Radiomic Analysis of Nigrosome-1 on 3.0T Susceptibility-Weighted Imaging for Characterizing Asymmetric Degeneration in Parkinson’s Disease

Radiomic Analysis of Nigrosome-1 on 3.0T Susceptibility-Weighted Imaging for Characterizing Asymmetric Degeneration in Parkinson’s Disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Radiomic Analysis of Nigrosome-1 on 3.0T Susceptibility-Weighted Imaging for Characterizing Asymmetric Degeneration in Parkinson’s Disease Yuan Liu, Shize Li, Jue Wang, Jinyong Zhan, Kaiying Xu, Saijun Chen, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9436986/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Nigrosome-1 (N1) is one of the earliest regions affected in Parkinson's disease (PD) and can be visualized on 3.0-T susceptibility-weighted imaging (SWI) as the swallow tail sign (STS). This study investigated the associations of visual STS assessment and N1-derived radiomic features with clinical characteristics in PD. Methods In this retrospective study, 83 patients with PD who underwent 3.0-T SWI and clinical evaluation were included. Patients were categorized according to STS visualization as having total absence or non-total absence of dorsolateral nigral hyperintensity. The N1 region was manually segmented on SWI, and 107 radiomic features were extracted. Least bsolute shrinkage and selection operator (LASSO) regression was applied for radiomic feature selection. Based on the selected features, three machine learning classifiers—LASSO-logistic regression (LASSO LR), the random forest, and a linear support vector machine (linear SVM)—were constructed and compared. Results STS visualization was significantly associated with clinical severity indicators, including Hoehn-Yahr (H-Y) stage, MDS-UPDRS II score, rigidity score, and plasma iron level (P < 0.05). Visual STS assessment showed significant side-to-side differences in the overall PD cohort and in early-stage PD, whereas no significant difference was observed in advanced PD. Among the tested classifiers, the random forest model showed the best performance for differentiating the more-affected from the less-affected side in advanced PD (AUC = 0.896, 95% CI: 0.721–0.933), while all models showed limited performance in the overall cohort and in early-stage PD (all AUC < 0.7). Additionally, radiomics models showed limited ability to distinguish motor subtype and disease stage (all AUC < 0.7). Conclusion Visual STS assessment and N1 radiomics may provide complementary information on nigral degeneration in PD. Visual assessment appears to be more informative for detecting asymmetry in early-stage PD, whereas N1 radiomics may better characterize asymmetric degeneration in advanced PD. parkinson’s disease nigrosome-1 susceptibility-weighted imaging radiomic swallow tail sign Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Parkinson's disease (PD) is a common neurodegenerative disorder in middle-aged and older adults and is clinically characterized by both motor and non-motor symptoms [ 1 ]. Loss of dopaminergic neurons and iron accumulation in the substantia nigra (SN) are among the principal pathological features of PD [ 2 ]. Histological studies based on calbindin D28k staining have identified subregions within the SN that lack this protein, known as nigrosomes [ 3 , 4 ]. Among these, nigrosome-1 (N1) is the largest and most severely affected subregion in terms of dopaminergic neuronal loss, making it a particularly important imaging target in PD [ 5 ]. Previous studies have shown that 3.0-T susceptibility-weighted imaging (SWI) enables visualization of N1 as a dorsolateral hyperintense structure within the SN, producing the characteristic "swallow tail" sign (STS) in healthy individuals; this sign is typically absent in PD [ 6 – 8 ]. Visual assessment of the STS has therefore been widely used in the imaging evaluation and differential diagnosis of PD. The absence of the STS has been proposed as a promising imaging marker for PD [ 9 , 10 ]. However, visual evaluation of the STS is influenced by image quality, acquisition parameters, and inter-reader variability. In addition, there is still no universally standardized criterion for identifying the N1 sign, which may reduce its reproducibility and clinical utility [ 7 , 11 ]. Quantitative analysis of the N1 region may help address these limitations by providing objective imaging biomarkers of nigral degeneration [ 7 , 12 , 13 ]. Radiomics, an emerging field in medical image analysis, enables the extraction of a large number of quantitative imaging features from regions of interest (ROIs) [ 14 – 16 ]. These features generally include first-order statistical features, shape features, texture features, and higher-order features, and may capture tissue heterogeneity beyond what is appreciable by visual inspection .Despite increasing interest in N1 imaging in PD, quantitative radiomic analysis of the N1 region on 3.0-T SWI remains limited [ 17 – 20 ], particularly in the context of clinical asymmetry. Most previous imaging studies have focused on the diagnostic value of the STS or on differentiating PD from healthy controls and atypical parkinsonian syndromes, whereas the potential roles of visual STS assessment and N1-derived radiomics in characterizing lateralized degeneration and clinical heterogeneity within PD have been less well investigated. Therefore, the present study aimed to evaluate the associations of visual STS assessment and N1 radiomic features with clinical characteristics in patients with PD, and to explore their value in identifying asymmetric nigral degeneration, motor subtypes, and disease stage. Materials and Methods Study Subjects A total of 93 patients with PD were recruited at our hospital between September 2023 and July 2025. The diagnosis of PD was confirmed by a movement disorder specialist according to the UK Brain Bank clinical diagnostic criteria. All patients underwent 3.0-T SWI and standardized clinical data collection, including sex, age, age at onset, disease duration, and PD-related scale assessments.The exclusion criteria were as follows: (1) secondary or atypical parkinsonism; and (2) factors affecting MRI quality, including metal implants, claustrophobia, or severe motion artifacts. The more-affected and less-affected sides were determined according to the MDS-UPDRS Part III scores. The side contralateral to the limb with the higher score was defined as the more-affected side of the N1 region, and the side contralateral to the limb with the lower score was defined as the less-affected side.According to the H-Y stage, patients were classified as early stage (≤ 2.5) or middle-to-late stage (> 2.5).Tremor and postural instability/gait difficulty (PIGD) scores were derived from items in Parts II and III of the MDS-UPDRS. Motor subtypes were classified according to the ratio of the PIGD score to the tremor score: a ratio > = 1.5 defined the tremor-dominant (TD) subtype, whereas a ratio < 1.5 defined the PIGD subtype. Imaging Acquisition All participants underwent 3.0 T SWI on a Philips Ingenia Elition X MRI system. Participants were instructed to remain as still as possible during the examination. The imaging parameters were as follows: repetition time (TR) = 51 ms; echo time (TE) = 0 ms; TE1 = 9.8 ms; ΔTE = 6.8 ms; number of echoes = 6; flip angle (FA) = 20°; field of view (FOV) = 230 × 230 mm²; voxel size = 0.7 × 0.7 × 1.0 mm³; slice thickness = 2 mm; number of slices = 130; slice gap = − 1 mm; and acquisition time = 3 min 2 s. All images were reviewed on the picture archiving and communication system (PACS). Assessment of STS Visualization The diagnostic criteria for the “swallow tail” sign were based on previous studies. In healthy individuals, dorsolateral nigral hyperintensity is typically visible and may appear droplet‑shaped, linear, or comma‑shaped, whereas it is usually absent in patients with PD. Two physicians independently evaluated the presence or absence of the bilateral STS slice by slice within the SN, blinded to all clinical information. Based on their evaluations, subjects were categorized into a total absence group and a non-total absence group (Fig. 1 ). The total absence group was defined as complete absence of dorsolateral nigral hyperintensity on both