Opportunistic rotator cuff tear screening from routine chest CT: a deep learning-radiomics hybrid model with multi-center validation

Opportunistic rotator cuff tear screening from routine chest CT: a deep learning-radiomics hybrid model with multi-center validation | 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 Article Opportunistic rotator cuff tear screening from routine chest CT: a deep learning-radiomics hybrid model with multi-center validation Yufeng Wang, Jianning Lin, Mei Kong, Junhai Yan, Suyan Tian, Junjie Xu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8876993/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 Rotator cuff tears (RCT) constitute the predominant etiology of shoulder dysfunction among middle-aged and elderly populations yet remain substantially underdiagnosed in community settings and routine health screenings owing to the prohibitive cost of magnetic resonance imaging. Chest computed tomography (CT), as a ubiquitously accessible imaging modality in contemporary clinical practice, furnishes a natural data substrate for opportunistic RCT screening. Notwithstanding, real-world implementation confronts formidable challenges encompassing suboptimal soft tissue contrast, incomplete anatomical coverage, and pervasive equipment heterogeneity. Methods We developed a fully automated, opportunistic screening system validated across a diverse multi-center cohort (N = 1,442) from national, municipal, and county-level hospitals. To overcome imaging variations, we implemented a rigorous preprocessing pipeline incorporating ComBat harmonization to minimize cross-center batch effects. The core architecture features a gated attention-based multiple instance learning (Attn-MIL) network to extract deep representations from 3D CT patches. These were synergistically fused with interpretable radiomic features quantifying muscle compensation and osseous degeneration. Results In the primary national-center cohort, the hybrid model yielded excellent diagnostic discrimination (AUC, 0.956; 95% CI, 0.943–0.969). Across highly heterogeneous real-world external validation cohorts, the model exhibited robust generalizability: AUC attained 0.893 at the municipal center and 0.858 at the county center. Subgroup analyses confirmed model robustness across divergent body habitus, scanner manufacturers and acquisition protocols. Notably, the model maintained high precision in identifying early-stage pathology, validating its sensitivity for occult injury screening. Interpretability analyses further corroborated that the model correctly captured kinetic chain reorganization patterns secondary to RCT. Conclusions This study establishes the clinical feasibility of opportunistic RCT screening using routine chest CT. By effectively neutralizing batch effects and leveraging deep feature fusion, our system enables accurate, zero-cost risk stratification without additional radiation, facilitating proactive population health management. Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Health sciences/Health care Physical sciences/Mathematics and computing Health sciences/Medical research Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Rotator cuff tears (RCT) represent the leading cause of shoulder pain and functional impairment among middle-aged and elderly populations 1 , 2 . As a musculotendinous complex comprising the supraspinatus, infraspinatus, subscapularis, and teres minor, the rotator cuff stabilizes the glenohumeral joint and facilitates upper limb mobility. Epidemiological studies indicate that 22.1% of the general population harbors rotator cuff tears, yet only one-third of affected individuals develop overt clinical symptoms 3 , 4 . Consequently, a large proportion of early-stage RCTs remain undetected until progression to larger tears, at which point irreversible muscle atrophy and fatty infiltration have already occurred, severely compromising treatment outcomes 5 , 6 . These observations underscore a critical unmet need for early identification and risk stratification of RCT in the general population. Despite this imperative, population-level screening for RCT remains unattainable in current clinical practice, primarily due to two interlocking factors. First, shoulder-specific imaging has not been incorporated into routine population-based health screening programs 7 . Consequently, a vast majority of asymptomatic or minimally symptomatic patients with early-stage injuries remain unidentified, often eluding clinical detection until their injuries progress to advanced tears carrying poor prognoses. Second, magnetic resonance imaging (MRI), the diagnostic reference standard, proves impractical for large-scale screening of asymptomatic individuals given its prohibitive temporal and financial burden 1 . This systematic underdiagnosis highlights a critical diagnostic gap, necessitating the development of alternative screening strategies that exploit existing imaging resources without imposing additional costs, radiation exposure, or workflow burdens 8 . Against this backdrop, opportunistic screening harnessing routine CT and artificial intelligence has achieved substantive breakthroughs across multiple clinical domains 9 . Notable implementations include the use of Hounsfield units (HU) as an efficacious surrogate for MRI-based hepatic steatosis screening, and automated bone mineral density quantification derived from thoracic CT for osteoporosis risk assessment 10 – 14 . Chest CT, specifically, is one of the most frequently performed imaging examinations worldwide, rendering it an attractive data source for opportunistic musculoskeletal screening. However, despite this ubiquity, no framework currently exists to harness such data for RCT screening which is a critical missed opportunity for secondary prevention 15 – 17 . This absence reflects fundamental technical challenges: compared with MRI, conventional CT provides inferior soft-tissue contrast, and routine chest CT protocols often incompletely visualize key rotator cuff tendons due to arm positioning and limited field of view, rendering direct morphological assessment unreliable 18 – 20 . To address these limitations, this study proposes an innovative diagnostic strategy predicated on systemic musculoskeletal degeneration. We propose that rotator cuff injury has deep pathophysiological linkages to both osseous degeneration and systemic sarcopenia rather than being an isolated local lesion. Accordingly, our work exceeds the restrictions of specific anatomical sites and creates a prediction model from three biologically connected dimensions: (1)macroscopic biomechanical compensation and adaptive reconfiguration in trunk musculature within the imaging field of view after rotator cuff dysfunction 21 , 22 ; (2) microscopic density and texture heterogeneity within rotator cuff muscles due to fatty infiltration 5 ; and (3) bone loss signatures in periarticular and trunk regions 23 – 25 . By combining localized disease signals with systemic background, our approach eliminates excessive reliance on single tendon imaging and significantly improves model resilience and diagnostic efficacy across diverse clinical datasets. We hypothesized that rotator cuff tears are not isolated local events but are associated with systemic musculoskeletal reorganization that can be captured as surrogate signatures on standard thoracic imaging. To test this, we developed a synergistic AI architecture utilizing feature-level fusion to harness the complementary strengths of deep learning and radiomics. specifically, we combined attention-based multiple instance learning (Attn-MIL) to extract high-dimensional representations with clinically interpretable radiomic features to overcome the limitations of soft-tissue contrast. This study validates the performance and generalizability of this hybrid model across multi-center cohorts, establishing a feasible pathway for opportunistic screening using repurposed radiological data without incremental costs or radiation exposure. Methods Study design and participant cohort This multicenter study employed a hybrid retrospective-prospective design and was conducted in strict accordance with the Declaration of Helsinki. Ethical approval was obtained from the Ethics Committees of Shanghai Sixth People’s Hospital (Primary Center, IRB No. 2019-KY-033(K)), Shengli Oilfield Central Hospital of Dongying (IRB No. YXLL202517201), and Pingyu County People’s Hospital (IRB No. PYCPH-2025-003). The study population comprised three independent cohorts. For both the prospective and retrospective cohorts, consecutive sampling was employed. The primary derivation cohort was prospectively enrolled from Shanghai Sixth People’s Hospital between June 2019 and March 2025. To evaluate the generalizability of our findings, two independent external validation cohorts were retrospectively collected from Shengli Oilfield Central Hospital of Dongying (February 2023 to June 2025) and Pingyu County People’s Hospital (September 2022 to August 2025). Written informed consent was obtained from all prospectively enrolled participants, while the requirement for informed consent was waived for the retrospective cohorts. Inclusion and Exclusion Criteria Patients were eligible for inclusion if they met the following criteria: (1) age between 50 and 85 years; (2) underwent routine chest CT examinations for non-rotator cuff indications (e.g., lung screening); (3) CT scan range fully encompassed the anatomy from the thoracic inlet to the superior border of the first lumbar vertebra (L1), ensuring complete visualization of the pectoralis major and upper body musculature for reliable segmentation; and (4) underwent shoulder MRI or arthroscopic exploration within 7 days of the CT acquisition to ensure temporal synchronization. An overview of the entire study design is presented in Fig. 1 . Patients were excluded if they met any of the following criteria: (1) Confounding Shoulder Pathologies: History of significant trauma, prior surgery, fractures, malignancy, infectious lesions, or severe osteoarthritis on the ipsilateral shoulder. (2) Image Quality Limitations: Suboptimal chest CT or shoulder MRI quality due to severe motion artifacts, metallic implants, or other noise that precluded accurate muscle segmentation or tendon integrity assessment. (3) Neuromuscular Comorbidities: Diagnosed history of neuromuscular