
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

39237644
71860
10.1038/s41598-024-71860-y
Article
Multi-modality radiomics of conventional T1 weighted and diffusion tensor imaging for differentiating Parkinson’s disease motor subtypes in early-stages
Panahi Mehdi mitipanahi@gmail.com

1
Hosseini Mahboube Sadat 2
1 Department of Computer Engineering, Payame Noor University Erbil Branch, Erbil, Iraq
2 https://ror.org/0091vmj44 grid.412502.0 0000 0001 0686 4748 Department of Medical Radiation Engineering, Shahid Beheshti University, Tehran, Iran
5 9 2024
5 9 2024
2024
14 2070819 7 2024
2 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
This study aimed to develop and validate a multi-modality radiomics approach using T1-weighted and diffusion tensor imaging (DTI) to differentiate Parkinson's disease (PD) motor subtypes, specifically tremor-dominant (TD) and postural instability gait difficulty (PIGD), in early disease stages. We analyzed T1-weighted and DTI scans from 140 early-stage PD patients (70 TD, 70 PIGD) and 70 healthy controls from the Parkinson's Progression Markers Initiative database. Radiomics features were extracted from 16 brain regions of interest. After harmonization and feature selection, four machine learning classifiers were trained and evaluated for both three-class (HC vs TD vs PIGD) and binary (TD vs PIGD) classification tasks. The light gradient boosting machine (LGBM) classifier demonstrated the best overall performance. For the three-class classification, LGBM achieved an accuracy of 85% and an area under the receiver operating characteristic curve (AUC) of 0.94 using combined T1 and DTI features. In the binary classification task, LGBM reached an accuracy of 95% and AUC of 0.95. Key discriminative features were identified in the Thalamus, Amygdala, Hippocampus, and Substantia Nigra for the three-group classification, and in the Pallidum, Amygdala, Hippocampus, and Accumbens for binary classification. The combined T1 + DTI approach consistently outperformed single-modality classifications, with DTI alone showing particularly low performance (AUC 0.55–0.62) in binary classification. The high accuracy and AUC values suggest that this approach could significantly improve early diagnosis and subtyping of PD. These findings have important implications for clinical management, potentially enabling more personalized treatment strategies based on early, accurate subtype identification.

Keywords

Parkinson's disease
Radiomics
Motor subtypes
Magnetic resonance imaging
Machine learning
Subject terms

Biomarkers
Neurology
issue-copyright-statement© Springer Nature Limited 2024
==== Body
pmcIntroduction

Parkinson's disease (PD) is the second most common neurodegenerative disorder, affecting millions of people worldwide with an increasing prevalence in aging populations1. PD is characterized by a complex array of motor and non-motor symptoms that lead to disability, reduced quality of life, and increased mortality and pose significant burden on healthcare systems and society2–5. Early and accurate diagnosis of PD remains a significant clinical challenge, with misdiagnosis rates as high as 24% in early stages6–9. Furthermore, the heterogeneity of PD clinical presentation has led to the identification of distinct motor subtypes, primarily the tremor-dominant (TD) and postural instability gait difficulty (PIGD) subtypes10,11. Compared to the TD subtype, patients with PIGD tend to have more severe impairment of nervous system function, including gait inhibition, anxiety, and dementia12,13. The PIGD subtype also shows poorer response to treatments like deep brain stimulation and levodopa14,15. Therefore, both early diagnosis and accurate subtyping of PD are crucial for optimal patient management and personalized treatment strategies.

Currently, diagnosis and differentiation of PD subtypes rely heavily on clinical assessment, including patient history and observable symptoms. However, this approach can be subjective and may lack precision, especially in early disease stages16. There is an urgent need to develop more objective and sensitive diagnostic tools for early PD detection and subtyping.

Magnetic resonance imaging (MRI) has emerged as a powerful tool for studying brain changes in neurological disorders, including PD. Various MRI techniques, including structural MRI, diffusion tensor imaging (DTI), and functional MRI, have been applied to investigate PD-related brain alterations17,18. Conventional MRI techniques have revealed structural and functional differences between PD subtypes. For instance, Rosenberg-Katz et al.19 reported greater atrophy in motor, cognitive, and marginal areas in PIGD compared to TD patients. Zhang et al.20 demonstrated distinct functional connectivity patterns in PD subtypes using resting-state functional Magnetic Resonance Imaging (fMRI). However, most previous studies have focused on analyzing low-level features from MRI images, potentially overlooking valuable high-level image information.

Radiomics is an innovative approach that extracts large numbers of quantitative features from medical images to characterize tissue heterogeneity beyond what is visually apparent21. Combined with machine learning techniques, radiomics has shown great potential for detecting neuroimaging biomarkers in various neuropsychiatric disorders22–24. In recent years, there has been growing interest in applying radiomics analysis to study PD diagnosis and subtype classification. Liu et al.25 applied radiomics analysis to T2-weighted images of the neostriatum for PD diagnosis. Choi et al.26 used deep learning-based radiomics features from dopamine transporter imaging to improve PD diagnosis. Rahmim et al.27 applied radiomics analysis to longitudinal dopamine transporter single photon emission computed tomography (DaT-SPECT) images, enhancing prediction of PD progression. Xiao et al.28 developed a hybrid radiomics model based on quantitative susceptibility mapping for PD diagnosis.

While these studies have shown promising results, they primarily focused on single imaging modalities. The potential of multi-modality radiomics incorporating both structural and microstructural information has not been fully explored in PD subtyping. Recent work by Bian et al.29 highlighted the potential of combining different MRI sequences for improved PD diagnosis in their systematic review. Furthermore, Feng et al.30 emphasized the value of radiomics in capturing subtle brain changes in neurodegenerative diseases, suggesting its potential in differentiating disease subtypes.

Building on these advances, our study aims to develop a novel multi-modality radiomics approach combining conventional T1-weighted imaging and DTI to differentiate PD motor subtypes in early disease stages. This approach offers several innovative aspects. By integrating complementary information from T1-weighted and DTI sequences, we aim to capture both macrostructural and microstructural brain changes associated with PD subtypes. Unlike previous studies that focused on binary classifications, we perform both a three-class classification (HC vs. TD vs. PIGD) and a binary classification (TD vs. PIGD), providing a more comprehensive assessment of PD subtypes.

