Multiple system atrophy (MSA) is a neurodegenerative disorder of unknown etiology and insidious onset, characterized primarily by autonomic dysfunction, poorly levodopa-responsive parkinsonism, cerebellar ataxia, and corticospinal tract dysfunction (1). MSA diagnosis remains challenging due to overlapping clinical manifestations with other neurodegenerative diseases and the lack of reliable biomarkers (2, 3). Epidemiological studies indicate that MSA progresses rapidly with shortened survival, underscoring the critical importance of early diagnosis for symptom management, prognosis evaluation, precision therapy development, and drug discovery (4).

Historically, MSA diagnosis relied on clinical symptoms, signs, and neuroimaging findings (5, 6). Although neuropathological examination remains the gold standard, biopsy-associated risks and patient reluctance limit its utility. Clinical diagnosis alone faces limitations due to phenotypic heterogeneity and symptom overlap across neurodegenerative disorders. Consequently, neuroimaging has been incorporated as supportive evidence in diagnostic criteria (7, 8). Previous studies identified key MRI features: in the MSA-P subtype: Hypointensity in the putamen on T2-weighted imaging (T2WI) and susceptibility-weighted imaging (SWI), with hyperintensity on T2^* sequences (9); in the MSA-C subtype: the “hot cross bun sign” (pontine cruciform hyperintensity on T2WI/FLAIR) and middle cerebellar peduncle (MCP) hyperintensity. The “hot cross bun sign” exhibits 99% specificity and 45% sensitivity in differentiating MSA-C from spinocerebellar ataxias, while MCP hyperintensity shows 99% specificity and 68% sensitivity (10). The grading of pontine “hot cross bun sign” (11) correlates positively with cerebellar ataxia severity in MSA-C. These characteristic MRI markers aid in distinguishing MSA from Parkinson's disease (PD), progressive supranuclear palsy (PSP), and sporadic late-onset ataxia, though sensitivity in early-stage disease remains suboptimal (12). While PET-CT and SPECT offer diagnostic value, high cost and radiation exposure hinder widespread clinical adoption (13, 14). Transcranial sonography further suffers from limited sensitivity and specificity (15).

In 2022, the International Movement Disorder Society updated diagnostic criteria, stratifying MSA into four tiers: Neuropathologically established, Clinically established, Clinically probable, Possible prodromal MSA (6). The same year, China released its expert consensus, aligning with international standards while incorporating regional evidence (16). This consensus explicitly mandates multimodal MRI—including T1 (axial/sagittal), T2, ADC, SWI, and T2 FLAIR sequences—as essential for diagnosis, differential evaluation, and disease monitoring. It emphasizes that precise diagnosis requires integrating clinical, imaging, and laboratory data, highlighting the need for novel methods to enhance diagnostic accuracy (12).

Despite these advances, there remains a pressing need for more sensitive and objective imaging diagnostic model. This study aims to develop optimal diagnostic model for MSA based on radiomics features derived from multimodal MRI, providing a novel and precise diagnostic tool for clinical practice.

Multiple system atrophy (MSA) is a rare disease; because samples are hard to collect, studies are often characterized by small sample sizes and high-dimensional feature spaces, which can easily lead to overfitting if not handled properly. In this study, 70% of the samples were randomly selected for feature extraction of brain regions, aiming to reduce the excessive dependence of the model on the training data, thereby reducing the risk of overfitting. The core logic of this approach, a subsampling approach, is to use a random masking mechanism similar to Dropout to form regularization to improve generalization through data-level randomness (25–27). Previous studies have shown the utility of feature screening in small, high-dimensional data such as the one used in our study (28).

To efficiently identify the most discriminative features from extensive feature pools, we performed Z-score normalization on intraclass correlation coefficient (ICC)-validated features per brain region (29), then integrated four sequences (T1, T2, T2 FLAIR, ADC) into multimodal representations (30). Subsequently, LASSO regression was applied to extract key features from each region's multimodal set (31). Least absolute shrinkage and selection operator (LASSO) regression is a linear regression method combining feature selection and regularization. The core of LASSO regression is to realize sparse modeling by introducing L1 regularization term. Lasso regression can solve the problem of high-dimensional data redundancy by compressing the coefficients of unimportant features to zero and automatically screening key variables. Only a few nonzero coefficients are retained in the generated model, which improves the interpretation of the model. L1 regularization can deal with multicollinearity problems more effectively than ridge regression (L2 regularization) (32). Due to these characteristics, Lasso regression has been widely used in radiomics. Radiomics features are often in thousands of dimensions, and Lasso can simplify model parameters by filtering out 99% redundant features from the original features (33). Features were selected using internal 10-fold cross validation in the training set by the minimum mean squared error (MSE) (22). Among the non-zero weight features obtained from lasso regression, the five features with the greatest weight influence were selected as the variables to calculate the Rad-score. Based on the weight of feature variables and regression intercept construct of LASSO regression, Rad-score construction formulas (see Supplementary material) for seven brain regions were established as biomarker (34). The combined plot shows that the rad-score of each brain region has some discrimination power.

In order to maximize the model performance, a total of seven RAD-scores from seven brain regions in each sample were combined into a new research sample for logistic regression modeling. In order to improve the generalization ability, the total 135 samples were randomly divided into training group, test group and validation group according to 6:2:2 (35). The training group was used for modeling, and the learning curve within that group was plotted. The learning curve began to converge when the training sample reached 15, and the test score and training score increased with the training sample, and tended to converge to a curve pattern with the same value, indicating that the model did not overfit, showing that the model had good generalization ability (36). Among the key indicators, only MSA in the validation group achieved a recall rate of 0.89, while the others achieved an accuracy rate and recall rate of more than 0.9, showing excellent classification functions on the validation set and the test cohorts (37). This 0.89 sensitivity highlights the model's potential as a screening tool for MSA.