diseases capable of systemically affecting muscle quality (e.g., amyotrophic lateral sclerosis, myasthenia gravis). (4) Systemic Wasting Conditions: Presence of active malignancy or cachexia. In the prospective primary cohort, to minimize selection bias and strictly control for potential confounding factors, Propensity Score Matching (PSM) was implemented. Given that muscle mass and quality are inherently influenced by physiological profiles, patients with confirmed rotator cuff tears (Tear Group) were matched 1:1 with those having intact rotator cuffs (Control Group). Matching was performed using a nearest-neighbor algorithm with a caliper width of 0.02. The covariates for matching included age, height and weight. This process was essential to construct a baseline demographically and anthropometrically balanced analysis set, ensuring that any observed differences in muscle metrics were attributable to pathological changes rather than disparities in body size or age-related physiological decline. Reference standard and validation of automated workflow reliability This study defined bilateral shoulder MRI diagnosis or arthroscopic surgical findings as the composite reference standard for rotator cuff tears. Imaging assessment for non-surgical subjects was independently executed by two radiologists with more than 5 years of subspecialty experience in a double-blinded fashion, with diagnostic discordances adjudicated by a third senior expert. The detailed protocol for inter-reader reliability assessment is provided in Supplementary Method S1 and Supplementary Figures S7–S8 . To ensure the reliability of the automated processing workflow, we deployed a dual verification paradigm. First, skeletal landmark localization accuracy was assessed via Dice similarity coefficient (DSC) in 150 randomly selected samples. Subsequently, 40 representative samples stratified by DSC performance were reviewed by two experts using a 5-point Likert scale to evaluate the anatomical coverage completeness of the functional compensation zone. Statistical evaluation employed weighted Kappa coefficient for inter-observer agreement and Bland-Altman analysis for systematic bias assessment. Detailed validation results are presented in Supplementary Method S2 and Supplementary Figure S9 . Preprocessing To address imaging heterogeneity across multi-center and multi-device sources, all native CT images were resampled to a standardized 1.0×1.0×1.0 mm³ isotropic voxel space. We implemented intensity clipping (− 200 to 800 HU) followed by linear normalization to [0, 1] to minimize noise from calcifications and metallic artifacts while targeting muscle tissue characteristics (detailed preprocessing protocols are provided in Supplementary Method S3 ). Scans with severe artifacts were excluded. Following preprocessing, we utilized the TotalSegmentator V2 framework to automatically segment 14 key anatomical structures, comprising rotator cuff muscles, major trunk muscles, and skeletal landmarks ( Supplementary Figure S1 ) 26 . To accommodate significant variability in scan ranges and patient positioning, we developed a dynamic localization algorithm based on stable anatomical anchors. This approach established a standardized coordinate system, ensuring that sampled patches consistently encompassed the core functional regions of the shoulder girdle for subsequent feature extraction. Gated Attention Multiple Instance Learning To address the challenge of weakly supervised learning, we engineered an attention-weighted multiple instance learning framework. In this architecture, each subject’s bilateral shoulder girdle is formulated as a bag containing a collection of 3D image patches centered on the predefined anatomical anchors. A 3D convolutional neural network (CNN) serves as the feature encoder, projecting voxel signals into a latent feature space. To aggregate these instance-level embeddings into a patient-level representation, we implemented a gated attention mechanism. This mechanism enables the model to autonomously assign higher diagnostic importance (attention weights) to spatial regions exhibiting rotator cuff pathology, without requiring fine-grained local annotations. The resulting aggregated feature vector represents the global pathological signature of the subject. Detailed protocols are provided in Supplementary Method S3 . Multi-center quantitative feature extraction, feature fusion, and comparative model analysis To comprehensively evaluate diagnostic performance, we designed a comparative analysis framework encompassing three distinct modeling paradigms: a deep learning model (Attn-MIL), a radiomics-driven machine learning model (ML), and a deep learning-machine learning hybrid model. For the radiomics approach, quantitative features were extracted from the 14 anatomical ROIs strictly adhering to IBSI guidelines, with multi-center batch effects corrected via the ComBat method to ensure data harmonization. The hybrid model integrated deep representations ( \(\:{\text{X}}_{\text{D}\text{L}}\) ) with optimal radiomic signatures ( \(\:{\text{X}}_{\text{R}\text{a}\text{d}}\) ). All models underwent rigorous validation using stratified 10-fold cross-validation, with performance differences assessed via DeLong tests. Furthermore, to bridge the gap between black-box predictions and clinical intuition, model decision logic was elucidated using SHAP analysis for tabular features and Gradient-weighted Spatial Attention Mapping (Grad-SAM) for 3D spatial interpretability (detailed experimental settings, hyperparameter configurations, and mathematical formulations are provided in Supplementary Method 3 ). Statistical analysis The primary evaluation metric was the AUC with 95% CIs. For baseline characteristics, Continuous variables with normal distribution were expressed as mean ± standard deviation and compared using independent samples t-tests. Non-normally distributed variables were expressed as median (IQR) and compared using the Mann-Whitney U test. Categorical variables were reported as frequencies and percentages, with intergroup comparisons utilizing Pearson chi-square test or Fisher exact test. Optimal cutoff values were determined via the Youden index to compute sensitivity, specificity, positive predictive value, and negative predictive value. Concordance between model-predicted risk and observed outcomes was evaluated utilizing calibration curves, while clinical net benefit was quantified through decision curve analysis. Differences in AUCs between model architectures (e.g., Hybrid vs. DL) and across clinical subgroups (e.g., manufacturers, slice thicknesses) were evaluated using the DeLong test. Deep learning model training and inference were executed in a server environment configured with Python v3.10, PyTorch v2.1.2, and CUDA 11.8 (Ubuntu 22.04). Subsequent statistical analyses and visualization were performed locally using Python v3.12.7 and R v4.2, with a two-sided P < 0.05 considered statistically significant. Results Participant baseline characteristics and propensity score matching analysis This study initially screened 1,487 subjects across three centers. Following exclusion of 45 subjects owing to insufficient field of view (FOV) coverage or severe imaging artifacts, 1,442 subjects entered subsequent analysis. The primary center initially encompassed 776 control subjects and 485 rotator cuff tear (RCT) patients. As delineated in Supplementary Table S1 , prior to propensity score matching, the two groups manifested statistically significant differences in baseline metrics including height (P < .001, SMD = − 0.312) and weight (P < .001, SMD = − 0.276), potentially reflecting latent selection bias inherent to clinical recruitment. Given that demographic indicators such as age, height, weight and BMI may engender confounding effects on core observational metrics including muscle volume, cross-sectional area, and fatty infiltration degree, 1:1 propensity score matching was implemented to ensure the authenticity and reliability of study results. Following matching, the primary center cohort ultimately comprised 947 subjects, encompassing 485 RCT patients and 462 successfully matched control subjects (23 RCT patients were retained as unmatched owing to absence of suitable controls within the preset caliper). Following PSM correction, baseline differences between groups were effectively obviated: age (61.00 vs 61.00 years, P = .923, SMD = − 0.028), height (P = .679, SMD = − 0.031), weight (P = .893, SMD = − 0.009), and body mass index (BMI, 24.38 vs 24.50 kg/m², P = .902, SMD = − 0.008) all manifested high equilibrium between groups. As delineated in Supplementary Table S1 and Supplementary Figure S2 , all covariates exhibited standardized mean differences (SMD) significantly attenuated below the 0.10 threshold following matching, corroborating statistical comparability between case and control groups in demographic background and further bolstering the rigor of study conclusions. Demographic data of finally enrolled patients are presented in Table 1 . To ensure geographic and clinical generalizability, our model was externally validated using multicenter cohorts from Dongying (n = 131) and Pingyu (n = 50). While the primary center cohort was characterized by an older, predominantly male population (median age 61.00; 59.81% male), the external sites introduced significant demographic variability, including a younger median age (55.00 years) in the Pingyu center and a lower male ratio (50.38%) in Dongying (Table 1 ). Furthermore, the inclusion of the entire spectrum of Goutallier grading (Grades 0–4) across all sites—particularly the substantial representation of Grades 1 and 3 in the primary cohort—underscores the model's exposure to varying degrees of rotator cuff pathology. Such diversity in sample distribution is critical for affirming the model’s reliability in real-world clinical settings. Automated segmentation algorithm validation The automated analysis pipeline based on TotalSegmentator demonstrated exceptional anatomical robustness in processing highly heterogeneous routine chest CT images. The algorithm’s segmentation performance remained stable across 14 predefined anatomical regions of interest (ROIs) within both the primary center’s discovery cohort and the external validation set. Quantitative evaluation on 150 randomly selected samples revealed that the fully