Our work is inspired by recent studies that have shown the potential of radiomics in PD research. Shu et al.31 developed an integrative nomogram combining white matter radiomics features from T1-weighted MRI and non-motor symptoms for identifying early-stage PD. Sun et al.32 utilized radiomics analysis of subcortical nuclei to differentiate between TD and PIGD subtypes. However, these studies were limited to single imaging modalities and did not explore the full potential of combining structural and diffusion MRI data.

In this study, we extract radiomic features from T1-weighted and DTI images of early-stage PD patients and healthy controls. We then develop and validate machine learning models for both three-class (HC vs. TD vs. PIGD) and binary (TD vs. PIGD) classifications. By identifying robust imaging biomarkers that can accurately classify PD motor subtypes, we aim to provide a valuable tool for early, objective PD subtype diagnosis. This approach has the potential to guide personalized treatment strategies, improve patient outcomes, and contribute to our understanding of the neurobiological underpinnings of PD subtypes.

Our research addresses the pressing need for more accurate and objective methods of PD subtype classification, potentially revolutionizing the diagnosis and management of this complex neurodegenerative disorder. By leveraging the power of multi-modality radiomics and advanced machine learning techniques, we hope to overcome the limitations of current diagnostic approaches and pave the way for more personalized and effective PD management strategies.

Results

To visualize the effect of harmonization, we performed Principal Component Analysis (PCA) on the data before and after the ComBat harmonization process. The PCA visualizations were performed on the entire harmonized dataset to demonstrate the overall effect of harmonization, prior to splitting the data into training and test sets for the machine learning models. Figure 1 left side shows the PCA plot before harmonization, where clear clustering of data points by scanner model is evident, indicating the presence of batch effects. Figure 1 right side displays the PCA plot after harmonization, demonstrating a more homogeneous distribution of data points across different scanner models, suggesting successful reduction of scanner-related variability while preserving biological differences between HC (Healthy Control), PIGD, and TD groups. Additionally, we examined the distribution of individual radiomic features across scanner models before and after harmonization. Figure 2 illustrates this for a representative feature, showing a more consistent distribution across scanner models after harmonization, further confirming the effectiveness of the ComBat method in mitigating scanner-related differences.Fig. 1 PCA visualization of the entire dataset before (left) and after (right) harmonization. Left: PCA plot before harmonization shows clear clustering by group, indicating potential batch effects. Right: PCA plot after harmonization demonstrates a more homogeneous distribution of data points, suggesting successful reduction of scanner-related variability while preserving biological differences between HC (red), PIGD (green), and TD (blue) groups.

Fig. 2 Distribution of a representative feature across scanner models before and after harmonization.

In our analysis, we identified the 20 most important features for each classification type and modality, which are illustrated in Supplementary Figs. 1–6. Focusing on the top 5 optimal features, we observed distinct patterns across different classifications and modalities. For the three-group classification, T1-weighted imaging revealed key features in the left Thalamus, left Hippocampus, left Accumbens, and right Pallidum. DTI analysis highlighted the left Accumbens, left Amygdala, right bilateral Thalamus, and left Putamen. The combined modality approach identified important features in the Thalamus, left Amygdala, left Hippocampus, and right Substantia-Nigra. In the binary classification, T1-weighted imaging emphasized features in the left Amygdala, left Caudate, and left Pallidum. DTI analysis for this classification highlighted the left Hippocampus, left Thalamus, Substantia-Nigra, and right Accumbens. The combined modality approach for binary classification identified key features in the left Pallidum, left Amygdala, left Hippocampus, and left Accumbens. These findings underscore the specific brain regions that contribute most significantly to differentiating between groups in our radiomics-based classification models, providing insight into the neuroanatomical basis of our classification approach.

Our study evaluated the performance of four machine learning models (Random Forest (RF), Decision Tree (DT), Light Gradient Boosting Machine (LGBM), and AdaBoost (ADA)) across two imaging modalities (T1-weighted and DTI) and their combination (T1 + DTI) for both three-class and binary classification tasks .

For the three-class classification (HC vs TD vs PIGD), Table 1 summarizes the performance metrics for each model and modality combination. While we calculated additional performance metrics including precision, recall, and F1-score, we focus our reporting on sensitivity, specificity, accuracy, and AUC as these provide a comprehensive and non-redundant assessment of model performance. Full results for all metrics are available in Supplementary Table 5. As shown, accuracies ranged from 0.65 to 0.86, with AUC (area under the curve) values between 0.76 and 0.95. Figure 3 visualizes the corresponding ROC (operating characteristic curve) curves, illustrating the trade-off between sensitivity and specificity for each model across the two imaging modalities and their combination. In the T1-weighted modality, the LGBM model achieved the highest AUC of 0.90. For DTI, the LGBM model again showed the best AUC at 0.94, while the Decision Tree model had the highest accuracy of 0.86. In the combined T1 + DTI approach, the LGBM model maintained the highest AUC at 0.93.Table 1 The performance of different models using T1 and DTI sequence in three-classification tasks (HC vs DTI vs TD).

Sequence	Models	Average	
Sensitivity	Specificity	Accuracy	AUC	
T1	RF	0.97	0.95	0.77	0.87	
DT	0.91	0.95	0.74	0.80	
LGBM	0.92	0.97	0.79	0.90	
ADA	0.93	0.80	0.65	0.81	
DTI	RF	0.81	0.80	0.67	0.83	
DT	0.92	0.97	0.86	0.89	
LGBM	0.89	0.98	0.85	0.94	
ADA	0.79	0.86	0.74	0.92	
T1 + DTI	RF	0.96	0.93	0.83	0.92	
DT	0.93	0.93	0.86	0.89	
LGBM	0.94	0.90	0.83	0.93	
ADA	0.92	0.85	0.70	0.76	

Fig. 3 Receiver operator curves (ROC) of different models based on radiomic features extracted from the T1-weighted, DTI and combined T1 + DTI sequence in three-group classification. RF: Random Forest, DT: Decision Tree, LGBM: Light Gradient Boosting Machine, ADA: AdaBoost.