sides, whereas the non-total absence group was defined as the presence of dorsolateral nigral hyperintensity on at least one side. Inter‑rater agreement was excellent (k = 0.970, P = 0.001). Region Of Interest Delineation and Image Analysis The DICOM data from SWI were imported into 3D Slicer (version 5.4.0). The N1 region, typically located at or below the caudal portion of the red nucleus (RN) [ 21 ], was defined as the ROI. A researcher blinded to all clinical information manually segmented the relevant slices from the lower part of the RN to the lower part of the SN. To minimize partial volume effects, the most inferior part and boundary regions of the SN were excluded. Finally, two volumes of interest (VOIs) were generated (Fig. 2 ). Radiomic Feature Extraction and Models Construction A total of 107 radiomic features were extracted from the manually segmented N1 region, including 18 first-order features, 14 shape features, and 75 texture features. LASSO regression was used to rank features and identify the top 20 predictors for sparse linear modeling after z-score normalization. LASSO logistic regression and linear SVM were evaluated using the reduced feature subset, whereas the final random forest model for asymmetry classification was evaluated using the full radiomic feature set. Three supervised machine-learning classifiers - LASSO LR, linear SVM, and random forest - were constructed for three classification tasks: (1) differentiating the more-affected side from the less-affected side, (2) classifying motor subtypes, and (3) distinguishing disease stage. Model performance was assessed by five-fold cross-validation and ROC analysis, with AUCs and 95% CIs reported. Models selection was based primarily on cross-validated AUC, whereas calibration and decision curve analysis were used as supplementary assessments of models reliability and potential clinical utility. Statistical Analysis Categorical variables were compared using the chi-square test. Continuous variables were analyzed using the independent-samples t test or the Mann-Whitney U test, depending on data distribution. Multivariable logistic regression analyses were conducted to control for potential confounding factors between groups. The McNemar test was used to evaluate the association between STS findings and the more-affected versus less-affected sides. Models performance was assessed and visualized using ROC curves. All statistical analyses were performed using Python (version 3.14.0) and SPSS statistical software (version 26.0). Statistical significance was defined as P < 0.05. Results 1. Assessment of STS Visualization A total of 93 patients with PD who completed 3.0 T SWI and clinical evaluation were initially enrolled. After excluding six patients with motion artifacts and four patients with data abnormalities, 83 patients were ultimately included in the final analysis. Patients were categorized into the total absence group and the non-total absence group according to STS visualization on SWI. Significant differences were observed between the STS visualization groups in H-Y stage, MDS-UPDRS II score, rigidity score, and plasma iron level (P<0.05) (Table 1). In addition, visual assessment of the STS demonstrated significant asymmetry between the more-affected side and the less-affected side in the overall PD cohort and in patients with early-stage PD. However, this side-to-side difference was not significant in patients with advanced PD (Fig. 3). 2. Radiomics Models for Differentiating the More-Affected and Less-Affected Sides in PD In the overall PD cohort, the N1 radiomics models showed limited discriminative performance for distinguishing the more-affected side from the less-affected side (the highest AUC=0.694). All models also showed limited performance in the early-stage PD subgroup (the highest AUC=0.703). However, in advanced PD, the random forest model achieved the highest performance among the three classifiers, with a cross-validated AUC of 0.896 (95% CI: 0.721-0.933) (Fig. 4A). The LASSO-derived top-20 subset was used primarily for sparse linear modeling and feature-ranking interpretation. SHAP analysis of the full-feature random forest model further demonstrated that both shape- and texture-based features contributed to the discrimination of the more-affected and less-affected sides in advanced PD (Fig. 4B). Sphericity showed the highest mean absolute SHAP value, followed by Imc2, Correlation, SizeZoneNonUniformityNormalized, ClusterShade, and Skewness. In addition, Calibration analysis demonstrated good agreement between predicted and observed outcomes, with a Brier score of 0.183.ROC curves were generated from out-of-fold predictions obtained by five-fold stratified cross-validation. 3. Radiomics Models for Predicting Motor Subtypes in PD Radiomics models based on N1 features from either the less-affected side or the more-affected side demonstrated limited performance in predicting motor subtypes (all AUC < 0.7) (Fig. 5). 4. Radiomics Models for Differentiating Disease Stages in PD Radiomics models based on N1 features from either the less-affected side or the more-affected side demonstrated limited performance in distinguishing early-stage PD from advanced PD (all AUC < 0.7) (Fig. 5). Discussion To the best of our knowledge, radiomic analysis of the N1 region on 3.0 T SWI has been only rarely explored in PD, particularly in relation to clinical asymmetry. Most previous radiomics studies have focused on distinguishing PD from healthy controls or from atypical parkinsonian syndromes. In this study, we investigated the clinical relevance of STS visualization and N1-derived radiomic features on 3.0 T SWI in PD. Three main findings emerged. First, STS visualization groups were significantly associated with disease severity, including H–Y stage, MDS-UPDRS II score, rigidity scores, and plasma iron levels. Second, visual assessment of the STS showed significant side-to-side differences in the overall PD cohort and in early-stage PD, but not in advanced PD. Third, radiomic features derived from N1 showed the best performance in differentiating the more-affected side from the less-affected side in advanced PD, whereas their performance was limited in the overall cohort and in early-stage PD. Together, these findings suggest that visual assessment and radiomics may capture complementary aspects of nigral degeneration at different stages of PD. The observed association between STS visualization and clinical severity supports the relevance of dorsolateral nigral hyperintensity absence as an imaging marker of disease burden. As N1 is one of the earliest and most severely affected regions within the SN in PD, disappearance of the STS on SWI may reflect progressive dopaminergic neuronal loss and susceptibility-related alterations. The association with plasma iron levels further raises the possibility that altered iron metabolism contributes to N1-related imaging abnormalities. Although peripheral iron indices cannot directly reflect local brain iron content, the present finding is in line with the hypothesis that iron dysregulation contributes to the pathophysiology of nigral degeneration in PD, lending biological plausibility to this observation[ 22 ]. A key finding of this study is the stage-dependent relationship between visual STS assessment and radiomic analysis in evaluating disease asymmetry. In the overall cohort and particularly in early-stage PD, visual STS assessment demonstrated significant side-to-side differences between the more-affected and less-affected sides. This suggests that in earlier disease stages, when nigral degeneration remains relatively lateralized, qualitative visual evaluation may still be sufficient to detect asymmetric involvement of N1. One possible explanation is that the hemisphere corresponding to more severe motor symptoms may show earlier or more prominent loss of dorsolateral nigral hyperintensity, allowing visual asymmetry to remain appreciable on routine SWI. This interpretation is supported by recent MRI findings indicating that hemispheric neuromelanin-iron dysfunction within the SN is associated with lateralized motor onset in early-stage PD[ 23 ]. In contrast, visual STS asymmetry was no longer significant in advanced PD, whereas