automated workflow achieved a median Dice Similarity Coefficient (DSC) of 0.91 (IQR, 0.89–0.94). Specifically, the algorithm demonstrated robust performance across skeletal landmarks, with a median DSC of 0.93 for the humerus and 0.90 for the scapula, validating the model’s high voxel-level segmentation consistency. To address common challenges in routine chest scans, such as postural shifts and field-of-view (FOV) truncation, the dynamic sampling strategy—which utilizes lung apex coordinates and the spatial centroids of the clavicle-scapula complex—exhibited robust generalizability. As illustrated in Supplementary Figure S1 , the pipeline Table 1 Patient demographics and baseline clinical characteristics. Comparisons of demographic and clinical variables across the Model Development Cohort (n = 947, matched) and two Independent External Validation Cohorts (Dongying n = 131, Pingyu n = 50). Continuous variables are presented as median (IQR) or mean ± SD, and categorical variables as number (%). Abbreviations : IQR interquartile range, SD standard deviation. Model development cohort (N = 947) Independent testing cohorts (N = 181) Dongying (N = 131) Pingyu (n = 50) Age (years) 61.00 (55.00, 67.00) 61.00 (54.00, 70.00) 55 (52.00, 63.50) Sex (Male,%) 570 (59.81%) 66 (50.38%) 27 (54.00%) Height (cm) 162.00 (157.00, 168.00) 166.28 ± 7.11 168.00 (159.00, 175.00) Weight (kg) 65.00 (57.00, 73.00) 66.00 (53.50, 79.50) 68.00 (58.00, 81.50) BMI (kg/m²) 24.38 (22.30, 26.54) 24.16 ± 5.35 24.61 ± 5.92 Goutallier Grade 0 117 29 7 1 329 48 16 2 291 30 14 3 189 16 8 4 21 9 5 successfully achieved precise localization of core anatomical regions in the bilateral shoulder girdles. Even under extreme conditions wherein severe muscle wasting (Goutallier Grade 3–4) precipitated blurring of tissue boundaries, the algorithm maintained stable ROI localization through constraints imposed by anatomical anchors. This high-precision automated segmentation performance furnished solid technical assurance for subsequent extraction of quantitative structural features with biological consistency (such as CSA, HU, inter alia). Feature selection and model development For feature selection, we employed LASSO regression to reduce the dimensionality of the high-dimensional feature space, which comprised 70 structural and 256 deep features. To achieve an optimal trade-off between predictive accuracy and model parsimony, we evaluated various candidate subsets (K = 5,8,10,12,15,18) by modulating the regularization parameter (λ). A final subset of 18 key features (K = 18) was selected, as it demonstrated superior predictive performance in the primary discovery cohort while maintaining a favorable feature-to-sample ratio to mitigate the risk of overfitting. As illustrated in the LASSO coefficient path plot (Supplementary Figure S3) , the convergence of features under varying regularization strengths is clearly visualized. The selected 18 features encompass morphological, textural, and density-based metrics of trunk muscles, rotator cuff muscles, and osseous structures, providing a robust foundation for subsequent classification model development. Following determination of the optimal feature subset size, systematic horizontal performance benchmarking was executed across multiple machine learning architectures. Quantitative analysis revealed that under the K = 18 feature configuration, gradient boosted decision tree algorithms exhibited significantly superior discrimination for diagnosing rotator cuff tears compared to linear models and ensemble forest algorithms, indicating their ability to capture non-linear interactions across heterogeneous feature types (Table 2 ). In the fused feature space, XGBoost achieved the numerically highest median AUC (0.9517), while LightGBM (0.9514) and CatBoost Table 2 Performance comparison of machine learning algorithms in the development cohort. Evaluation of seven candidate classifiers (XGBoost, LightGBM, CatBoost, Random Forest, SVM, Logistic Regression, Linear Regression) utilizing the hybrid feature signature. Performance metrics include AUC, Accuracy, Sensitivity, and Specificity. The XGBoost classifier demonstrated the optimal balance of predictive performance and computational efficiency. Abbreviations : AUC area under the curve, SVM support vector machine. Model K AUC XGBoost 18 0.9516802784844024 LightGBM 18 0.9514169679118133 CatBoost 18 0.9506761279957157 LightGBM 12 0.9502476904538759 CatBoost 12 0.9487035301468291 XGBoost 12 0.9486365867809167 Linear Regression 18 0.9483286472977194 Random Forest 12 0.9478421921720891 Random Forest 18 0.9473959030660062 LightGBM 8 0.9442317133038782 Random Forest 8 0.9441759271656179 XGBoost 8 0.9440308832061409 CatBoost 8 0.9439103851474986 Random Forest 10 0.9433592181014862 Random Forest 5 0.9423773820681037 LightGBM 10 0.9421252287231668 Logistic Regression 18 0.9415405899941983 XGBoost 10 0.941098763779176 CatBoost 10 0.9396483241844067 CatBoost 5 0.9372249743383765 XGBoost 5 0.9371357165171597 LightGBM 5 0.9353996518944971 Logistic Regression 12 0.9341678939617085 Linear Regression 12 0.9333556477886374 Linear Regression 10 0.9262507252197973 Linear Regression 8 0.9214218770919803 Logistic Regression 10 0.9188066229303342 Logistic Regression 8 0.9156915249698756 Linear Regression 5 0.9149551479448387 SVM 18 0.9147074574909626 SVM 5 0.9109385459900923 SVM 8 0.910295889677333 SVM 10 0.9082228767795779 SVM 12 0.9077341902084171 Logistic Regression 5 0.906533672513054 (0.9507) yielded highly comparable discrimination, suggesting that differences among leading boosting models were marginal in this setting. For the final end-to-end screening workflow, the decision model was selected based not only on point-estimate AUC in the primary cohort but also on robustness considerations essential for multi-center translation. We therefore chose CatBoost as the core classifier because it demonstrated more stable fold-to-fold performance under cross-validation and class imbalance, and improved tolerance to residual cross-center feature distribution shifts after harmonization, resulting in more consistent generalization behavior in heterogeneous external cohorts. This stability-oriented selection strategy was adopted to prioritize reliable real-world inference over marginal AUC differences. Hybrid model performance evaluation and multi-center external validation In the hybrid feature space integrating quantitative structural features and deep learning representations, the multimodal fusion model (Hybrid Model) manifested excellent diagnostic performance. As illustrated in Fig. 2 , this Catboost-based model attained a median AUC of 0.956 (95% CI, 0.943–0.969) in the primary center validation set. In comparison, single-modality models exhibited relatively inferior performance, with the machine learning model (ML Model) predicated on 18 features achieving a median AUC of 0.9507, while the pure deep learning model (DL Model) attained a comparatively lower median AUC. To further quantify performance differences between models, DeLong tests were executed. Statistical analysis (as delineated in Fig. 2 ) revealed that the hybrid model exhibited superior diagnostic precision over the standalone DL framework (ΔAUC, 0.047; P < 0.001), substantiating that introducing quantitative muscle structural features plays a pivotal role in augmenting screening accuracy. Additionally, the machine learning model also manifested a trend of outperforming the deep learning model (P < 0.001). Notably, although the hybrid model's AUC numerically exceeded that of the single machine learning model, the statistical difference between the two did not reach significance (P = 0.35), indicating that quantitative structural features contributed the primary discriminative weight in the current prediction task, while deep features served important roles in performance enhancement and robustness supplementation. In cross-center generalization performance validation, the hybrid model maintained high stability across two independent external validation cohorts (Fig. 3 ). Despite significant heterogeneity in imaging acquisition equipment and scan slice thickness between the Dongying center (N = 131) and Pingyu center (N = 50), the model attained an AUC of 0.893 (95% CI, 0.829–0.945) at the Dongying center and 0.858 (95% CI, 0.725–0.967) at the Pingyu center. Confusion matrix analysis revealed that the model exhibited excellent sensitivity for rotator cuff tears of varying severity while maintaining high specificity. These results compellingly demonstrate that through ComBat harmonization algorithm and feature-level fusion strategy, this screening workflow can effectively surmount imaging heterogeneity in real clinical scenarios, possessing the potential for large-scale opportunistic screening across multi-tier healthcare institutions. Subgroup analysis and model robustness validation To further validate the stable performance of the Hybrid model across disparate demographic characteristics and clinical imaging acquisition protocols, we executed comprehensive subgroup analyses, as delineated in Table 3 . Across age subgroups, the model manifested high predictive consistency, with AUCs of 0.965 for the 50–59 years population, 0.962 for the 60–69 years population, and 0.944 for the older 70–79 years cohort. These results corroborate the algorithm's applicability for screening aging populations with varying degrees of anatomical degeneration. In analyses targeting body habitus and anatomical variation, the model demonstrated robust adaptability. Within BMI subgroups, the model performed optimally in populations with a BMI of 24-27.9 kg/m2 (AUC = 0.972) and 18.5–23.9 kg/m2 (AUC = 0.954). Although performance exhibited slight fluctuation in obese subjects (BMI≥28kg/m2), likely due to increased soft tissue attenuation and physical noise, the AUC remained stable at 0.934. Furthermore, stratified analyses by height and weight—as well as sex—confirmed model robustness across disparate phenotypes, with AUCs reaching 0.970 for subjects weighing 70–79 kg.A critical finding was the model's sensitivity to disease severity. As shown in Table 3 , the model achieved an outstanding AUC of 0.984 in identifying early-stage pathology (Normal/Mild Goutallier Grade 0–1). For patients with moderate fatty infiltration (Goutallier Grade 2), the model maintained satisfactory Table 3 | Subgroup