In this study, we demonstrated that the radiomics-based Rad-score exhibited greater sensitivity than conventional MRI imaging markers currently incorporated in diagnostic criteria for multiple system atrophy (MSA). The seven brain regions delineated in this study are the MRI imaging biomarkers mentioned in the diagnostic criteria of MSA, and can serve as on basis for clinical diagnosis of MSA (6). Although MRI abnormalities in MSA patients have high specificity, their sensitivity is usually low. Moreover, the clinical utility of these MRI findings in improving diagnostic accuracy remains to be fully elucidated (38). All cases included in this study were diagnosed as clinically probable multiple system atrophy (MSA) due to the absence of characteristic MRI findings. For further validation, 135 samples were evaluated by two radiologists with more than 10 years of experience. The AUCs for Radiologist A and Radioligist B were 0.559 and 0.535, and the kappa coefficient of agreement between them was 0.152. These results demonstrate that macroscopic MRI features alone were insufficient for accurate diagnosis in this study. In contrast, the diagnostic model based on the Rad-score derived from seven brain regions showed excellent classification performance, supporting its practical utility.

The weights of the RADscores of these seven brain regions in the model found in this study are also consistent with the laterality characteristics of MSA found by other research methods. The SNAP diagram of the Logistic regression model shows that among the seven brain regions, the influence weights are ranked as follows: left putamen > right putamen > right medipeduncle > left cerebellar > right cerebellar > pons > left medipeduncle. The results showed that the influence of the left putamen was greater than that of the right medipeduncle, and the influence of the left cerebellar hemisphere was greater than that of the right cerebellar hemisphere. These findings may be related to the pathogenesis of MSA. There is also an important tendency of hemisphere lateralization in the process of PD. Therefore, PD is considered an inherently asymmetric disease in clinical practice. This clinical asymmetry is associated with more severe contralateral nigrostriatal degeneration (39). Some studies have shown a “left hemisphere susceptibility” in this condition, as the left nigrostriatal pathway is more affected than the right (40). Previous PET imaging studies based on altered ¹⁸F-DOPA uptake have confirmed that the loss of ¹⁸F-DOPA uptake rate in the nigrostriatal system in selected populations of drug-naive Parkinson's disease cohorts is predominantly on the most affected side, so that the left hemisphere image depicts the more affected side. While the less affected side (LAS) corresponds to the right hemisphere, the reduced topography was mainly in the putamen of the left hemisphere with maximum uptake loss in the anterior-posterior axis and dorsoventral axis, respectively (41). In the study by Van Laere and colleagues, left putamen uptake was observed in 24 of 38 patients (63.1%) with right-sided predominant disease (P < 0.001), indicating that this laterality is also present in IPD such as MSA (42). The dopaminergic system is thought to be primarily responsible for this lateralization due to its critical role in motor control. Inherent interhemispheric imbalances in nigrostriatal dopamine (DA) levels in humans and animals have been shown to be associated with lateralization of motor behavior (43). This change can cause the corresponding changes in the images of the putamen. Although such changes cannot be detected in the macroscopic image features, the RADscore constructed by radiomics can accurately detect the changes in the left and right putamen. In the MSA group in the present study, the changes of the left putamen were greater than those of the right putamen, which is consistent with previous studies.

Minori Furuta et al. found that MSA patient exhibited laterality changes in the middle cerebellar peduncle on SPECT (44). While conventional MRI failed to reveal these alterations, radiomics captured them and confirmed laterality patterns reported previously. Similarly, Francesca Caso observed atrophy of the left cerebellar hemisphere but not the right cerebellar hemisphere in patients with MSA-P by 1.5T magnetic resonance imaging, suggesting that atrophy of the left cerebellar hemisphere may be more easily observed at the macroscopic level than that of the right (45). This study found that the effect of the left cerebellar hemisphere is also called right hemisphere enlargement, which is consistent with this. These results are consistent with the laterality of previous studies, and further support the proposed RADscore as a biomrker to not only preferentially screen out highly suspected MSA cases. In addition, Eun Hye Jeong et al. found through ¹²³I-FP-CIT SPECT study that the asymmetry of putamen was more obvious in the early stage of the disease, and this asymmetry decreased with the extension of follow-up time (46). The patients in this study belonged to the early stage of the disease when none of the macroscopic imaging markers required by the guidelines were found, so the RADscore difference of the putamen was more significant. The Rad-score may serve as a potential biomarker for the early diagnosis of multiple system atrophy (MSA). The diagnostic model based on the Rad-score demonstrates promising diagnostic performance in identifying MSA cases.

In conclusion, for patients with clinically suspected multiple system atrophy (MSA) but lacking definitive MRI markers, the radiomics-based RAD score offers a sensitive imaging biomarker that enables the construction of a diagnostic model capable of distinguishing MSA from healthy controls and improving overall diagnostic accuracy.

This study has several limitations. First, it was a single-center retrospective analysis, which may limit the generalizability of the findings to broader or more diverse populations. Second, although we included patients with clinically probable MSA and healthy controls, the diagnosis was primarily based on clinical criteria, which may introduce selection bias. Third, the radiomics model was built using manually delineated regions of interest (ROIs), and thus may be subject to inter- and intra-observer variability; future studies incorporating automated segmentation techniques are warranted. Finally, external validation using an independent cohort is needed to further confirm the robustness and clinical applicability of the RAD score as a diagnostic biomarker.