N1 radiomics showed substantially improved performance for differentiating the more-affected side from the less-affected side, with the highest AUC observed in this subgroup. In early-stage PD, interhemispheric differences within N1 may still be relatively subtle, resulting in substantial overlap of radiomic features between the more-affected and less-affected sides and thereby limiting discriminative performance. This pattern suggests that, as PD progresses and bilateral nigral degeneration becomes increasingly widespread, the ability of qualitative visual assessment to discriminate disease-related changes may be reduced. In this context, radiomic analysis may provide additional sensitivity by capturing subtle intranigral heterogeneity beyond the resolution of routine visual inspection. Importantly, recent studies have shown that higher R2* and quantitative susceptibility values in SN are associated with both motor severity and motor asymmetry, supporting the view that clinically relevant interhemispheric differences may persist even when gross bilateral STS loss reduces visual contrast[ 24 ]. The texture-related features identified in the model, including skewness, kurtosis, maximal correlation coefficient, and gray-level non-uniformity, may reflect increasingly complex and asymmetric microstructural alterations within the nigrosome in advanced disease. Importantly, the differing performance of visual STS assessment and radiomics should not be interpreted as contradictory. Rather, the two approaches appear to offer complementary information at different stages of PD. Visual STS assessment may be more informative in early-stage PD, when asymmetry remains visually recognizable, whereas N1 radiomics may better capture residual quantitative heterogeneity in advanced PD, when bilateral STS loss reduces the sensitivity of qualitative evaluation. From a clinical perspective, this stage-specific pattern supports the potential utility of combining qualitative and quantitative N1 assessment to improve the imaging evaluation of asymmetric nigral degeneration in PD. By comparison, the performance of N1 radiomics for motor subtype prediction was limited. These findings suggest that imaging features derived from a single nigral subregion may not adequately capture the broader biological complexity underlying motor phenotype and disease progression. Motor subtype is likely influenced by distributed network-level dysfunction and extranigral pathology. This interpretation is supported by recent studies showing that motor subtype discrimination is enhanced when multimodal MRI features or cross-regional radiomic interactions within motor-circuit structures are considered, rather than focusing on a single nigral ROI[ 25 , 26 ]. Accordingly, although N1 radiomics may be useful for assessing asymmetric nigral involvement, its value for broader phenotypic classification appears more restricted. Furthermore, the limited performance of N1 radiomics for differentiating early-stage from advanced PD may have several explanations. First, PD progression extends beyond the N1 region, and a single-region radiomics model may therefore be insufficient to capture the overall neurodegenerative burden. Second, susceptibility-related and structural changes within N1 may occur relatively early in the disease course, reducing their incremental discriminative value once degeneration is already established. Third, clinical staging in PD does not necessarily show a linear relationship with localized imaging alterations, because disease severity is influenced by multiple pathological processes beyond the nigrostriatal system. In addition, recent radiomics work suggests that MRI-derived radiomic signatures may be more informative for predicting subsequent motor progression than for coarse cross-sectional stage stratification, which may further explain why N1-based models performed better for asymmetry analysis than for stage classification in the present dataset[ 27 ]. Taken together, these observations indicate that N1 radiomics alone is unlikely to serve as a robust marker of overall disease stage. Finally, the present findings may also have pathophysiological implications. Given the sensitivity of SWI to susceptibility-related changes and the ability of radiomic features to characterize intensity distribution and textural heterogeneity within N1, our results indirectly support the notion that iron-related microstructural alterations contribute to the imaging phenotype of PD. The significant association between STS visualization and plasma iron levels further supports this interpretation, although the biological relationship between peripheral iron markers and regional brain susceptibility changes warrants further investigation. Overall, our data suggest that quantitative analysis of N1 may provide imaging information beyond that available from conventional visual assessment alone. Limitations Several limitations of this study should be acknowledged. First, this was a retrospective single-center study with a relatively small sample size, particularly for subgroup analyses, which may limit statistical power and increase the risk of overfitting. Second, the N1 region was manually segmented, which may have introduced observer-dependent variability. Recent work indicates that more standardized localization and segmentation strategies may improve reproducibility in future studies[ 28 , 29 ].Third, the absence of an external validation cohort limits the generalizability and robustness of the radiomics models. Fourth, only SWI-based radiomic features from the N1 region were analyzed, whereas PD is a multisystem disorder involving widespread nigrostriatal and extranigral abnormalities. Fifth, preprocessing and harmonization choices can substantially affect feature reproducibility and classification performance in MRI radiomics, and this issue should be explicitly addressed in future multicenter studies[ 30 ]. Finally, although the present findings suggest stage-dependent complementary roles of visual STS assessment and N1 radiomics, the current results should be interpreted as exploratory and require confirmation in larger prospective multicenter studies. Future work incorporating multimodal MRI, additional nigral and extranigral regions, harmonization-aware radiomics workflows, and independent validation cohorts may improve both biological interpretability and predictive performance. Conclusion In summary, visual STS assessment and N1 radiomics provide complementary information on nigral degeneration in PD. Visual assessment of the STS appears to be more informative for detecting lateralized nigral changes in early-stage PD, whereas N1 radiomics may better characterize asymmetric degeneration in advanced PD. The present findings support the potential value of combining qualitative STS evaluation with quantitative N1 radiomic analysis to improve the imaging assessment of disease asymmetry in PD. Declarations Author Contribution Conceptualization: Yuan Liu, Shize Li, Zhifei Ben, Saijun ChenData curation: Yuan Liu, Shize Li, Jue WangFormal analysis: Yuan Liu, Jinyong Zhan, Kaiying XuInvestigation: Yuan Liu, Shize Li, Jue WangMethodology: Yuan Liu, Shize Li, Zhifei BenProject administration: Saijun Chen, Zhifei BenResources: Saijun Chen, Zhifei BenSoftware: Shize Li, Yuan LiuSupervision: Saijun Chen, Zhifei BenValidation: Yuan Liu, Shize LiVisualization: Yuan Liu, Shize LiWriting – original draft: Yuan Liu, Shize LiWriting – review & editing: All authorsAll authors have read and approved the final version of the manuscript.Conflicts of Interest: The authors declare no conflicts of interest. Yuan Liu and Shize Li contributed equally to this work and should be regarded as co-first authors.Saijun Chen and Zhifei Ben contributed equally to this work and should be regarded as co-corresponding authors. 