analysis of diagnostic performance. Diagnostic metrics of the Hybrid Model stratified by clinical covariates: Age groups, Height and Weight groups, BMI categories, Goutallier Grades and Tube Voltage (kVp). Data are presented with 95% CIs. The model maintained high AUCs across all subgroups, confirming robustness in opportunistic screening settings. Abbreviations : AUC area under the curve, AUPRC area under the precision-recall curve, PPV positive predictive value, NPV negative predictive value. discriminative power with an AUC of 0.940. Sensitivity analyses for clinical acquisition parameters revealed strong generalization capabilities across mainstream scanning protocols. Standardized 120 kVp and 100 kVp low-dose protocols attained high diagnostic performance (AUC = 0.973 and 0.931, respectively). Finally, as illustrated in Supplementary Figure S4 , the model's cross-vendor generalizability was evaluated across scanners from four major manufacturers: GE, UIH, Siemens, and Philips. While the ComBat harmonization algorithm was implemented to mitigate non-biological biases, the model demonstrated varying yet functional performance levels across different hardware platforms. This consistent performance across diverse clinical, demographic, and hardware parameters strongly substantiates the portability and large-scale deployment potential of this screening tool within real-world, multi-tier healthcare systems. Model interpretability analysis and biological hypothesis validation To elucidate the internal logic underlying the hybrid model’s prediction of rotator cuff tears, we implemented a multi-level interpretability analysis, progressing from global feature importance to local spatial attention. In the global feature contribution dimension, the SHAP interpretability framework was employed to quantify the decision weights of both quantitative structural features and the aggregated 256-dimensional deep features ( Fig. 4 a ) . The results revealed that mean CT values and texture features, representing muscle mass and spatial heterogeneity respectively, occupied the dominant positions in the hybrid model's decision-making process. Specifically, the model identified Infraspinatus density as the most critical predictor, followed by features related to the triceps brachii and pectoralis minor, suggesting a heavy reliance on quantitative tissue quality over mere geometric shape. Complementing this global quantitative perspective, we further explored the model's visual focus in the spatial dimension using the Grad-SAM heatmap analysis. As illustrated in Fig. 4 b,c, for RCT patients confirmed by the reference standard, the high-weight attention regions autonomously and consistently localized to the core shoulder girdle muscle groups. Through anatomical tracing of these heatmaps, we discerned that the model not only focused on in situ atrophic changes in rotator cuff muscles (e.g., supraspinatus and infraspinatus) but also manifested significant spatial responsiveness to compensatory regions, encompassing trunk motor muscle groups such as the latissimus dorsi and trapezius. This spatial weight distribution pattern exhibits high concordance with the clinical biomechanical patterns of muscle degeneration and kinetic chain imbalance induced by RCT To corroborate the biological plausibility of the model’s decision-making process, we conducted a comprehensive univariate analysis on the training set. A total of 55 features demonstrated statistically significant differences between the rotator cuff tear (RCT) and control groups (all P < 0.05; detailed in Supplementary Table S2 and visualized in Supplementary Figure S5 ). These features were categorized into three clinical dimensions: rotator cuff degeneration, synergistic trunk muscle compensation, and skeletal mineral density. In the rotator cuff dimension, RCT patients exhibited widespread muscle atrophy and fatty infiltration. Infraspinatus density—identified as the top predictor in SHAP analysis—was significantly lower in the RCT group compared to controls (P < 0.001), accompanied by significant decreases in supraspinatus and teres minor densities. Concurrently, significantly elevated texture entropy in these muscles suggested increased spatial heterogeneity and microstructural disruption associated with chronic tears. Regarding trunk muscle compensation, we observed inverse regulatory patterns compared to the rotator cuff. Patients in the RCT group exhibited significantly higher density and lower entropy in the triceps brachii and pectoralis minor (P < 0.05), suggesting compensatory muscular hypertrophy and enhanced structural homogeneity due to synergistic recruitment. Latissimus dorsi density also trended higher, further substantiating the model’s ability to capture the functional redistribution of the shoulder girdle.Finally, in the osteoporosis-related dimension, significant bone density reductions were observed in the RCT group across the scapula, clavicle, and proximal humerus (P < 0.01). These findings provide direct data support for the known association between rotator cuff pathology and systemic-local bone mineral density loss. Additionally, correlation analysis between deep learning features and clinical quantitative metrics further enhanced model semantic transparency. As illustrated in the correlation bubble plot in Fig. 5 , among the 256-dimensional deep features generated by multiple instance learning, key factors exhibited significant Spearman rank correlation (ρ > 0.6) with muscle texture contrast and entropy. This consistency substantiates that the deep learning model constructed in this study does not merely execute superficial data fitting but genuinely captures microscopic pathological signatures refractory to visual quantification, with its output features essentially corresponding to the evolutionary processes of intramuscular lipid infiltration and fibrosis. Integrating spatial heatmaps, feature weights, and biological statistical differences (Fig. 5 and Supplementary Figure S6 ), this study not only corroborates the perceptual capability of routine chest CT for rotator cuff pathological status but also furnishes direct digital imaging evidence for the compensatory hypothesis of functional reorganization secondary to rotator cuff injury. Discussion This study validated the clinical feasibility of opportunistic RCT screening using routine chest CT by leveraging a synergistic deep learning-radiomics architecture. The main findings of this study were threefold. First, the automated hybrid model achieved high diagnostic discrimination in the primary center, yielding an AUC of 0.956 (95% CI, 0.943–0.969). Second, the model demonstrated robust generalizability across heterogeneous external validation cohorts, maintaining AUCs of 0.893 and 0.858 despite variations in scanner manufacturers and slice thicknesses. Third, the system exhibited high sensitivity for identifying early-stage injuries (Goutallier Grade 0–1, AUC = 0.984), confirming its potential for secondary prevention. Collectively, these results establish that identifying occult RCTs is achievable through the repurposing of existing radiological data without incurring incremental examination costs or additional radiation exposure. While MRI remains the diagnostic reference standard, its implementation in population-based screening is fundamentally restricted by substantial financial costs and acquisition time. On routine chest CT, direct visualization of rotator cuff tendons is frequently compromised by limited soft-tissue contrast. Although recent advances in dual-energy CT have demonstrated potential for the physical enhancement of tendon conspicuity, such hardware-dependent solutions lack universal accessibility in primary care settings 27 , 28 . Addressing this limitation, this study establishes a diagnostic paradigm that circumvents the reliance on direct morphological delineation. Instead of pursuing physical visualization of the local lesion, the proposed framework strategically exploits the systemic sequelae of the pathology. By synergizing intramuscular texture heterogeneity with kinetic chain compensatory remodeling, the model captures the multidimensional footprint of rotator cuff failure. The statistical superiority of the hybrid model over the standalone deep learning model (P < 0.001) empirically validates this combinatorial strategy. These results demonstrate that even when the primary anatomical disruption is indistinct, secondary physiological adaptations provide robust diagnostic signals, thereby offering a scalable pathway for opportunistic screening on standard imaging platforms. The SHAP interpretability analysis further elucidates the biological logic of our model, validating the efficacy of secondary compensatory mechanisms as robust surrogate markers for rotator cuff pathology. First, regarding the rotator cuff itself, Infraspinatus density was identified as the most critical predictor, exhibiting a statistically significant reduction in the tear group (P < 0.001). This confirms the expected pathological trajectory of fatty infiltration and muscle atrophy 2 , 29 . Second, we observed a distinct inverse pattern in synergistic muscles. The density of the latissimus dorsi, triceps brachii, and pectoralis minor was significantly higher in the tear group (P < 0.001). This aligns with the biomechanical principle of force couple rebalancing 30 , 31 . Following the functional failure of the rotator cuff, these synergistic stabilizers undergo compensatory hypertrophy and recruitment to preserve essential upper limb adduction and depression. Finally, regarding osseous adaptation, profound bone density loss was confirmed in the scapula and proximal humerus (P < 0.001). This finding is consistent with Wolff’s law 32 , 33 . Chronic pain-induced immobilization reduces mechanical loading on the greater tuberosity, precipitating local trabecular rarefaction. By keenly capturing these stable, statistically significant secondary signatures, our model effectively circumvents the limitations of direct tendon visualization such as artifacts and field-of-view truncation thereby ensuring high diagnostic specificity. The clinical translational value of this study resides in the model's high scalability and diagnostic precision, which directly addresses the implementation barriers of population-based screening. Unlike traditional