For the binary classification task (TD vs PIGD), Table 2 presents the performance metrics, while Fig. 4 displays the ROC curves. As with the three-class classification, we calculated additional performance metrics including precision, recall, and F1-score for the binary classification task. However, for consistency and conciseness, we focus our reporting on sensitivity, specificity, accuracy, and AUC in Tables 1 and 2. Full results for all metrics are available in Supplementary Table 5 Table 6. In the T1-weighted modality, all models demonstrated high performance, with AUC values ranging from 0.92 to 0.96 and accuracies between 0.92 and 0.93. The Random Forest model achieved the highest AUC of 0.96. Notably, the DTI modality alone showed lower performance for this binary task, with AUC values between 0.53 and 0.62. The combined T1 + DTI approach improved upon DTI alone, with the LGBM model achieving the highest AUC of 0.95 and an accuracy of 0.95.Fig. 4 Receiver operator curves (ROC) of different models based on radiomic features extracted from the T1-weighted, DTI and combined T1 + DTI sequence in binary-group classification. RF: Random Forest, DT: Decision Tree, LGBM: Light Gradient Boosting Machine, ADA: AdaBoost.

Table 2 The performance of different models using T1 and DTI sequence in binary classification tasks (TD vs PIGD).

Sequence	Models	Average	
Sensitivity	Specificity	Accuracy	AUC	
T1	RF	0.90	0.94	0.92	0.96	
DT	0.90	0.94	0.92	0.92	
LGBM	0.90	0.97	0.93	0.95	
ADA	0.90	0.94	0.92	0.95	
DTI	RF	0.56	0.58	0.57	0.59	
DT	0.64	0.61	0.62	0.62	
LGBM	0.50	0.60	0.55	0.55	
ADA	0.53	0.42	0.47	0.53	
T1 + DTI	RF	0.83	0.98	0.91	0.91	
DT	0.83	0.98	0.91	0.90	
LGBM	0.89	1	0.95	0.95	
ADA	0.83	0.95	0.89	0.89	

Across both classification tasks, the LGBM model consistently demonstrated strong performance. The combined T1 + DTI approach generally yielded improved or comparable results to individual modalities, particularly in the binary classification task where it substantially outperformed DTI alone.

Discussion

The primary goal of this research was to investigate the potential of radiomics analysis using conventional T1-weighted and DTI to differentiate PD motor subtypes, specifically the TD and PIGD subtypes, in early disease stages. By extracting a large number of quantitative features from MRI scans and applying advanced machine learning techniques, we aimed to develop an objective and sensitive method for PD subtype classification that could aid in early diagnosis and personalized treatment strategies.

Our results demonstrated that radiomics features extracted from combined T1 and DTI sequences, when used with machine learning classifiers, can effectively differentiate between TD and PIGD subtypes as well as between PD patients and healthy controls. The LGBM classifier showed the best overall performance, achieving an accuracy of 85.4% and AUC of 0.881 for binary TD vs PIGD classification. For the three-class problem of distinguishing TD, PIGD and controls, LGBM achieved 81.4% accuracy and 0.95 AUC. These results indicate that our radiomics approach can capture subtle brain differences between PD subtypes.

A substantial finding of our study was the identification of key brain regions contributing to the classification. For the three-group classification, important features were found in the Thalamus, Amygdala, Hippocampus, and Substantia Nigra. For binary classification, key features were identified in the Pallidum, Amygdala, Hippocampus, and Accumbens. These findings provide insight into the neuroanatomical basis of our classification approach and align with known pathological changes in PD. The importance of the thalamus in our classification reflects its role in motor control and sensory processing, which are affected differently in PD subtypes. The amygdala's significance may relate to varying non-motor symptoms between subtypes, particularly mood disorders. Hippocampal involvement aligns with studies showing hippocampal atrophy in PD, potentially contributing to cognitive differences between subtypes. The substantia nigra's importance reflects the fundamental pathology of PD, with potentially different patterns of neuronal loss in different subtypes. The pallidum's role in our model may indicate differential basal ganglia involvement in TD versus PIGD subtypes. Finally, the accumbens' significance suggests that both motor and non-motor features, mediated by the ventral striatum, may distinguish PD subtypes. These neuroanatomical correlates provide a biological basis for our classification model and support the view of PD as a heterogeneous, multisystem disorder.

Compared to the study by Sun et al.32, which used only T1-weighted images and achieved an AUC of 0.833 for TD vs PIGD classification, our multi-sequence approach demonstrated superior performance (AUC 0.95). This highlights the added value of incorporating DTI-derived features. While Bu et al.33 focused on differentiating PD from multiple system atrophy rather than PD subtypes, they also found benefit in combining T1 and susceptibility-weighted imaging, achieving 85.4% accuracy. Our work extends this multi-sequence concept specifically to PD subtype classification, achieving higher accuracy (95%) for binary classification.

There are some limitations to consider. As a retrospective study using data from a single database (PPMI), validation on external datasets is needed to ensure generalizability. While our final analysis included equal group sizes (70 TD, 70 PIGD, 70 HC), the initial pool of eligible subjects, particularly PIGD patients with both required imaging modalities, was limited in the PPMI database. Larger multi-center studies would help confirm the robustness of the identified radiomic biomarkers. Additionally, while we focused on early-stage PD, longitudinal studies tracking radiomics changes over disease progression could provide further insights.

Future work should explore integration of other imaging modalities like functional MRI or PET to potentially improve classification performance. Investigating correlations between radiomic features and other clinical/biological markers could enhance our understanding of PD subtype pathophysiology. There is also potential to apply similar radiomics approaches to predict disease progression or treatment response.

In conclusion, this study demonstrates the promise of multi-sequence MRI radiomics for objective PD motor subtype classification. By leveraging advanced image analysis and machine learning on routine clinical scans, this approach could aid in early, accurate diagnosis and subtyping of PD. This has important implications for clinical management, as early identification of subtypes could guide more personalized treatment strategies. The radiomics-derived imaging biomarkers also provide new avenues for investigating the neurobiological basis of PD heterogeneity.

The importance of our work lies in its potential to address a critical clinical challenge in PD management. Current methods of PD subtype classification rely heavily on subjective clinical assessments, which can be imprecise, especially in early disease stages. Our radiomics-based approach offers a more objective and potentially more sensitive tool for early subtype identification. This could lead to earlier and more targeted interventions, potentially improving patient outcomes. Furthermore, our multi-modal approach, combining T1 and DTI data, represents an advancement over previous studies that relied on single imaging modalities. This combination allows for a more comprehensive characterization of brain changes in PD, capturing both structural and microstructural alterations. The superior performance of our model compared to previous studies underscores the value of this multi-modal approach. Lastly, the identification of specific brain regions contributing to subtype classification provides valuable insights into the neuroanatomical basis of PD heterogeneity. This could guide future research into the pathophysiological mechanisms underlying different PD subtypes and potentially inform the development of targeted therapies.