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Comparison of clinical characteristics among STS visualization groups Characteristics Total absence group (n=65) Non-total absence group (n=18) P Value Female/male 31/34 7/11 0.507 Age of onset 62.00(58.00,66.00) 61.00(56.25,65.00) 0.514 Disease duration 5.00(3.00,8.75) 3.00(2.00,6.50) 0.131 H-Y 2.50(2.00,3.00) 2.00(1.50,2.38) 0.026* Total MDS-UPDRS 77.40±35.04 67.72±36.01 0.306 MDS-UPDRS I 12.78±5.57 13.00±7.96 0.896 MDS-UPDRS II 15.00(12.00,22.75) 9.00(5.25,15.00) 0.013* MDS-UPDRS III 45.49±21.97 39.83±20.73 0.331 Tremor scores 6.00(1.25,10.00) 7.50(5.25,12.00) 0.278 Rigidity scores 6.00(3.00,9.00) 3.50(2.00,6.00) 0.038* NMSS 33.00(23.25,64.00) 39.50(19.00,78.50) 0.991 MMSE 27.00(22.00,29.00) 27.50(22.25,29.00) 0.982 HAMD 8.50(5.25,12.75) 10.50(6.25,19.75) 0.355 HAMA 10.50(7.25,16.00) 11.00(5.00,17.00) 0.517 Ceruloplasmin 26.45(24.03,31.08) 26.80(23.00,32.00) 0.898 plasma levels of iron 8.18(7.86,8.77) 8.60(8.41,8.85) 0.041* H-Y, Hoehn and Yahr stage; MDS-UPDRS, Movement Disorder Society–Unified Parkinson’s Disease Rating Scale; MDS-UPDRS I, Part I: Non-Motor Experiences of Daily Living; MDS-UPDRS II, Part II: Motor Experiences of Daily Living; MDS-UPDRS III, Part III: Motor Examination; NMSS, Non-Motor Symptoms Scale; MMSE, Mini-Mental State Examination. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9436986","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":633755279,"identity":"cc1d3797-4d00-4e44-a57b-77691b24b2c6","order_by":0,"name":"Yuan Liu","email":"","orcid":"","institution":"Ningbo No. 2 Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Liu","suffix":""},{"id":633755280,"identity":"5a0d0cea-beb7-46a6-a24e-a3250a7bb351","order_by":1,"name":"Shize Li","email":"","orcid":"","institution":"Ningbo No. 2 Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shize","middleName":"","lastName":"Li","suffix":""},{"id":633755281,"identity":"dd45d6b2-2118-4e71-975d-d47b444a6347","order_by":2,"name":"Jue Wang","email":"","orcid":"","institution":"Ningbo No. 2 Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jue","middleName":"","lastName":"Wang","suffix":""},{"id":633755282,"identity":"f9cc4f81-d5c8-4640-9e7b-8e3d67d07881","order_by":3,"name":"Jinyong Zhan","email":"","orcid":"","institution":"Ningbo No. 2 Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jinyong","middleName":"","lastName":"Zhan","suffix":""},{"id":633755283,"identity":"1439205a-ac4b-4ccd-83b1-b215f61fa864","order_by":4,"name":"Kaiying Xu","email":"","orcid":"","institution":"Ningbo No. 2 Hospital","correspondingAuthor":false,"prefix":"","firstName":"Kaiying","middleName":"","lastName":"Xu","suffix":""},{"id":633755284,"identity":"84bbe047-b098-4ea8-ae7d-d7aa625c5aff","order_by":5,"name":"Saijun Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAo0lEQVRIiWNgGAWjYJCCAxIMDHKkazEm3abEBqKV8vcffnjAsu1Oet/xBMYPH3OI0CJxI83ggGTbs9yZZx4wS87cRoQWAwkGkJbDuRtuJLAx8xKlhf/4B5CWdAPitTDkgG1JIF6LxI2cggMS5w4bzjzzsJk4v/D3H9/8WaLssDzf8eSDHz4SowUEmCVA5AESooaB8QNYSwLxOkbBKBgFo2BkAQCZTjsxk+uSXwAAAABJRU5ErkJggg==","orcid":"","institution":"Ningbo No. 2 Hospital","correspondingAuthor":true,"prefix":"","firstName":"Saijun","middleName":"","lastName":"Chen","suffix":""},{"id":633755285,"identity":"22746a9a-91ee-4230-a438-bbb827425df7","order_by":6,"name":"Zhifei Ben","email":"","orcid":"","institution":"Ningbo No. 2 Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhifei","middleName":"","lastName":"Ben","suffix":""}],"badges":[],"createdAt":"2026-04-16 10:38:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9436986/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9436986/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108735256,"identity":"5375a1ea-0a6d-4896-8f9d-e9c47e871396","added_by":"auto","created_at":"2026-05-07 20:02:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":364235,"visible":true,"origin":"","legend":"\u003cp\u003eSWI images from patients with PD demonstrating different patterns of dorsolateral nigral hyperintensity. (a) and (d) show unilateral dorsolateral nigral hyperintensity, with (a) representing a magnified view of (d). (c) shows bilateral absence of dorsolateral nigral hyperintensity, and (b) and (e) show bilateral presence of dorsolateral nigral hyperintensity, with (b) representing a magnified view of (e).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9436986/v1/4f3d4efb1b076a6ab763c121.png"},{"id":108806138,"identity":"8d2651a9-1bab-46b4-a9ef-928762a4976f","added_by":"auto","created_at":"2026-05-08 15:27:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":586387,"visible":true,"origin":"","legend":"\u003cp\u003eSegmentation of N-1. (A) and (B) show the initial slice below the red nucleus (RN). (C) and (D) illustrate the procedure for three-dimensional reconstruction of N-1. (E) and (F) show the most inferior slice of the SN.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9436986/v1/deaad2f0cb2f2c0f44fd6c82.png"},{"id":108735258,"identity":"91a4cf87-9a94-4efb-b398-0204fd9e4a13","added_by":"auto","created_at":"2026-05-07 20:02:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":43869,"visible":true,"origin":"","legend":"\u003cp\u003eSide-to-side differences in STS visualization across the overall, early-stage, and advanced PD cohorts.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9436986/v1/bd9add39141b9cd3c9c04aec.png"},{"id":108807661,"identity":"e0fb5e8d-ca12-4355-8a89-975eb1399529","added_by":"auto","created_at":"2026-05-08 15:31:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":131708,"visible":true,"origin":"","legend":"\u003cp\u003e(A–C) Receiver operating Characteristic (ROC) curve analyses for distinguishing the more affected side from the less affected side in the overall PD cohort, the early-stage PD subgroup, and the advanced PD subgroup; (D)Ranking of the top 20 most important features.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9436986/v1/8644488e2592324d84824ee5.png"},{"id":108735260,"identity":"ca405faa-fa3c-4824-8d96-d0b884fbd42e","added_by":"auto","created_at":"2026-05-07 20:02:19","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":90013,"visible":true,"origin":"","legend":"\u003cp\u003e(A, B) ROC curve analyses for predicting motor subtypes based on N1 features derived from the less affected and more affected sides. (C, D) ROC curve analyses for distinguishing early-stage PD from advanced PD based on N1 features derived from the less affected and more affected sides.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9436986/v1/fe8067ac07c0ea65e48921e9.png"},{"id":108809994,"identity":"dee0aab1-69e4-4c63-9e5f-683a159ecc10","added_by":"auto","created_at":"2026-05-08 15:56:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1491874,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9436986/v1/9d87a617-952f-4b95-8383-90451106efe0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Radiomic Analysis of Nigrosome-1 on 3.0T Susceptibility-Weighted Imaging for Characterizing Asymmetric Degeneration in Parkinson’s Disease","fulltext":[{"header":"Introduction","content":"\u003cp\u003eParkinson's disease (PD) is a common neurodegenerative disorder in middle-aged and older adults and is clinically characterized by both motor and non-motor symptoms [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Loss of dopaminergic neurons and iron accumulation in the substantia nigra (SN) are among the principal pathological features of PD [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Histological studies based on calbindin D28k staining have identified subregions within the SN that lack this protein, known as nigrosomes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Among these, nigrosome-1 (N1) is the largest and most severely affected subregion in terms of dopaminergic neuronal loss, making it a particularly important imaging target in PD [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious studies have shown that 3.0-T susceptibility-weighted imaging (SWI) enables visualization of N1 as a dorsolateral hyperintense structure within the SN, producing the characteristic \"swallow tail\" sign (STS) in healthy individuals; this sign is typically absent in PD [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Visual assessment of the STS has therefore been widely used in the imaging evaluation and differential diagnosis of PD. The absence of the STS has been proposed as a promising imaging marker for PD [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, visual evaluation of the STS is influenced by image quality, acquisition parameters, and inter-reader variability. In addition, there is still no universally