radiomics pipelines that rely on labor-intensive manual segmentation, our fully automated framework achieved a median Dice Similarity Coefficient of 0.91 for key anatomical structures. This high anatomical localization accuracy eliminates the need for manual intervention, enabling the high-throughput processing of large-scale datasets that would be infeasible with human readers. Based on this automated workflow, the model functions as a cost-effective gatekeeper for stratified imaging. Specifically, quantitative analysis confirmed its robust discrimination for early-stage pathology (Goutallier Grade 0–1, AUC = 0.984) and maintained high sensitivity even in the asymptomatic subgroup (AUC > 0.85). These results indicate that the system can effectively identify high-risk individuals from the general population who truly require confirmatory MRI. By repurposing existing CT data without incremental examination costs, this workflow shifts the diagnostic window to the latent stage of disease, optimizing public health resource allocation for secondary prevention. Our study entails several limitations. First, we strictly defined the time window between CT and MRI to within seven days. This criterion was necessary to obviate confounding effects from muscle atrophy, though it constrained the eligible sample size for external validation. Second, radiomic texture features are theoretically sensitive to scanning parameters. Although we implemented the ComBat harmonization algorithm to mitigate non-biological biases, future validation on larger-scale data remains warranted. Third, we did not establish reader control experiments. This is because radiologists do not proactively assess rotator cuff injuries on chest CT in routine practice. The objective of this study is to augment clinical attention rather than supplant manual diagnosis. Finally, refined stratified analysis of tear severity was not executed. Different tendon involvement patterns may correspond to disparate compensatory phenotypes. This represents an important direction for future investigation. Conclusion In conclusion, this study establishes the clinical feasibility of opportunistic RCT screening leveraging routine chest CT. The proposed system transcends the traditional paradigm of local lesion visualization. Instead, it relies on systematic musculoskeletal compensation and osseous degeneration. Our multi-center validation confirms its robustness across healthcare institutions of varying tiers. This risk-stratification paradigm provides a practical digital solution for early intervention. Ultimately, it informs the judicious allocation of public health resources through the repurposing of existing radiological data. Declarations Data availability The datasets generated and/or analysed during the current study are not publicly available due to the presence of patient identifiable information and strict ethical restrictions regarding clinical imaging data but are available from the corresponding author on reasonable request. Code availability The underlying code for this study is not publicly available but may be made available to qualified researchers on reasonable request from the corresponding author. Author contributions Y.W. and J.L. contributed equally to this work. Y.W. and J.L. conceived the study, designed the experiments, and drafted the original manuscript. Y.W. developed the deep learning architecture, performed the radiomic feature extraction, and executed the statistical analyses. J.L. coordinated the data collection at the primary center, performed the radiological annotations, and oversaw the image quality control. M.K. and J.Y. were responsible for data acquisition and curation for the external validation cohorts at Shengli Oilfield Central Hospital and Pingyu County People’s Hospital, respectively. S.T. provided expertise in clinical research design and validated the statistical methodology. J.X. participated in the reference standard validation (reader study) and clinical data interpretation. C.C. , H.L. , and J.Z. supervised the entire project, secured funding, and critically revised the manuscript for important intellectual content. All authors read and approved the final manuscript. Fundings This work was supported by National Natural Science Foundation of China (No. 82272579 and No. 82402827); China Postdoctoral Science Foundation (No. 2024M762039); The Shanghai Guidelines for the Application of the 2025 Annual High-quality Development Plan for Science and Technology Industry-Innovative Development of Biomedicine Project (No. 25S11907600); National Natural Science Foundation of China (No. 82504492); Natural Science Foundation of Jiangsu Higher Education Institutions of China (No. 25KJB320011); Suzhou Science and Technology Bureau project (No. SYW2025180); Young Elite Scientists Sponsorship Program of Jiangsu Province (No. JSTJ-2025-421); Suzhou Medical College-QiLu Medical Research Program of Soochow University (No. 24QL200114). Competing interests The authors declare no competing interests. References Morimoto, T., Izumi, M., Ozaki, K., Sasanuma, H. & Ikeuchi, M. 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Construction of musculoskeletal quantitative model based on deep learning and study of musculoskeletal relationship in patients with osteoporosis. J Adv Res (2026). Westerhoff, M. et al. Deep learning-based opportunistic ct osteoporosis screening and the establishment of normative values. Radiology 317, e250917 (2025). Pickhardt, P.J. et al. Opportunistic screening for osteoporosis using abdominal computed tomography scans obtained for other indications. Ann Intern Med 158, 588–595 (2013). Pan, Y. et al. Automatic opportunistic osteoporosis screening using low-dose chest computed tomography scans obtained for lung cancer screening. Eur Radiol 30, 4107–4116 (2020). Pickhardt, P.J. et al. Population-based opportunistic osteoporosis screening: Validation of a fully automated CT tool for assessing longitudinal BMD changes. Br J Radiol 92, 20180726 (2019). Wu, Y. et al. Artificial intelligence assisted automatic screening of opportunistic osteoporosis in computed tomography images from different scanners. Eur Radiol 35, 2287–2295 (2025). Seitz, A.L., McClure, P.W., Finucane, S., Boardman, N.D., 3rd & Michener, L.A. Mechanisms of rotator cuff tendinopathy: intrinsic, extrinsic, or both? Clin Biomech (Bristol) 26, 1–12 (2011). Roger, B. et al. Imaging findings in the dominant shoulder of throwing athletes: comparison of radiography, arthrography, CT arthrography, and MR arthrography with arthroscopic correlation. AJR Am J Roentgenol 172, 1371–1380 (1999). Omoumi, P. et al. Evaluation of rotator cuff tendon tears: comparison of multidetector CT arthrography and 1.5-T MR arthrography. Radiology 264, 812–822 (2012). Croci, E. et al. Load-induced increase in muscle activity during 30° abduction in patients with rotator cuff tears and control subjects. J Orthop Traumatol 24, 41 (2023). Yu, X.S., Zhu, H. & Griffin, L. Fatigue-related changes in intermuscular electromyographic coherence across rotator cuff and deltoid muscles in individuals with and without subacromial pain. J Neurophysiol 132, 617–627 (2024). Liu, Y. et al. Association between osteoporosis and rotator cuff tears: evidence from causal inference and colocalization analyses. Bone Res 13, 75 (2025). Hong, J.P. et al. Osteoporosis increases the risk of rotator cuff tears: a population-based cohort study. J Bone Miner Metab 40, 348–356 (2022). Kara, M. et al. The relationship among probable sarcopenia, osteoporosis and supraspinatus tendon tears in postmenopausal women: The sarcosp study. Calcif Tissue Int 114, 340–347 (2024). Wasserthal, J. et al. Totalsegmentator: Robust segmentation of 104 anatomic structures in ct images. Radiol Artif Intell 5, e230024 (2023). Liu, S. et al. Diagnostic performance of dual-energy CT for opportunistic detection of rotator cuff disease: a retrospective multireader study. Insights Imaging 16, 231 (2025). Liu, S. et al. Diagnostic value of dual-energy CT virtual monochromatic imaging for supraspinatus tendon injuries: a comparison with standard CT and MRI. Eur Radiol 35, 7877–7887 (2025). Hawkes, D.H. et al. Shoulder muscle activation and coordination in patients with a massive rotator cuff tear: an electromyographic study. J Orthop Res 30, 1140–1146 (2012). Spall, P., Ribeiro, D.C. & Sole, G. Electromyographic activity of shoulder girdle muscles in patients with symptomatic and asymptomatic rotator cuff tears: A systematic review and meta-analysis. Pm r 8, 894–906 (2016). Ciampi, P., Mancini, N., Peretti, G. & Fraschini, G. Muscular compensation in patient with massive rotator cuff tear: Superficial electromyography study. Orthopaedic Proceedings 91-B, 464–464 (2009). Wang, L., You, X., Zhang, L., Zhang, C. & Zou, W. Mechanical regulation of bone remodeling. Bone Res 10, 16 (2022). Pataky, J. et al. Glenohumeral joint loading is impacted by rotator cuff tear severity during functional task performance. Clin Biomech (Bristol) 90, 105494 (2021). Ilse, M., Tomczak, J. & Welling, M. in International conference on machine learning 2127–2136 (PMLR, 2018). van Griethuysen, J.J.M. et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res 77, e104-e107 (2017). Fortin, J.P. et al. Harmonization of cortical thickness measurements across scanners and sites. Neuroimage 167, 104–120 (2018). Lundberg, S.M. & Lee, S.-I. in Proceedings of the 31st International Conference on Neural Information Processing Systems 4768–4777 (Curran Associates Inc., Long Beach, California, USA; 2017). Barkan, O. et al. in Proceedings of the 30th ACM International Conference on Information & Knowledge Management 2882–2887 (2021). Additional Declarations No competing interests reported. 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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-8876993","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":616197531,"identity":"b4303118-b04e-4850-9e5f-131f7ae34f46","order_by":0,"name":"Yufeng Wang","email":"","orcid":"","institution":"Shanghai Sixth People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yufeng","middleName":"","lastName":"Wang","suffix":""},{"id":616197532,"identity":"c4e59487-09f9-43da-985a-c1d94a72914e","order_by":1,"name":"Jianning Lin","email":"","orcid":"","institution":"Shanghai Sixth People's 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05:08:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8876993/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8876993/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106403219,"identity":"f224382d-264d-4808-b254-ca05d5d5d89c","added_by":"auto","created_at":"2026-04-08 09:13:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1182114,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy design and architectural framework.