In summary, our work demonstrates the potential of multi-sequence MRI radiomics for objective PD motor subtype classification, achieving high accuracy (95% for binary classification) that improves upon previous single-modality approaches. This represents a promising step towards more precise PD subtyping, particularly in early disease stages. By leveraging advanced image analysis and machine learning on routine clinical scans, this approach could aid in earlier and more accurate diagnosis of PD subtypes.

While these results are encouraging, it's important to note that further validation on larger, multi-center datasets is necessary before this approach can be considered for clinical application. Additionally, longitudinal studies are needed to assess how well these radiomics-based classifications predict disease progression and treatment response. If validated in larger studies, this radiomics-based approach could contribute to more personalized PD management by enabling earlier identification of subtypes, potentially guiding treatment decisions. Furthermore, the radiomics-derived imaging biomarkers provide new avenues for investigating the neurobiological basis of PD heterogeneity, which could inform future research into targeted therapies.

Materials and methods

The schematic of our workflow was depicted in Fig. 5.Fig. 5 Workflow of present study. Abbreviations: PPMI—Parkinson's Progression Markers Initiative; HC—Healthy Control; TD—Tremor Dominant; PIGD—Postural Instability Gait Difficulty; DTI-RL—Diffusion Tensor Imaging Right to Left; DTI-LR—Diffusion Tensor Imaging Left to Right; ROI—Region of Interest; RF—Random Forest; LGBM—Light Gradient Boosting Machine; ADA—AdaBoost; DT—Decision Tree.

Participants

Data used in this study were extracted from the Parkinson’s progression markers initiative (PPMI) database (www.ppmi-info.org). The T1-weighted and DTI images selected for this study were acquired using a 3 Tesla scanner from different manufactures (GE, Siemens). Acquisition protocols are reported in Supplementary Table 1 and Table 2. Because the PPMI is a longitudinal study, we chose to use the baseline data to study early-stage PD. Importantly, all subjects were in the first or second stage of the disease according to the Hoehn-Yahr (H&Y) scale, and none of them had received drug treatment. A total of 140 PD patients and 70 healthy controls (HCs) were included for this study. All PD patients were further divided into subgroups of PIGD (n = 70) and TD (n = 70) based on the method used by Stebbins et al.34, according to the ratio of the mean Unified Parkinson's Disease Rating Scale (UPDRS) TD scores to the mean UPDRS PIGD scores (ratio ≥ 1.5 as TD, ratio ≤ 1 as PIGD). In addition, patients who had a positive mean in the numerator and a zero in the denominator were classified as TD; patients with a zero in the numerator and a positive mean in the denominator were classified as PIGD. Characteristics of the three groups including age, gender, TD and PIGD scores, and H&Y stage are summarized in Table 3. Finally, a three labeled group of HCs vs PIGD vs TD, and a binary labeled group of PIGD vs TD were obtained.Table 3 Basic characteristics of patients and controls.

Variable	HCs (n = 70)	PIGD (n = 70)	TD (n = 70)	
Age (years)	65.3 ± 10.7	65.9 ± 8.7	65.9 ± 8.6	
Gender (M/F)	36/34	35/35	33/37	
H&Y stage	–	1.6 ± 0.5	1.8 ± 0.4	
PIGD scores	–	0.4 ± 0.2	0.2 ± 0.2	
TD scores	–	0.2 ± 0.2	0.7 ± 0.3	

In the PPMI database, the number of patients who had undergone both T1-weighted imaging and DTI with reversed phase encoding was limited, particularly for PIGD patients. Initially, we had a larger pool of TD and HC subjects compared to PIGD subjects. To minimize potential confounding effects from age and sex differences between groups, we performed age and sex matching. This matching process resulted in equal group sizes of 70 subjects each for TD, PIGD, and HC groups, with the number in each group limited by the available PIGD subjects who met all inclusion criteria.

Image preprocessing

All image preprocessing steps were performed using FMRIB Software Library (FSL) version 5.0.9 (Oxford Centre for Functional MRI of the Brain, UK), a comprehensive library of analysis tools for functional, structural, and diffusion MRI brain imaging data35. For T1-weighted images, skull stripping was performed using FSL's Brain Extraction Tool (BET)36. Bias field correction was then carried out using FSL's FMRIB's Automated Segmentation Tool (FAST)37.

DTI data underwent a series of preprocessing steps. Initially, skull stripping was performed using FSL's BET, followed by bias field correction using FSL's FAST. Susceptibility-induced distortion correction was then applied using FSL's TOPUP tool38. This was followed by correction for eddy current-induced distortions and subject movement using FSL's eddy tool39. After these corrections, tensor fitting was performed using FSL's DTIFIT tool to generate Fractional Anisotropy (FA) and Mean Diffusivity (MD) maps40. These preprocessing steps ensured that all images were corrected for common artifacts.

ROI segmentation

16 brain volume of interest (VOI) including bilateral Accumbens (Ac), Amygdala (Am), Caudate Nucleus (CN), Hippocampus (H), Pallidum (Pa), Putamen (Pu), Substantia Nigra (SN), and Thalamus (Th) were extracted as masks. The Harvard–Oxford subcortical structural probabilistic atlas41–44, provided in FSLView, was utilized to extract probabilistic masks from the labels for the Ac, Am, CN, H, Pa, Pu and Th on the standard MNI-152-1 mm brain template. The probabilistic masks for each structure were processed with a threshold level of 50% probability using fslmaths to diminish the possibility of overlap with adjacent structures and subsequently converted to binary masks. Additionally, the probabilistic atlas of the basal ganglia (ATAG)45 was employed to extract SN probabilistic masks on the standard MNI-152-1 mm brain template. Subsequently, fslmaths was utilized to process standard SN probabilistic mask with a threshold level of 5% probability and convert them to binary masks. The linear and non-linear registration using FSL's FMRIB's Linear Image Registration Tool (FLIRT) and FMRIB's Non-linear Image Registration Tool (FNIRT)46,47 were then employed to register these standard masks from standard MNI-152-1 mm brain template to native space for each subject. The FMRIB's invwarp tool was utilized to calculate the inverse warp of non-linear transformation of the MR images to standard space. This inverse transformation was subsequently employed with the applywarp tool to de-normalize all standard masks from the standard MNI-152-1 mm brain template to the subject space for each participant.

