad spectrum of clinical presentations, including heterogeneous neuropsychiatric (NP) syndromes. Structural brain abnormalities are commonly found in SLE and NPSLE, but their role in diagnosis is limited, and their usefulness in distinguishing between NPSLE patients and patients in which the NP symptoms are not primarily attributed to SLE (non-NPSLE) is non-existent. Self-supervised contrastive learning algorithms proved to be useful in classification tasks in rare diseases with limited number of datasets. Our aim was to apply self-supervised contrastive learning on T1-weighted images acquired from a well-defined cohort of SLE patients, aiming to distinguish between NPSLE and non-NPSLE patients.Subjects and MethodsWe used 3T MRI T1-weighted images of 163 patients. The training set comprised 68 non-NPSLE and 34 NPSLE patients. We applied random geometric transformations between iterations to augment our data sets. The ML pipeline consisted of convolutional base encoder and linear projector. To test the classification task, the projector was removed and one linear layer was measured. Validation of the method consisted of 6 repeated random sub-samplings, each using a random selection of a small group of patients of both subtypes.ResultsIn the 6 trials, between 79% and 83% of the patients were correctly classified as NPSLE or non-NPSLE. For a qualitative evaluation of spatial distribution of the common features found in both groups, Gradient-weighted Class Activation Maps (Grad-CAM) were examined. Thresholded Grad-CAM maps show areas of common features identified for the NPSLE cohort, while no such communality was found for the non-NPSLE group.Discussion/ConclusionThe self-supervised contrastive learning model was effective in capturing common brain MRI features from a limited but well-defined cohort of SLE patients with NP symptoms. The interpretation of the Grad-CAM results is not straightforward, but indicates involvement of the lateral and third ventricles, periventricular white matter and basal cisterns. We believe that the common features found in the NPSLE population in this study indicate a combination of tissue loss, local atrophy and to some extent that of periventricular white matter lesions, which are commonly found in NPSLE patients and appear hypointense on T1-weighted images.
Highlights
Systemic lupus erythematosus (SLE) is a female-predominant auto-immune disease with a broad spectrum of clinical presentations and multi-organ involvement
No significant differences were found when comparing the accuracy of the classification across the three data augmentation options
We designed a self-supervised machine-learning pipeline for classification of T1-weighted MRI images aimed at distinguishing between images of NP events attributed to SLE (NPSLE) patients and those of non-NPSLE patients
Summary
Systemic lupus erythematosus (SLE) is a female-predominant auto-immune disease with a broad spectrum of clinical presentations and multi-organ involvement. The involvement of the central nervous system (CNS) in SLE leads to a series of non-specific neuropsychiatric (NP) manifestations in 12–95% of SLE patients (Ainiala et al, 2001). These NP symptoms widely range in terms of severity and prognostic implications (Schwartz et al, 2019). A study performed in our center reported that about 15% of NP events attributed to SLE (NPSLE) during the first patient evaluation were reclassified after reassessment as non-NPSLE (Magro-Checa et al, 2017). This discrepancy highlights the pressing need for biomarkers that will contribute to more reliably distinguish between NPSLE and non-NPSLE early in the diagnostic process
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