\u003c/strong\u003e \u003cstrong\u003ea\u003c/strong\u003e Three-tier recruitment strategy across the National center (Development cohort, n=1,287), Municipal center (External Validation 1, Dongying, n=131), and County-level center (External Validation 2, Pingyu, n=50). \u003cstrong\u003eb\u003c/strong\u003e Dual-stream feature concatenation architecture integrating the Radiomics path (70 features from 14 ROIs) and the Deep Learning path (Attn-MIL network). \u003cstrong\u003ec\u003c/strong\u003e Multicenter performance evaluation via internal and external validation metrics. \u003cstrong\u003ed\u003c/strong\u003e Interpretability workflow utilizing SHAP and Grad-SAM to reveal biomechanical compensation patterns. \u003cstrong\u003eAbbreviations: \u003c/strong\u003eROI region of interest, Attn-MIL gated attention-based multiple instance learning, SHAP SHapley Additive exPlanations, Grad-SAM gradient-weighted semantically-guided activation mapping. (figure was created in Biorender.com)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8876993/v1/9e10e07b4a0db90bcdef6f11.png"},{"id":106307373,"identity":"a48f1630-f441-4ce3-845d-1d9a37d8d657","added_by":"auto","created_at":"2026-04-07 10:04:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":603673,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiagnostic performance and clinical utility assessment of the screening models.\u003c/strong\u003e \u003cstrong\u003ea\u003c/strong\u003e ROC curves comparing the discriminative efficacy of DL, ML, and Hybrid Fusion models. \u003cstrong\u003eb\u003c/strong\u003eCalibration curves evaluating the reliability of predicted probabilities. \u003cstrong\u003ec\u003c/strong\u003ePRC illustrating model performance in the imbalanced dataset. \u003cstrong\u003ed\u003c/strong\u003e DCA quantifying the clinical net benefit across threshold probabilities. \u003cstrong\u003ee\u003c/strong\u003ePairwise statistical comparison of AUCs using the DeLong test; the heatmap displays the P-values for each model comparison. All results represent aggregated data from 10-fold cross-validation. \u003cstrong\u003eAbbreviations:\u003c/strong\u003e AUC, area under the receiver operating characteristic curve; AUPRC, area under the precision-recall curve; DCA, decision curve analysis; DL, deep learning; Hybrid, hybrid fusion model; ML, machine learning (radiomics); PRC, precision-recall curve; RCT, rotator cuff tear; ROC, receiver operating characteristic.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8876993/v1/b1724edd0152022dd36da56d.png"},{"id":106402895,"identity":"3cf01533-b13b-419e-b412-3ef31aa742e1","added_by":"auto","created_at":"2026-04-08 09:13:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":365546,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGeneralizability assessment in heterogeneous external validation cohorts.\u003c/strong\u003e \u003cstrong\u003ea, b\u003c/strong\u003e ROC curves and Precision-Recall curves demonstrating the model’s discrimination ability in External Center 1(AUC = 0.893; AUPRC = 0.853) and External Center 2 (AUC = 0.858; AUPRC = 0.798). \u003cstrong\u003ec, d\u003c/strong\u003e Confusion matrices for the Hybrid model in the prediction of rotator cuff tears in External Center. ROC receiver operating characteristic, AUC area under the curve.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8876993/v1/f910c767ca9ec1436e97dabb.png"},{"id":106307371,"identity":"8bcb08ee-d7d7-4efc-a469-296aa9976f8c","added_by":"auto","created_at":"2026-04-07 10:04:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":610118,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFeature attribution and multi-planar clinical visualization of the Hybrid Fusion model.\u003c/strong\u003e \u003cstrong\u003ea\u003c/strong\u003e SHAP summary plot ranks the top 18 features by global importance; Infraspinatus density and Trapezius entropy emerge as the most influential predictors, with color denoting feature value (red = high, blue = low). \u003cstrong\u003eb, c \u003c/strong\u003eRepresentative axial (b) and coronal (c) CT images demonstrating precise anatomical localization and clinical application of the diagnostic pipeline in a sample patient.. \u003cstrong\u003eAbbreviations:\u003c/strong\u003e SHAP, SHapley Additive exPlanations; CT, computed tomography; CSA, cross-sectional area; T1 Vert., T1 vertebra; Pec. Minor, pectoralis minor.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8876993/v1/8dd7eda4ffcd50792f68dc8f.png"},{"id":106404761,"identity":"fb565b08-eb59-4e4b-8e1e-7ff9c333329c","added_by":"auto","created_at":"2026-04-08 09:17:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":309350,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSemantic mapping of deep learning and radiomic features.\u003c/strong\u003e \u003cstrong\u003ea\u003c/strong\u003e A correlation bubble plot visualizing the associations between abstract deep learning (DL) features (y-axis) and interpretable radiomic features (x-axis) (red = positive; blue = negative). \u003cstrong\u003eb\u003c/strong\u003e Heatmap displaying the quantitative correlation values for the top 10 strongest DL-radiomics pairs identified in the analysis.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8876993/v1/67056d265e37a08b8ffd1ad0.png"},{"id":108492980,"identity":"6e0cc7c6-827d-4ffb-8fdd-9f54b97beb1a","added_by":"auto","created_at":"2026-05-05 09:59:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3660589,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8876993/v1/58c2f814-f774-47ac-b788-efe53eeef829.pdf"},{"id":106307370,"identity":"57cb05d1-f045-476f-bcf7-6c419a41b248","added_by":"auto","created_at":"2026-04-07 10:04:52","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":5504225,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-8876993/v1/4a22adb7b2d551e221406194.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Opportunistic rotator cuff tear screening from routine chest CT: a deep learning-radiomics hybrid model with multi-center validation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRotator cuff tears (RCT) represent the leading cause of shoulder pain and functional impairment among middle-aged and elderly populations\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. As a musculotendinous complex comprising the supraspinatus, infraspinatus, subscapularis, and teres minor, the rotator cuff stabilizes the glenohumeral joint and facilitates upper limb mobility. Epidemiological studies indicate that 22.1% of the general population harbors rotator cuff tears, yet only one-third of affected individuals develop overt clinical symptoms\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Consequently, a large proportion of early-stage RCTs remain undetected until progression to larger tears, at which point irreversible muscle atrophy and fatty infiltration have already occurred, severely compromising treatment outcomes\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. These observations underscore a critical unmet need for early identification and risk stratification of RCT in the general population.\u003c/p\u003e \u003cp\u003eDespite this imperative, population-level screening for RCT remains unattainable in current clinical practice, primarily due to two interlocking factors. First, shoulder-specific imaging has not been incorporated into routine population-based health screening programs\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Consequently, a vast majority of asymptomatic or minimally symptomatic patients with early-stage injuries remain unidentified, often eluding clinical detection until their injuries progress to advanced tears carrying poor prognoses. Second, magnetic resonance imaging (MRI), the diagnostic reference standard, proves impractical for large-scale screening of asymptomatic individuals given its prohibitive temporal and financial burden\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. This systematic underdiagnosis highlights a critical diagnostic gap, necessitating the development of alternative screening strategies that exploit existing imaging resources without imposing additional costs, radiation exposure, or workflow burdens\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Against this backdrop, opportunistic screening harnessing routine CT and artificial intelligence has achieved substantive breakthroughs across multiple clinical domains\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Notable implementations include the use of Hounsfield units (HU) as an efficacious surrogate for MRI-based hepatic steatosis screening, and automated bone mineral density quantification derived from thoracic CT for osteoporosis risk assessment\u003csup\u003e\u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Chest CT, specifically, is one of the most frequently performed imaging examinations worldwide, rendering it an attractive data source for opportunistic musculoskeletal screening. However, despite this ubiquity, no framework currently exists to harness such data for RCT screening which is a critical missed opportunity for secondary prevention\u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. This absence reflects fundamental technical challenges: compared with MRI, conventional CT provides inferior soft-tissue contrast, and routine chest CT protocols often incompletely visualize key rotator cuff tendons due to arm positioning and limited field of view, rendering direct morphological assessment unreliable\u003csup\u003e\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo address these limitations, this study proposes an innovative diagnostic strategy predicated on systemic musculoskeletal degeneration. We propose that rotator cuff injury has deep pathophysiological linkages to both osseous degeneration and systemic sarcopenia rather than being an isolated local lesion. Accordingly, our work exceeds the restrictions of specific anatomical sites and creates a prediction model from three biologically connected dimensions: (1)macroscopic biomechanical compensation and adaptive reconfiguration in trunk musculature within the imaging field of view after rotator cuff dysfunction\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e; (2) microscopic density and texture heterogeneity within rotator cuff muscles due to fatty infiltration\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e; and (3) bone loss signatures in periarticular and trunk regions\u003csup\u003e\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. By combining localized disease signals with systemic background, our approach eliminates excessive reliance on single tendon imaging and significantly improves model resilience and diagnostic efficacy across diverse clinical datasets.