standardized criterion for identifying the N1 sign, which may reduce its reproducibility and clinical utility [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eQuantitative analysis of the N1 region may help address these limitations by providing objective imaging biomarkers of nigral degeneration [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Radiomics, an emerging field in medical image analysis, enables the extraction of a large number of quantitative imaging features from regions of interest (ROIs) [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These features generally include first-order statistical features, shape features, texture features, and higher-order features, and may capture tissue heterogeneity beyond what is appreciable by visual inspection .Despite increasing interest in N1 imaging in PD, quantitative radiomic analysis of the N1 region on 3.0-T SWI remains limited [\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], particularly in the context of clinical asymmetry. Most previous imaging studies have focused on the diagnostic value of the STS or on differentiating PD from healthy controls and atypical parkinsonian syndromes, whereas the potential roles of visual STS assessment and N1-derived radiomics in characterizing lateralized degeneration and clinical heterogeneity within PD have been less well investigated.\u003c/p\u003e \u003cp\u003eTherefore, the present study aimed to evaluate the associations of visual STS assessment and N1 radiomic features with clinical characteristics in patients with PD, and to explore their value in identifying asymmetric nigral degeneration, motor subtypes, and disease stage.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Subjects\u003c/h2\u003e \u003cp\u003eA total of 93 patients with PD were recruited at our hospital between September 2023 and July 2025. The diagnosis of PD was confirmed by a movement disorder specialist according to the UK Brain Bank clinical diagnostic criteria. All patients underwent 3.0-T SWI and standardized clinical data collection, including sex, age, age at onset, disease duration, and PD-related scale assessments.The exclusion criteria were as follows: (1) secondary or atypical parkinsonism; and (2) factors affecting MRI quality, including metal implants, claustrophobia, or severe motion artifacts.\u003c/p\u003e \u003cp\u003eThe more-affected and less-affected sides were determined according to the MDS-UPDRS Part III scores. The side contralateral to the limb with the higher score was defined as the more-affected side of the N1 region, and the side contralateral to the limb with the lower score was defined as the less-affected side.According to the H-Y stage, patients were classified as early stage (\u0026le;\u0026thinsp;2.5) or middle-to-late stage (\u0026gt;\u0026thinsp;2.5).Tremor and postural instability/gait difficulty (PIGD) scores were derived from items in Parts II and III of the MDS-UPDRS. Motor subtypes were classified according to the ratio of the PIGD score to the tremor score: a ratio\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;1.5 defined the tremor-dominant (TD) subtype, whereas a ratio\u0026thinsp;\u0026lt;\u0026thinsp;1.5 defined the PIGD subtype.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eImaging Acquisition\u003c/h3\u003e\n\u003cp\u003eAll participants underwent 3.0 T SWI on a Philips Ingenia Elition X MRI system. Participants were instructed to remain as still as possible during the examination. The imaging parameters were as follows: repetition time (TR)\u0026thinsp;=\u0026thinsp;51 ms; echo time (TE)\u0026thinsp;=\u0026thinsp;0 ms; TE1\u0026thinsp;=\u0026thinsp;9.8 ms; ΔTE\u0026thinsp;=\u0026thinsp;6.8 ms; number of echoes\u0026thinsp;=\u0026thinsp;6; flip angle (FA)\u0026thinsp;=\u0026thinsp;20\u0026deg;; field of view (FOV)\u0026thinsp;=\u0026thinsp;230 \u0026times; 230 mm\u0026sup2;; voxel size\u0026thinsp;=\u0026thinsp;0.7 \u0026times; 0.7 \u0026times; 1.0 mm\u0026sup3;; slice thickness\u0026thinsp;=\u0026thinsp;2 mm; number of slices\u0026thinsp;=\u0026thinsp;130; slice gap\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1 mm; and acquisition time\u0026thinsp;=\u0026thinsp;3 min 2 s. All images were reviewed on the picture archiving and communication system (PACS).\u003c/p\u003e\n\u003ch3\u003eAssessment of STS Visualization\u003c/h3\u003e\n\u003cp\u003eThe diagnostic criteria for the \u0026ldquo;swallow tail\u0026rdquo; sign were based on previous studies. In healthy individuals, dorsolateral nigral hyperintensity is typically visible and may appear droplet‑shaped, linear, or comma‑shaped, whereas it is usually absent in patients with PD. Two physicians independently evaluated the presence or absence of the bilateral STS slice by slice within the SN, blinded to all clinical information. Based on their evaluations, subjects were categorized into a total absence group and a non-total absence group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The total absence group was defined as complete absence of dorsolateral nigral hyperintensity on both sides, whereas the non-total absence group was defined as the presence of dorsolateral nigral hyperintensity on at least one side. Inter‑rater agreement was excellent (k\u0026thinsp;=\u0026thinsp;0.970, P\u0026thinsp;=\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eRegion Of Interest Delineation and Image Analysis\u003c/h3\u003e\n\u003cp\u003eThe DICOM data from SWI were imported into 3D Slicer (version 5.4.0). The N1 region, typically located at or below the caudal portion of the red nucleus (RN) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], was defined as the ROI. A researcher blinded to all clinical information manually segmented the relevant slices from the lower part of the RN to the lower part of the SN. To minimize partial volume effects, the most inferior part and boundary regions of the SN were excluded. Finally, two volumes of interest (VOIs) were generated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eRadiomic Feature Extraction and Models Construction\u003c/h3\u003e\n\u003cp\u003eA total of 107 radiomic features were extracted from the manually segmented N1 region, including 18 first-order features, 14 shape features, and 75 texture features. LASSO regression was used to rank features and identify the top 20 predictors for sparse linear modeling after z-score normalization. LASSO logistic regression and linear SVM were evaluated using the reduced feature subset, whereas the final random forest model for asymmetry classification was evaluated using the full radiomic feature set. Three supervised machine-learning classifiers - LASSO LR, linear SVM, and random forest - were constructed for three classification tasks: (1) differentiating the more-affected side from the less-affected side, (2) classifying motor subtypes, and (3) distinguishing disease stage. Model performance was assessed by five-fold cross-validation and ROC analysis, with AUCs and 95% CIs reported. Models selection was based primarily on cross-validated AUC, whereas calibration and decision curve analysis were used as supplementary assessments of models reliability and potential clinical utility.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eCategorical variables were compared using the chi-square test. Continuous variables were analyzed using the independent-samples t test or the Mann-Whitney U test, depending on data distribution. Multivariable logistic regression analyses were conducted to control for potential confounding factors between groups. The McNemar test was used to evaluate the association between STS findings and the more-affected versus less-affected sides. Models performance was assessed and visualized using ROC curves. All statistical analyses were performed using Python (version 3.14.0) and SPSS statistical software (version 26.0). Statistical significance was defined as P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e1.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAssessment of STS Visualization\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 93 patients with PD who completed 3.0 T SWI and clinical evaluation were initially enrolled. After excluding six patients with motion artifacts and four patients with data abnormalities, 83 patients were ultimately included in the final analysis.