Features extraction

For each VOI on T1-weighted, FA and MD maps, a total of 94 features, consisting of 19 features from first-order image intensity statistics, 24 from gray level cooccurrence matrix (GLCM), 16 from gray level run length matrix (GLRLM), 16 from gray level size zone matrix (GLSZM), 14 from gray level dependence matrix (GLDM), and 5 from neighboring gray tone difference matrix (NGTDM)), were automatically extracted using the open-source Python package of Pyradiomics48. Supplementary Table 3 summarizes all radiomic features considered in this work. Prior to feature extraction, we applied 15 image filters to each VOI using SimpleITK filters. These filters included wavelet transformations (applying all possible combinations of high or low pass filters in each of the three dimensions: HHH, HHL, HLH, HLL, LHH, LHL, LLH, and LLL), Laplacian of Gaussian (LoG) with a sigma value of 1 mm, Exponential, Gradient, Logarithm, Square, and Square Root filters. The wavelet filters capture multi-scale texture information, while the LoG filter enhances edges and high-frequency features. The Exponential and Square filters emphasize higher intensity values, the Logarithm filter enhances details in darker regions while compressing brighter areas, the Gradient filter highlights intensity changes, and the Square Root filter enhances lower intensity values. After applying each filter, we extracted the full set radiomic features from the resulting filtered images. This approach allowed us to capture a wide range of image characteristics, providing a comprehensive set of features for our radiomics analysis. All images were discretized into 64 fixed bin count and calculated bin width in each VOI based on intensity range. In total 4512 features (3 modalities × 16 VOIs × 94 features) were extracted for each subject.

Features selection and modeling

Our feature selection and modeling process began with data splitting, followed by harmonization to account for potential batch effects, and then feature selection and classification. We first split our dataset into training (80%) and test (20%) sets using stratified sampling to maintain the proportion of classes across all sets. This splitting was performed before any feature selection or model training to ensure an unbiased evaluation of our final models.

Prior to feature selection, we applied the ComBat harmonization method using the neuroCombat package to reduce potential batch effects arising from different scanner models49. The process adjusted for covariates including scanner model, group (HC, TD, PIGD), sex, and age, helping to minimize non-biological variations while preserving meaningful biological differences.

Following ComBat harmonization, we applied an additional normalization step using StandardScaler from the scikit-learn library in Python. This step ensures all features are scaled to have a mean of 0 and a standard deviation of 1 before proceeding with feature selection and model training. After normalization, we employed a two-step feature selection process on the training data. First, we applied variance thresholding with a threshold of 0.01 to remove features with low variance across samples50. Subsequently, we used a Linear Support Vector Classification (LinearSVC) model with L1 penalty to further reduce the feature set, selecting the most informative features for classification51. This method promotes feature sparsity by assigning zero coefficients to less informative features, effectively selecting the most relevant ones52. We set the regularization parameter C to 0.1, emphasizing regularization and feature sparsity53. The L1 regularization (with penalty parameter set to 'l1') further enhanced feature selection. To accommodate L1 regularization, we set the dual parameter to False.

For feature selection criteria, we retained features with non-zero coefficients after fitting the LinearSVC model. This approach allows the model to automatically select the most informative features based on their contribution to the classification task. We did not perform explicit hyperparameter optimization; the C value of 0.1 was chosen based on its ability to emphasize regularization and feature sparsity, as supported by previous research53. To assess the stability of feature selection, we used a fivefold stratified cross-validation approach within the training set, performing feature selection independently for each fold.

For modeling, we trained four different machine learning classifiers on the training data: Random Forest54 (RF), Decision Tree55 (DT), Light Gradient Boosting Machine (LGBM)56, and AdaBoost57 (ADA). Each classifier was trained using default hyperparameters. We used fivefold stratified cross-validation on the training set to assess model performance and stability.

We evaluated the final performance of each classifier on the held-out test set, computing several performance metrics including sensitivity, specificity, accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC).

These analyses were performed using Python 3.7 with scikit-learn 0.24.2, LightGBM 3.2.1, neuroCombat, and other relevant libraries for machine learning, data harmonization, and visualization.

Methodology checklist

To ensure comprehensive reporting of our radiomics methodology, we followed a simplified version of the CLEAR (CheckList for EvalAation of Radiomics) guidelines58. A completed checklist is provided in the supplementary materials (Supplementary Table 4).

Supplementary Information

Supplementary Information.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-71860-y.

Author contributions

M.P. Writing—original draft, conceptualization, software, methodology, investigation, formal analysis. MS.H. writing—review & editing, methodology, software, conceptualization, investigation.

Data availability

Publicly available datasets were analyzed in this study. This data can be found here: https://www.ppmi-info.org/access-data-specimens/download-data.

Competing interests

The authors declare no competing interests.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References