\u003c/p\u003e \u003cp\u003eWe hypothesized that rotator cuff tears are not isolated local events but are associated with systemic musculoskeletal reorganization that can be captured as surrogate signatures on standard thoracic imaging. To test this, we developed a synergistic AI architecture utilizing feature-level fusion to harness the complementary strengths of deep learning and radiomics. specifically, we combined attention-based multiple instance learning (Attn-MIL) to extract high-dimensional representations with clinically interpretable radiomic features to overcome the limitations of soft-tissue contrast. This study validates the performance and generalizability of this hybrid model across multi-center cohorts, establishing a feasible pathway for opportunistic screening using repurposed radiological data without incremental costs or radiation exposure.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003eStudy design and participant cohort\u003c/h2\u003e\n\u003cp\u003eThis multicenter study employed a hybrid retrospective-prospective design and was conducted in strict accordance with the Declaration of Helsinki. Ethical approval was obtained from the Ethics Committees of Shanghai Sixth People\u0026rsquo;s Hospital (Primary Center, IRB No. 2019-KY-033(K)), Shengli Oilfield Central Hospital of Dongying (IRB No. YXLL202517201), and Pingyu County People\u0026rsquo;s Hospital (IRB No. PYCPH-2025-003). The study population comprised three independent cohorts. For both the prospective and retrospective cohorts, consecutive sampling was employed. The primary derivation cohort was prospectively enrolled from Shanghai Sixth People\u0026rsquo;s Hospital between June 2019 and March 2025. To evaluate the generalizability of our findings, two independent external validation cohorts were retrospectively collected from Shengli Oilfield Central Hospital of Dongying (February 2023 to June 2025) and Pingyu County People\u0026rsquo;s Hospital (September 2022 to August 2025). Written informed consent was obtained from all prospectively enrolled participants, while the requirement for informed consent was waived for the retrospective cohorts.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eInclusion and Exclusion Criteria\u003c/h3\u003e\n\u003cp\u003ePatients were eligible for inclusion if they met the following criteria: (1) age between 50 and 85 years; (2) underwent routine chest CT examinations for non-rotator cuff indications (e.g., lung screening); (3) CT scan range fully encompassed the anatomy from the thoracic inlet to the superior border of the first lumbar vertebra (L1), ensuring complete visualization of the pectoralis major and upper body musculature for reliable segmentation; and (4) underwent shoulder MRI or arthroscopic exploration within 7 days of the CT acquisition to ensure temporal synchronization. An overview of the entire study design is presented in \u003cstrong\u003eFig.\u0026nbsp;1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003ePatients were excluded if they met any of the following criteria: (1) Confounding Shoulder Pathologies: History of significant trauma, prior surgery, fractures, malignancy, infectious lesions, or severe osteoarthritis on the ipsilateral shoulder. (2) Image Quality Limitations: Suboptimal chest CT or shoulder MRI quality due to severe motion artifacts, metallic implants, or other noise that precluded accurate muscle segmentation or tendon integrity assessment. (3) Neuromuscular Comorbidities: Diagnosed history of neuromuscular diseases capable of systemically affecting muscle quality (e.g., amyotrophic lateral sclerosis, myasthenia gravis). (4) Systemic Wasting Conditions: Presence of active malignancy or cachexia.\u003c/p\u003e\n\u003cp\u003eIn the prospective primary cohort, to minimize selection bias and strictly control for potential confounding factors, Propensity Score Matching (PSM) was implemented. Given that muscle mass and quality are inherently influenced by physiological profiles, patients with confirmed rotator cuff tears (Tear Group) were matched 1:1 with those having intact rotator cuffs (Control Group). Matching was performed using a nearest-neighbor algorithm with a caliper width of 0.02. The covariates for matching included age, height and weight. This process was essential to construct a baseline demographically and anthropometrically balanced analysis set, ensuring that any observed differences in muscle metrics were attributable to pathological changes rather than disparities in body size or age-related physiological decline.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReference standard and validation of automated workflow reliability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study defined bilateral shoulder MRI diagnosis or arthroscopic surgical findings as the composite reference standard for rotator cuff tears. Imaging assessment for non-surgical subjects was independently executed by two radiologists with more than 5 years of subspecialty experience in a double-blinded fashion, with diagnostic discordances adjudicated by a third senior expert. The detailed protocol for inter-reader reliability assessment is provided in \u003cstrong\u003eSupplementary Method S1\u003c/strong\u003e and \u003cstrong\u003eSupplementary Figures S7\u0026ndash;S8\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eTo ensure the reliability of the automated processing workflow, we deployed a dual verification paradigm. First, skeletal landmark localization accuracy was assessed via Dice similarity coefficient (DSC) in 150 randomly selected samples. Subsequently, 40 representative samples stratified by DSC performance were reviewed by two experts using a 5-point Likert scale to evaluate the anatomical coverage completeness of the functional compensation zone. Statistical evaluation employed weighted Kappa coefficient for inter-observer agreement and Bland-Altman analysis for systematic bias assessment. Detailed validation results are presented in \u003cstrong\u003eSupplementary Method S2\u003c/strong\u003e and \u003cstrong\u003eSupplementary Figure S9\u003c/strong\u003e.\u003c/p\u003e\n\u003ch3\u003ePreprocessing\u003c/h3\u003e\n\u003cp\u003eTo address imaging heterogeneity across multi-center and multi-device sources, all native CT images were resampled to a standardized 1.0\u0026times;1.0\u0026times;1.0 mm\u0026sup3; isotropic voxel space. We implemented intensity clipping (\u0026minus;\u0026thinsp;200 to 800 HU) followed by linear normalization to [0, 1] to minimize noise from calcifications and metallic artifacts while targeting muscle tissue characteristics (detailed preprocessing protocols are provided in \u003cstrong\u003eSupplementary Method S3\u003c/strong\u003e). Scans with severe artifacts were excluded.\u003c/p\u003e\n\u003cp\u003eFollowing preprocessing, we utilized the TotalSegmentator V2 framework to automatically segment 14 key anatomical structures, comprising rotator cuff muscles, major trunk muscles, and skeletal landmarks (\u003cstrong\u003eSupplementary Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. To accommodate significant variability in scan ranges and patient positioning, we developed a dynamic localization algorithm based on stable anatomical anchors. This approach established a standardized coordinate system, ensuring that sampled patches consistently encompassed the core functional regions of the shoulder girdle for subsequent feature extraction.\u003c/p\u003e\n\u003ch3\u003eGated Attention Multiple Instance Learning\u003c/h3\u003e\n\u003cp\u003eTo address the challenge of weakly supervised learning, we engineered an attention-weighted multiple instance learning framework. In this architecture, each subject\u0026rsquo;s bilateral shoulder girdle is formulated as a bag containing a collection of 3D image patches centered on the predefined anatomical anchors. A 3D convolutional neural network (CNN) serves as the feature encoder, projecting voxel signals into a latent feature space. To aggregate these instance-level embeddings into a patient-level representation, we implemented a gated attention mechanism. This mechanism enables the model to autonomously assign higher diagnostic importance (attention weights) to spatial regions exhibiting rotator cuff pathology, without requiring fine-grained local annotations. The resulting aggregated feature vector represents the global pathological signature of the subject. Detailed protocols are provided in \u003cstrong\u003eSupplementary Method S3\u003c/strong\u003e.\u003c/p\u003e\n\u003ch3\u003eMulti-center quantitative feature extraction, feature fusion, and comparative model analysis\u003c/h3\u003e\n\u003cp\u003eTo comprehensively evaluate diagnostic performance, we designed a comparative analysis framework encompassing three distinct modeling paradigms: a deep learning model (Attn-MIL), a radiomics-driven machine learning model (ML), and a deep learning-machine learning hybrid model. For the radiomics approach, quantitative features were extracted from the 14 anatomical ROIs strictly adhering to IBSI guidelines, with multi-center batch effects corrected via the ComBat method to ensure data harmonization. The hybrid model integrated deep representations (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{X}}_{\\text{D}\\text{L}}\\)\u003c/span\u003e\u003c/span\u003e) with optimal radiomic signatures (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{X}}_{\\text{R}\\text{a}\\text{d}}\\)\u003c/span\u003e\u003c/span\u003e). All models underwent rigorous validation using stratified 10-fold cross-validation, with performance differences assessed via DeLong tests. Furthermore, to bridge the gap between black-box predictions and clinical intuition, model decision logic was elucidated using SHAP analysis for tabular features and Gradient-weighted Spatial Attention Mapping (Grad-SAM) for 3D spatial interpretability (detailed experimental settings, hyperparameter configurations, and mathematical formulations are provided in \u003cstrong\u003eSupplementary Method 3\u003c/strong\u003e).