\u003c/p\u003e\n\u003cp\u003ePatients were categorized into the total absence group and the non-total absence group according to STS visualization on SWI. Significant differences were observed between the STS visualization groups in H-Y stage, MDS-UPDRS II score, rigidity score, and plasma iron level (P\u0026lt;0.05) (Table 1). In addition, visual assessment of the STS demonstrated significant asymmetry between the more-affected side and the less-affected side in the overall PD cohort and in patients with early-stage PD. However, this side-to-side difference was not significant in patients with advanced PD (Fig. 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Radiomics Models for Differentiating the More-Affected and Less-Affected Sides in PD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the overall PD cohort, the N1 radiomics models showed limited discriminative performance for distinguishing the more-affected side from the less-affected side (the highest AUC=0.694). All models also showed limited performance in the early-stage PD subgroup (the highest AUC=0.703). However, in advanced PD, the random forest model achieved the highest performance among the three classifiers, with a cross-validated AUC of 0.896 (95% CI: 0.721-0.933) (Fig. 4A). The LASSO-derived top-20 subset was used primarily for sparse linear modeling and feature-ranking interpretation. SHAP analysis of the full-feature random forest model further demonstrated that both shape- and texture-based features contributed to the discrimination of the more-affected and less-affected sides in advanced PD (Fig. 4B). Sphericity showed the highest mean absolute SHAP value, followed by Imc2, Correlation, SizeZoneNonUniformityNormalized, ClusterShade, and Skewness. In addition, Calibration analysis demonstrated good agreement between predicted and observed outcomes, with a Brier score of 0.183.ROC curves were generated from out-of-fold predictions obtained by five-fold stratified cross-validation.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e3. Radiomics Models for Predicting Motor Subtypes in PD\u003c/h3\u003e\n\u003cp\u003eRadiomics models based on N1 features from either the less-affected side or the more-affected side demonstrated limited performance in predicting motor subtypes (all AUC \u0026lt; 0.7) (Fig. 5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Radiomics Models for Differentiating Disease Stages in PD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRadiomics models based on N1 features from either the less-affected side or the more-affected side demonstrated limited performance in distinguishing early-stage PD from advanced PD (all AUC \u0026lt; 0.7) (Fig. 5).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo the best of our knowledge, radiomic analysis of the N1 region on 3.0 T SWI has been only rarely explored in PD, particularly in relation to clinical asymmetry. Most previous radiomics studies have focused on distinguishing PD from healthy controls or from atypical parkinsonian syndromes. In this study, we investigated the clinical relevance of STS visualization and N1-derived radiomic features on 3.0 T SWI in PD. Three main findings emerged. First, STS visualization groups were significantly associated with disease severity, including H\u0026ndash;Y stage, MDS-UPDRS II score, rigidity scores, and plasma iron levels. Second, visual assessment of the STS showed significant side-to-side differences in the overall PD cohort and in early-stage PD, but not in advanced PD. Third, radiomic features derived from N1 showed the best performance in differentiating the more-affected side from the less-affected side in advanced PD, whereas their performance was limited in the overall cohort and in early-stage PD. Together, these findings suggest that visual assessment and radiomics may capture complementary aspects of nigral degeneration at different stages of PD.\u003c/p\u003e \u003cp\u003eThe observed association between STS visualization and clinical severity supports the relevance of dorsolateral nigral hyperintensity absence as an imaging marker of disease burden. As N1 is one of the earliest and most severely affected regions within the SN in PD, disappearance of the STS on SWI may reflect progressive dopaminergic neuronal loss and susceptibility-related alterations. The association with plasma iron levels further raises the possibility that altered iron metabolism contributes to N1-related imaging abnormalities. Although peripheral iron indices cannot directly reflect local brain iron content, the present finding is in line with the hypothesis that iron dysregulation contributes to the pathophysiology of nigral degeneration in PD, lending biological plausibility to this observation[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA key finding of this study is the stage-dependent relationship between visual STS assessment and radiomic analysis in evaluating disease asymmetry. In the overall cohort and particularly in early-stage PD, visual STS assessment demonstrated significant side-to-side differences between the more-affected and less-affected sides. This suggests that in earlier disease stages, when nigral degeneration remains relatively lateralized, qualitative visual evaluation may still be sufficient to detect asymmetric involvement of N1. One possible explanation is that the hemisphere corresponding to more severe motor symptoms may show earlier or more prominent loss of dorsolateral nigral hyperintensity, allowing visual asymmetry to remain appreciable on routine SWI. This interpretation is supported by recent MRI findings indicating that hemispheric neuromelanin-iron dysfunction within the SN is associated with lateralized motor onset in early-stage PD[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn contrast, visual STS asymmetry was no longer significant in advanced PD, whereas N1 radiomics showed substantially improved performance for differentiating the more-affected side from the less-affected side, with the highest AUC observed in this subgroup. In early-stage PD, interhemispheric differences within N1 may still be relatively subtle, resulting in substantial overlap of radiomic features between the more-affected and less-affected sides and thereby limiting discriminative performance. This pattern suggests that, as PD progresses and bilateral nigral degeneration becomes increasingly widespread, the ability of qualitative visual assessment to discriminate disease-related changes may be reduced. In this context, radiomic analysis may provide additional sensitivity by capturing subtle intranigral heterogeneity beyond the resolution of routine visual inspection. Importantly, recent studies have shown that higher R2* and quantitative susceptibility values in SN are associated with both motor severity and motor asymmetry, supporting the view that clinically relevant interhemispheric differences may persist even when gross bilateral STS loss reduces visual contrast[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The texture-related features identified in the model, including skewness, kurtosis, maximal correlation coefficient, and gray-level non-uniformity, may reflect increasingly complex and asymmetric microstructural alterations within the nigrosome in advanced disease.\u003c/p\u003e \u003cp\u003eImportantly, the differing performance of visual STS assessment and radiomics should not be interpreted as contradictory. Rather, the two approaches appear to offer complementary information at different stages of PD. Visual STS assessment may be more informative in early-stage PD, when asymmetry remains visually recognizable, whereas N1 radiomics may better capture residual quantitative heterogeneity in advanced PD, when bilateral STS loss reduces the sensitivity of qualitative evaluation. From a clinical perspective, this stage-specific pattern supports the potential utility of combining qualitative and quantitative N1 assessment to improve the imaging evaluation of asymmetric nigral degeneration in PD.