1. Dorsey EA Projected number of people with Parkinson disease in the most populous nations 2005 through 2030 Neurology 2007 68 384 386 10.1212/01.wnl.0000247740.47667.03 17082464
Dorsey, E. A. et al. Projected number of people with Parkinson disease in the most populous nations 2005 through 2030. Neurology 68, 384–386 (2007).17082464 10.1212/01.wnl.0000247740.47667.03
2. Dorsey ER Bloem BR The Parkinson pandemic—a call to action JAMA Neurol. 2018 75 9 10 10.1001/jamaneurol.2017.3299 29131880
Dorsey, E. R. & Bloem, B. R. The Parkinson pandemic—a call to action. JAMA Neurol. 75, 9–10 (2018).29131880 10.1001/jamaneurol.2017.3299
3. Dorsey ER Global, regional, and national burden of Parkinson's disease, 1990–2016: A systematic analysis for the Global Burden of Disease Study 2016 Lancet Neurol. 2018 17 939 953 10.1016/S1474-4422(18)30295-3 30287051
Dorsey, E. R. et al. Global, regional, and national burden of Parkinson’s disease, 1990–2016: A systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 17, 939–953 (2018).30287051 10.1016/S1474-4422(18)30295-3
4. Schapira AH Chaudhuri KR Jenner P Non-motor features of Parkinson disease Nat. Rev. Neurosci. 2017 18 435 450 10.1038/nrn.2017.62 28592904
Schapira, A. H., Chaudhuri, K. R. & Jenner, P. Non-motor features of Parkinson disease. Nat. Rev. Neurosci. 18, 435–450 (2017).28592904 10.1038/nrn.2017.62
5. Titova N Chaudhuri KR Personalized medicine in Parkinson's disease: time to be precise Mov. Disord. 2017 32 1147 10.1002/mds.27027 28605054
Titova, N. & Chaudhuri, K. R. Personalized medicine in Parkinson’s disease: time to be precise. Mov. Disord. 32, 1147 (2017).28605054 10.1002/mds.27027
6. Rizzo G Accuracy of clinical diagnosis of Parkinson disease: a systematic review and meta-analysis Neurology 2016 86 566 576 10.1212/WNL.0000000000002350 26764028
Rizzo, G. et al. Accuracy of clinical diagnosis of Parkinson disease: a systematic review and meta-analysis. Neurology 86, 566–576 (2016).26764028 10.1212/WNL.0000000000002350
7. Azimi, M.-S. et al. in 2022 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). 1–4 (IEEE).
8. Azimi, M.-S. et al. in 2022 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). 1–4 (IEEE).
9. Hosseini, M. S. et al. in 2022 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). 1–4 (IEEE).
10. Jankovic J Variable expression of Parkinson's disease: A base-line analysis of the DAT ATOP cohort Neurology 1990 40 1529 1529 10.1212/WNL.40.10.1529 2215943
Jankovic, J. et al. Variable expression of Parkinson’s disease: A base-line analysis of the DAT ATOP cohort. Neurology 40, 1529–1529 (1990).2215943 10.1212/WNL.40.10.1529
11. Eisinger RS Motor subtype changes in early Parkinson's disease Parkinsonism Related Disorders 2017 43 67 72 10.1016/j.parkreldis.2017.07.018 28754232
Eisinger, R. S. et al. Motor subtype changes in early Parkinson’s disease. Parkinsonism Related Disorders 43, 67–72 (2017).28754232 10.1016/j.parkreldis.2017.07.018
12. Kulisevsky J Prevalence and correlates of neuropsychiatric symptoms in Parkinson's disease without dementia Movem. Disorders 2008 23 1889 1896 10.1002/mds.22246
Kulisevsky, J. et al. Prevalence and correlates of neuropsychiatric symptoms in Parkinson’s disease without dementia. Movem. Disorders 23, 1889–1896 (2008).10.1002/mds.22246
13. Kelly V Association of cognitive domains with postural instability/gait disturbance in Parkinson's disease Parkinsonism Related Disorders 2015 21 692 697 10.1016/j.parkreldis.2015.04.002 25943529
Kelly, V. et al. Association of cognitive domains with postural instability/gait disturbance in Parkinson’s disease. Parkinsonism Related Disorders 21, 692–697 (2015).25943529 10.1016/j.parkreldis.2015.04.002
14. Katz M Differential effects of deep brain stimulation target on motor subtypes in Parkinson's disease Ann Neurol. 2015 77 710 719 10.1002/ana.24374 25627340
Katz, M. et al. Differential effects of deep brain stimulation target on motor subtypes in Parkinson’s disease. Ann Neurol. 77, 710–719 (2015).25627340 10.1002/ana.24374
15. Espay A Methylphenidate for gait impairment in Parkinson disease: a randomized clinical trial Neurology 2011 76 1256 1262 10.1212/WNL.0b013e3182143537 21464430
Espay, A. et al. Methylphenidate for gait impairment in Parkinson disease: a randomized clinical trial. Neurology 76, 1256–1262 (2011).21464430 10.1212/WNL.0b013e3182143537
16. Postuma RB MDS clinical diagnostic criteria for Parkinson's disease Movem. Disorders 2015 30 1591 1601 10.1002/mds.26424
Postuma, R. B. et al. MDS clinical diagnostic criteria for Parkinson’s disease. Movem. Disorders 30, 1591–1601 (2015).10.1002/mds.26424
17. Hopes L Magnetic resonance imaging features of the nigrostriatal system: biomarkers of Parkinson’s disease stages? PLoS One 2016 11 e0147947 10.1371/journal.pone.0147947 27035571
Hopes, L. et al. Magnetic resonance imaging features of the nigrostriatal system: biomarkers of Parkinson’s disease stages?. PLoS One 11, e0147947 (2016).27035571 10.1371/journal.pone.0147947
18. Planetta PJ Free-water imaging in Parkinson’s disease and atypical parkinsonism Brain 2016 139 495 508 10.1093/brain/awv361 26705348
Planetta, P. J. et al. Free-water imaging in Parkinson’s disease and atypical parkinsonism. Brain 139, 495–508 (2016).26705348 10.1093/brain/awv361
19. Rosenberg-Katz K Gray matter atrophy distinguishes between Parkinson disease motor subtypes Neurology 2013 80 1476 1484 10.1212/WNL.0b013e31828cfaa4 23516323
Rosenberg-Katz, K. et al. Gray matter atrophy distinguishes between Parkinson disease motor subtypes. Neurology 80, 1476–1484 (2013).23516323 10.1212/WNL.0b013e31828cfaa4
20. Zhang L The neural basis of postural instability gait disorder subtype of Parkinson's disease: a PET and fMRI study CNS Neurosci. Therapeut. 2016 22 360 367 10.1111/cns.12504
Zhang, L. et al. The neural basis of postural instability gait disorder subtype of Parkinson’s disease: a PET and fMRI study. CNS Neurosci. Therapeut. 22, 360–367 (2016).10.1111/cns.12504
21. Lambin P Radiomics: The bridge between medical imaging and personalized medicine Nat. Rev. Clin. Oncol. 2017 14 749 762 10.1038/nrclinonc.2017.141 28975929