\u003c/p\u003e \u003cp\u003eBy comparison, the performance of N1 radiomics for motor subtype prediction was limited. These findings suggest that imaging features derived from a single nigral subregion may not adequately capture the broader biological complexity underlying motor phenotype and disease progression. Motor subtype is likely influenced by distributed network-level dysfunction and extranigral pathology. This interpretation is supported by recent studies showing that motor subtype discrimination is enhanced when multimodal MRI features or cross-regional radiomic interactions within motor-circuit structures are considered, rather than focusing on a single nigral ROI[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Accordingly, although N1 radiomics may be useful for assessing asymmetric nigral involvement, its value for broader phenotypic classification appears more restricted.\u003c/p\u003e \u003cp\u003eFurthermore, the limited performance of N1 radiomics for differentiating early-stage from advanced PD may have several explanations. First, PD progression extends beyond the N1 region, and a single-region radiomics model may therefore be insufficient to capture the overall neurodegenerative burden. Second, susceptibility-related and structural changes within N1 may occur relatively early in the disease course, reducing their incremental discriminative value once degeneration is already established. Third, clinical staging in PD does not necessarily show a linear relationship with localized imaging alterations, because disease severity is influenced by multiple pathological processes beyond the nigrostriatal system. In addition, recent radiomics work suggests that MRI-derived radiomic signatures may be more informative for predicting subsequent motor progression than for coarse cross-sectional stage stratification, which may further explain why N1-based models performed better for asymmetry analysis than for stage classification in the present dataset[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Taken together, these observations indicate that N1 radiomics alone is unlikely to serve as a robust marker of overall disease stage.\u003c/p\u003e \u003cp\u003eFinally, the present findings may also have pathophysiological implications. Given the sensitivity of SWI to susceptibility-related changes and the ability of radiomic features to characterize intensity distribution and textural heterogeneity within N1, our results indirectly support the notion that iron-related microstructural alterations contribute to the imaging phenotype of PD. The significant association between STS visualization and plasma iron levels further supports this interpretation, although the biological relationship between peripheral iron markers and regional brain susceptibility changes warrants further investigation. Overall, our data suggest that quantitative analysis of N1 may provide imaging information beyond that available from conventional visual assessment alone.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eSeveral limitations of this study should be acknowledged. First, this was a retrospective single-center study with a relatively small sample size, particularly for subgroup analyses, which may limit statistical power and increase the risk of overfitting. Second, the N1 region was manually segmented, which may have introduced observer-dependent variability. Recent work indicates that more standardized localization and segmentation strategies may improve reproducibility in future studies[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].Third, the absence of an external validation cohort limits the generalizability and robustness of the radiomics models. Fourth, only SWI-based radiomic features from the N1 region were analyzed, whereas PD is a multisystem disorder involving widespread\u003c/p\u003e \u003cp\u003enigrostriatal and extranigral abnormalities. Fifth, preprocessing and harmonization choices can substantially affect feature reproducibility and classification performance in MRI radiomics, and this issue should be explicitly addressed in future multicenter studies[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Finally, although the present findings suggest stage-dependent complementary roles of visual STS assessment and N1 radiomics, the current results should be interpreted as exploratory and require confirmation in larger prospective multicenter studies. Future work incorporating multimodal MRI, additional nigral and extranigral regions, harmonization-aware radiomics workflows, and independent validation cohorts may improve both biological interpretability and predictive performance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, visual STS assessment and N1 radiomics provide complementary information on nigral degeneration in PD. Visual assessment of the STS appears to be more informative for detecting lateralized nigral changes in early-stage PD, whereas N1 radiomics may better characterize asymmetric degeneration in advanced PD. The present findings support the potential value of combining qualitative STS evaluation with quantitative N1 radiomic analysis to improve the imaging assessment of disease asymmetry in PD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: Yuan Liu, Shize Li, Zhifei Ben, Saijun ChenData curation: Yuan Liu, Shize Li, Jue WangFormal analysis: Yuan Liu, Jinyong Zhan, Kaiying XuInvestigation: Yuan Liu, Shize Li, Jue WangMethodology: Yuan Liu, Shize Li, Zhifei BenProject administration: Saijun Chen, Zhifei BenResources: Saijun Chen, Zhifei BenSoftware: Shize Li, Yuan LiuSupervision: Saijun Chen, Zhifei BenValidation: Yuan Liu, Shize LiVisualization: Yuan Liu, Shize LiWriting \u0026ndash; original draft: Yuan Liu, Shize LiWriting \u0026ndash; review \u0026amp; editing: All authorsAll authors have read and approved the final version of the manuscript.Conflicts of Interest: The authors declare no conflicts of interest. Yuan Liu and Shize Li contributed equally to this work and should be regarded as co-first authors.Saijun Chen and Zhifei Ben contributed equally to this work and should be regarded as co-corresponding authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBerg, D., et al., MDS research criteria for prodromal Parkinson\u0026apos;s disease. Movement disorders : official journal of the Movement Disorder Society, 2015. 30(12): p. 1600-1611.\u003c/li\u003e\n\u003cli\u003eFearnley, J.M. and A.J. Lees, Ageing and Parkinson\u0026apos;s disease: substantia nigra regional selectivity. Brain : a journal of neurology, 1991. 114 ( Pt 5): p. 2283-2301.\u003c/li\u003e\n\u003cli\u003eDamier, P., et al., The substantia nigra of the human brain. I. Nigrosomes and the nigral matrix, a compartmental organization based on calbindin D(28K) immunohistochemistry. 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Panahi, Cross-regional radiomics: a novel framework for relationship-based feature extraction with validation in Parkinson\u0026apos;s disease motor subtyping. BioData mining, 2025. 18(1): p. 67.\u003c/li\u003e\n\u003cli\u003eShimozono, T., T. Shiiba and K. Takano, Radiomics score derived from T1-w/T2-w ratio image can predict motor symptom progression in Parkinson\u0026apos;s disease. European radiology, 2024. 34(12): p. 7921-7933.\u003c/li\u003e\n\u003cli\u003eSuh, P.S., et al., Deep Learning-Based Algorithm for Automatic Quantification of Nigrosome-1 and Parkinsonism Classification Using Susceptibility Map-Weighted MRI. AJNR. American journal of neuroradiology, 2025. 46(5): p. 999-1006.\u003c/li\u003e\n\u003cli\u003eLancione, M., et al., High resolution multi-parametric probabilistic in vivo atlas of dorsolateral nigral hyperintensity via 7\u0026thinsp;T MRI. Scientific data, 2025. 12(1): p. 958.\u003c/li\u003e\n\u003cli\u003ePanahi, M., M.S. Hosseini and S.M.R. Aghamiri, Impact of image preprocessing methods on MRI radiomics feature variability and classification performance in Parkinson\u0026apos;s disease motor subtype analysis. Scientific reports, 2025. 15(1): p. 40030.