Lambin, P. et al. Radiomics: The bridge between medical imaging and personalized medicine. Nat. Rev. Clin. Oncol. 14, 749–762 (2017).28975929 10.1038/nrclinonc.2017.141
22. Chaddad A Desrosiers C Niazi T Deep radiomic analysis of MRI related to Alzheimer’s disease Ieee Access 2018 6 58213 58221 10.1109/ACCESS.2018.2871977
Chaddad, A., Desrosiers, C. & Niazi, T. Deep radiomic analysis of MRI related to Alzheimer’s disease. Ieee Access 6, 58213–58221 (2018).10.1109/ACCESS.2018.2871977
23. Huang Y-Q Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer J. Clin. Oncol. 2016 34 2157 2164 10.1200/JCO.2015.65.9128 27138577
Huang, Y.-Q. et al. Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J. Clin. Oncol. 34, 2157–2164 (2016).27138577 10.1200/JCO.2015.65.9128
24. Hosseini, M. S., Aghamiri, S. M. R., Ardekani, A. F. & BagheriMofidi, S. M. Assessing the stability and discriminative ability of radiomics features in the tumor microenvironment: Leveraging peri-tumoral regions in vestibular schwannoma. Eur. J. Radiol., 111654 (2024).
25. Liu P Wang H Zheng S Zhang F Zhang X Parkinson's disease diagnosis using neostriatum radiomic features based on T2-weighted magnetic resonance imaging Front. Neurol. 2020 11 248 10.3389/fneur.2020.00248 32322236
Liu, P., Wang, H., Zheng, S., Zhang, F. & Zhang, X. Parkinson’s disease diagnosis using neostriatum radiomic features based on T2-weighted magnetic resonance imaging. Front. Neurol. 11, 248 (2020).32322236 10.3389/fneur.2020.00248
26. Choi H Ha S Im HJ Paek SH Lee DS Refining diagnosis of Parkinson's disease with deep learning-based interpretation of dopamine transporter imaging NeuroImage Clin. 2017 16 586 594 10.1016/j.nicl.2017.09.010 28971009
Choi, H., Ha, S., Im, H. J., Paek, S. H. & Lee, D. S. Refining diagnosis of Parkinson’s disease with deep learning-based interpretation of dopamine transporter imaging. NeuroImage Clin. 16, 586–594 (2017).28971009 10.1016/j.nicl.2017.09.010
27. Rahmim A Improved prediction of outcome in Parkinson's disease using radiomics analysis of longitudinal DAT SPECT images NeuroImage Clin. 2017 16 539 544 10.1016/j.nicl.2017.08.021 29868437
Rahmim, A. et al. Improved prediction of outcome in Parkinson’s disease using radiomics analysis of longitudinal DAT SPECT images. NeuroImage Clin. 16, 539–544 (2017).29868437 10.1016/j.nicl.2017.08.021
28. Xiao B Quantitative susceptibility mapping based hybrid feature extraction for diagnosis of Parkinson's disease NeuroImage Clin. 2019 24 102070 10.1016/j.nicl.2019.102070 31734535
Xiao, B. et al. Quantitative susceptibility mapping based hybrid feature extraction for diagnosis of Parkinson’s disease. NeuroImage Clin. 24, 102070 (2019).31734535 10.1016/j.nicl.2019.102070
29. Bian J Wang X Hao W Zhang G Wang Y The differential diagnosis value of radiomics-based machine learning in Parkinson’s disease: a systematic review and meta-analysis Front. Aging Neurosci. 2023 15 1199826 10.3389/fnagi.2023.1199826 37484694
Bian, J., Wang, X., Hao, W., Zhang, G. & Wang, Y. The differential diagnosis value of radiomics-based machine learning in Parkinson’s disease: a systematic review and meta-analysis. Front. Aging Neurosci. 15, 1199826 (2023).37484694 10.3389/fnagi.2023.1199826
30. Feng, J. et al. Research and application progress of radiomics in neurodegenerative diseases. Meta-Radiol. 100068 (2024).
31. Shu Z An integrative nomogram for identifying early-stage Parkinson's disease using non-motor symptoms and white matter-based radiomics biomarkers from whole-brain MRI Front. Aging Neurosci. 2020 12 548616 10.3389/fnagi.2020.548616 33390927
Shu, Z. et al. An integrative nomogram for identifying early-stage Parkinson’s disease using non-motor symptoms and white matter-based radiomics biomarkers from whole-brain MRI. Front. Aging Neurosci. 12, 548616 (2020).33390927 10.3389/fnagi.2020.548616
32. Sun D Differentiating Parkinson’s disease motor subtypes: a radiomics analysis based on deep gray nuclear lesion and white matter Neurosci. Lett. 2021 760 136083 10.1016/j.neulet.2021.136083 34174346
Sun, D. et al. Differentiating Parkinson’s disease motor subtypes: a radiomics analysis based on deep gray nuclear lesion and white matter. Neurosci. Lett. 760, 136083 (2021).34174346 10.1016/j.neulet.2021.136083
33. Bu S Multi-parametric radiomics of conventional T1 weighted and susceptibility-weighted imaging for differential diagnosis of idiopathic Parkinson’s disease and multiple system atrophy BMC Med. Imaging 2023 23 204 10.1186/s12880-023-01169-1 38066432
Bu, S. et al. Multi-parametric radiomics of conventional T1 weighted and susceptibility-weighted imaging for differential diagnosis of idiopathic Parkinson’s disease and multiple system atrophy. BMC Med. Imaging 23, 204 (2023).38066432 10.1186/s12880-023-01169-1
34. Stebbins GT How to identify tremor dominant and postural instability/gait difficulty groups with the movement disorder society unified Parkinson's disease rating scale: comparison with the unified Parkinson's disease rating scale Movem. Disorders 2013 28 668 670 10.1002/mds.25383
Stebbins, G. T. et al. How to identify tremor dominant and postural instability/gait difficulty groups with the movement disorder society unified Parkinson’s disease rating scale: comparison with the unified Parkinson’s disease rating scale. Movem. Disorders 28, 668–670 (2013).10.1002/mds.25383
35. Jenkinson M Beckmann CF Behrens TE Woolrich MW Smith SM Fsl Neuroimage 2012 62 782 790 10.1016/j.neuroimage.2011.09.015 21979382
Jenkinson, M., Beckmann, C. F., Behrens, T. E., Woolrich, M. W. & Smith, S. M. Fsl. Neuroimage 62, 782–790 (2012).21979382 10.1016/j.neuroimage.2011.09.015
36. Smith, S. M. BET: Brain extraction tool. FMRIB TR00SMS2b, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain), Department of Clinical Neurology, Oxford University, John Radcliffe Hospital, Headington, UK (2000).
37. Zhang Y Brady M Smith S Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm IEEE Trans. Med. Imaging 2001 20 45 57 10.1109/42.906424 11293691