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 1. Comparison of clinical characteristics among STS visualization groups\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"583\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTotal absence group\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=65)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eNon-total absence group\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=18)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eP Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFemale/male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31/34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7/11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.507\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge of onset\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e62.00(58.00,66.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e61.00(56.25,65.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.514\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDisease duration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.00(3.00,8.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.00(2.00,6.50)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eH-Y \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.50(2.00,3.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.00(1.50,2.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.026*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTotal MDS-UPDRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e77.40\u0026plusmn;35.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e67.72\u0026plusmn;36.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.306\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMDS-UPDRS I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.78\u0026plusmn;5.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.00\u0026plusmn;7.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.896\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMDS-UPDRS II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15.00(12.00,22.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.00(5.25,15.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.013*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMDS-UPDRS III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e45.49\u0026plusmn;21.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e39.83\u0026plusmn;20.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.331\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTremor scores\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.00(1.25,10.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.50(5.25,12.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.278\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRigidity scores\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.00(3.00,9.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.50(2.00,6.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.038*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNMSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33.00(23.25,64.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e39.50(19.00,78.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.991\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMMSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27.00(22.00,29.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27.50(22.25,29.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.982\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHAMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.50(5.25,12.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.50(6.25,19.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.355\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHAMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.50(7.25,16.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.00(5.00,17.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.517\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCeruloplasmin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26.45(24.03,31.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26.80(23.00,32.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.898\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eplasma levels of iron\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e8.18(7.86,8.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003e\n \u003cp\u003e8.60(8.41,8.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.041*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eH-Y, Hoehn and Yahr stage; MDS-UPDRS, Movement Disorder Society\u0026ndash;Unified Parkinson\u0026rsquo;s Disease Rating Scale; MDS-UPDRS I, Part I: Non-Motor Experiences of Daily Living; MDS-UPDRS II, Part II: Motor Experiences of Daily Living; MDS-UPDRS III, Part III: Motor Examination; NMSS, Non-Motor Symptoms Scale; MMSE, Mini-Mental State Examination.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"parkinson’s disease, nigrosome-1, susceptibility-weighted imaging, radiomic, swallow tail sign","lastPublishedDoi":"10.21203/rs.3.rs-9436986/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9436986/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eNigrosome-1 (N1) is one of the earliest regions affected in Parkinson's disease (PD) and can be visualized on 3.0-T susceptibility-weighted imaging (SWI) as the swallow tail sign (STS). This study investigated the associations of visual STS assessment and N1-derived radiomic features with clinical characteristics in PD.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this retrospective study, 83 patients with PD who underwent 3.0-T SWI and clinical evaluation were included. Patients were categorized according to STS visualization as having total absence or non-total absence of dorsolateral nigral hyperintensity. The N1 region was manually segmented on SWI, and 107 radiomic features were extracted. Least bsolute shrinkage and selection operator (LASSO) regression was applied for radiomic feature selection. Based on the selected features, three machine learning classifiers\u0026mdash;LASSO-logistic regression (LASSO LR), the random forest, and a linear support vector machine (linear SVM)\u0026mdash;were constructed and compared.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSTS visualization was significantly associated with clinical severity indicators, including Hoehn-Yahr (H-Y) stage, MDS-UPDRS II score, rigidity score, and plasma iron level (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Visual STS assessment showed significant side-to-side differences in the overall PD cohort and in early-stage PD, whereas no significant difference was observed in advanced PD. Among the tested classifiers, the random forest model showed the best performance for differentiating the more-affected from the less-affected side in advanced PD (AUC\u0026thinsp;=\u0026thinsp;0.896, 95% CI: 0.721\u0026ndash;0.933), while all models showed limited performance in the overall cohort and in early-stage PD (all AUC\u0026thinsp;\u0026lt;\u0026thinsp;0.7). Additionally, radiomics models showed limited ability to distinguish motor subtype and disease stage (all AUC\u0026thinsp;\u0026lt;\u0026thinsp;0.7).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eVisual STS assessment and N1 radiomics may provide complementary information on nigral degeneration in PD. Visual assessment appears to be more informative for detecting asymmetry in early-stage PD, whereas N1 radiomics may better characterize asymmetric degeneration in advanced PD.\u003c/p\u003e","manuscriptTitle":"Radiomic Analysis of Nigrosome-1 on 3.0T Susceptibility-Weighted Imaging for Characterizing Asymmetric Degeneration in Parkinson’s Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-07 20:02:15","doi":"10.21203/rs.3.rs-9436986/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1bb2a65b-0b1c-486c-91f5-22d7d345450f","owner":[],"postedDate":"May 7th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-15T14:30:22+00:00","index":18,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-12T13:01:44+00:00","index":17,"fulltext":""},{"type":"reviewerAgreed","content":"146660890430534931651433503521437341318","date":"2026-05-05T05:07:55+00:00","index":16,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-07T20:02:15+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-07 20:02:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9436986","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9436986","identity":"rs-9436986","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}