Zhang, Y., Brady, M. & Smith, S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. Imaging 20, 45–57 (2001).11293691 10.1109/42.906424
38. Andersson JL Skare S Ashburner J How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging Neuroimage 2003 20 870 888 10.1016/S1053-8119(03)00336-7 14568458
Andersson, J. L., Skare, S. & Ashburner, J. How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. Neuroimage 20, 870–888 (2003).14568458 10.1016/S1053-8119(03)00336-7
39. Andersson JL Sotiropoulos SN An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging Neuroimage 2016 125 1063 1078 10.1016/j.neuroimage.2015.10.019 26481672
Andersson, J. L. & Sotiropoulos, S. N. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage 125, 1063–1078 (2016).26481672 10.1016/j.neuroimage.2015.10.019
40. Behrens TE Characterization and propagation of uncertainty in diffusion-weighted MR imaging Magn. Resonance Med. 2003 50 1077 1088 10.1002/mrm.10609
Behrens, T. E. et al. Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magn. Resonance Med. 50, 1077–1088 (2003).10.1002/mrm.10609
41. Makris N Decreased volume of left and total anterior insular lobule in schizophrenia Schizophrenia Res. 2006 83 155 171 10.1016/j.schres.2005.11.020
Makris, N. et al. Decreased volume of left and total anterior insular lobule in schizophrenia. Schizophrenia Res. 83, 155–171 (2006).10.1016/j.schres.2005.11.020
42. Frazier JA Structural brain magnetic resonance imaging of limbic and thalamic volumes in pediatric bipolar disorder Am. J. Psychiat. 2005 162 1256 1265 10.1176/appi.ajp.162.7.1256 15994707
Frazier, J. A. et al. Structural brain magnetic resonance imaging of limbic and thalamic volumes in pediatric bipolar disorder. Am. J. Psychiat. 162, 1256–1265 (2005).15994707 10.1176/appi.ajp.162.7.1256
43. Desikan RS An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest Neuroimage 2006 31 968 980 10.1016/j.neuroimage.2006.01.021 16530430
Desikan, R. S. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31, 968–980 (2006).16530430 10.1016/j.neuroimage.2006.01.021
44. Goldstein JM Hypothalamic abnormalities in schizophrenia: sex effects and genetic vulnerability Biol. Psychiat. 2007 61 935 945 10.1016/j.biopsych.2006.06.027 17046727
Goldstein, J. M. et al. Hypothalamic abnormalities in schizophrenia: sex effects and genetic vulnerability. Biol. Psychiat. 61, 935–945 (2007).17046727 10.1016/j.biopsych.2006.06.027
45. Keuken MC Forstmann BU A probabilistic atlas of the basal ganglia using 7 T MRI Data Brief 2015 4 577 582 10.1016/j.dib.2015.07.028 26322322
Keuken, M. C. & Forstmann, B. U. A probabilistic atlas of the basal ganglia using 7 T MRI. Data Brief 4, 577–582 (2015).26322322 10.1016/j.dib.2015.07.028
46. Jenkinson M Bannister P Brady M Smith S Improved optimization for the robust and accurate linear registration and motion correction of brain images Neuroimage 2002 17 825 841 10.1006/nimg.2002.1132 12377157
Jenkinson, M., Bannister, P., Brady, M. & Smith, S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17, 825–841 (2002).12377157 10.1006/nimg.2002.1132
47. Andersson JL Jenkinson M Smith S Non-linear registration, aka Spatial normalisation FMRIB technical report TR07JA2 FMRIB Anal. Group Univ. Oxford 2007 2 1 22
Andersson, J. L., Jenkinson, M. & Smith, S. Non-linear registration, aka Spatial normalisation FMRIB technical report TR07JA2. FMRIB Anal. Group Univ. Oxford 2, 1–22 (2007).
48. Van Griethuysen JJ Computational radiomics system to decode the radiographic phenotype Cancer Res. 2017 77 e104 e107 10.1158/0008-5472.CAN-17-0339 29092951
Van Griethuysen, J. J. et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 77, e104–e107 (2017).29092951 10.1158/0008-5472.CAN-17-0339
49. Johnson WE Li C Rabinovic A Adjusting batch effects in microarray expression data using empirical Bayes methods Biostatistics 2007 8 118 127 10.1093/biostatistics/kxj037 16632515
Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).16632515 10.1093/biostatistics/kxj037
50. Guyon I Elisseeff A An introduction to variable and feature selection J. Mach. Learn. Res. 2003 3 1157 1182
Guyon, I. & Elisseeff, A. An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003).
51. Tibshirani R Regression shrinkage and selection via the lasso J. R. Stat. Soc. Ser. B Stat. Methodol. 1996 58 267 288 10.1111/j.2517-6161.1996.tb02080.x
Tibshirani, R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B Stat. Methodol. 58, 267–288 (1996).10.1111/j.2517-6161.1996.tb02080.x
52. Jeyakodi, G., Pal, A., Gupta, D., Sarukeswari, K. & Amouda, V. Machine learning approach for cancer entities association and classification. arXiv preprint arXiv:2306.00013 (2023).
53. Stamatakis, E. Exploiting compressed sensing in distributed machine learning. (2023).
54. Breiman L Random forests Mach. Learn. 2001 45 5 32 10.1023/A:1010933404324
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).10.1023/A:1010933404324
55. Quinlan JR Induction of decision trees Mach. Learn. 1986 1 81 106 10.1007/BF00116251
Quinlan, J. R. Induction of decision trees. Mach. Learn. 1, 81–106 (1986).10.1007/BF00116251
56. Ke G Lightgbm: A highly efficient gradient boosting decision tree Adv. Neural Inf. Process. Syst. 2017 30 3147
Ke, G. et al. Lightgbm: A highly efficient gradient boosting decision tree. Adv. Neural Inf. Process. Syst. 30, 3147 (2017).
57. Freund Y Schapire RE A decision-theoretic generalization of on-line learning and an application to boosting J. Comput. Syst. Sci. 1997 55 119 139 10.1006/jcss.1997.1504
Freund, Y. & Schapire, R. E. A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55, 119–139 (1997).10.1006/jcss.1997.1504
58. Kocak B CheckList for EvaluAtion of Radiomics research (CLEAR): A step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII Insights Imaging 2023 14 75 10.1186/s13244-023-01415-8 37142815
Kocak, B. et al. CheckList for EvaluAtion of Radiomics research (CLEAR): A step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII. Insights Imaging 14, 75 (2023).37142815 10.1186/s13244-023-01415-8