\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003eStatistical analysis\u003c/h2\u003e\n\u003cp\u003eThe primary evaluation metric was the AUC with 95% CIs. For baseline characteristics, Continuous variables with normal distribution were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation and compared using independent samples t-tests. Non-normally distributed variables were expressed as median (IQR) and compared using the Mann-Whitney U test. Categorical variables were reported as frequencies and percentages, with intergroup comparisons utilizing Pearson chi-square test or Fisher exact test. Optimal cutoff values were determined via the Youden index to compute sensitivity, specificity, positive predictive value, and negative predictive value. Concordance between model-predicted risk and observed outcomes was evaluated utilizing calibration curves, while clinical net benefit was quantified through decision curve analysis. Differences in AUCs between model architectures (e.g., Hybrid vs. DL) and across clinical subgroups (e.g., manufacturers, slice thicknesses) were evaluated using the DeLong test. Deep learning model training and inference were executed in a server environment configured with Python v3.10, PyTorch v2.1.2, and CUDA 11.8 (Ubuntu 22.04). Subsequent statistical analyses and visualization were performed locally using Python v3.12.7 and R v4.2, with a two-sided P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eParticipant baseline characteristics and propensity score matching analysis\u003c/h2\u003e\n \u003cp\u003eThis study initially screened 1,487 subjects across three centers. Following exclusion of 45 subjects owing to insufficient field of view (FOV) coverage or severe imaging artifacts, 1,442 subjects entered subsequent analysis. The primary center initially encompassed 776 control subjects and 485 rotator cuff tear (RCT) patients. As delineated in \u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e, prior to propensity score matching, the two groups manifested statistically significant differences in baseline metrics including height (P \u0026lt; .001, SMD\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.312) and weight (P \u0026lt; .001, SMD\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.276), potentially reflecting latent selection bias inherent to clinical recruitment.\u003c/p\u003e\n \u003cp\u003eGiven that demographic indicators such as age, height, weight and BMI may engender confounding effects on core observational metrics including muscle volume, cross-sectional area, and fatty infiltration degree, 1:1 propensity score matching was implemented to ensure the authenticity and reliability of study results. Following matching, the primary center cohort ultimately comprised 947 subjects, encompassing 485 RCT patients and 462 successfully matched control subjects (23 RCT patients were retained as unmatched owing to absence of suitable controls within the preset caliper). Following PSM correction, baseline differences between groups were effectively obviated: age (61.00 vs 61.00 years, P = .923, SMD\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.028), height (P = .679, SMD\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.031), weight (P = .893, SMD\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.009), and body mass index (BMI, 24.38 vs 24.50 kg/m\u0026sup2;, P = .902, SMD\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.008) all manifested high equilibrium between groups. As delineated in \u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e and \u003cstrong\u003eSupplementary Figure S2\u003c/strong\u003e, all covariates exhibited standardized mean differences (SMD) significantly attenuated below the 0.10 threshold following matching, corroborating statistical comparability between case and control groups in demographic background and further bolstering the rigor of study conclusions. Demographic data of finally enrolled patients are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eTo ensure geographic and clinical generalizability, our model was externally validated using multicenter cohorts from Dongying (n\u0026thinsp;=\u0026thinsp;131) and Pingyu (n\u0026thinsp;=\u0026thinsp;50). While the primary center cohort was characterized by an older, predominantly male population (median age 61.00; 59.81% male), the external sites introduced significant demographic variability, including a younger median age (55.00 years) in the Pingyu center and a lower male ratio (50.38%) in Dongying (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Furthermore, the inclusion of the entire spectrum of Goutallier grading (Grades 0\u0026ndash;4) across all sites\u0026mdash;particularly the substantial representation of Grades 1 and 3 in the primary cohort\u0026mdash;underscores the model\u0026apos;s exposure to varying degrees of rotator cuff pathology. Such diversity in sample distribution is critical for affirming the model\u0026rsquo;s reliability in real-world clinical settings.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eAutomated segmentation algorithm validation\u003c/h2\u003e\n \u003cp\u003eThe automated analysis pipeline based on TotalSegmentator demonstrated exceptional anatomical robustness in processing highly heterogeneous routine chest CT images. The algorithm\u0026rsquo;s segmentation performance remained stable across 14 predefined anatomical regions of interest (ROIs) within both the primary center\u0026rsquo;s discovery cohort and the external validation set. Quantitative evaluation on 150 randomly selected samples revealed that the fully automated workflow achieved a median Dice Similarity Coefficient (DSC) of 0.91 (IQR, 0.89\u0026ndash;0.94). Specifically, the algorithm demonstrated robust performance across skeletal landmarks, with a median DSC of 0.93 for the humerus and 0.90 for the scapula, validating the model\u0026rsquo;s high voxel-level segmentation consistency. To address common challenges in routine chest scans, such as postural shifts and field-of-view (FOV) truncation, the dynamic sampling strategy\u0026mdash;which utilizes lung apex coordinates and the spatial centroids of the clavicle-scapula complex\u0026mdash;exhibited robust generalizability. As illustrated in \u003cstrong\u003eSupplementary Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e, the pipeline\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatient demographics and baseline clinical characteristics.\u003c/strong\u003e Comparisons of demographic and clinical variables across the Model Development Cohort (n\u0026thinsp;=\u0026thinsp;947, matched) and two Independent External Validation Cohorts (Dongying n\u0026thinsp;=\u0026thinsp;131, Pingyu n\u0026thinsp;=\u0026thinsp;50). Continuous variables are presented as median (IQR) or mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, and categorical variables as number (%). \u003cstrong\u003eAbbreviations\u003c/strong\u003e: IQR interquartile range, SD standard deviation.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eModel development cohort (N\u0026thinsp;=\u0026thinsp;947)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eIndependent testing cohorts (N\u0026thinsp;=\u0026thinsp;181)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDongying (N\u0026thinsp;=\u0026thinsp;131)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePingyu (n\u0026thinsp;=\u0026thinsp;50)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.00 (55.00, 67.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.00 (54.00, 70.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55 (52.00, 63.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eSex (Male,%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e570 (59.81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66 (50.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (54.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eHeight (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e162.00 (157.00, 168.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e166.28\u0026thinsp;\u0026plusmn;\u0026thinsp;7.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168.00 (159.00, 175.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eWeight (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.00 (57.00, 73.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.00 (53.50, 79.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.00 (58.00, 81.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.38 (22.30, 26.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.16\u0026thinsp;\u0026plusmn;\u0026thinsp;5.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.61\u0026thinsp;\u0026plusmn;\u0026thinsp;5.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" align=\"left\"\u003e\n \u003cp\u003eGoutallier Grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003esuccessfully achieved precise localization of core anatomical regions in the bilateral shoulder girdles.\u003c/p\u003e\n \u003cp\u003eEven under extreme conditions wherein severe muscle wasting (Goutallier Grade 3\u0026ndash;4) precipitated blurring of tissue boundaries, the algorithm maintained stable ROI localization through constraints imposed by anatomical anchors. This high-precision automated segmentation performance furnished solid technical assurance for subsequent extraction of quantitative structural features with biological consistency (such as CSA, HU, inter alia).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eFeature selection and model development\u003c/h2\u003e\n \u003cp\u003eFor feature selection, we employed LASSO regression to reduce the dimensionality of the high-dimensional feature space, which comprised 70 structural and 256 deep features. To achieve an optimal trade-off between predictive accuracy and model parsimony, we evaluated various candidate subsets (K\u0026thinsp;=\u0026thinsp;5,8,10,12,15,18) by modulating the regularization parameter (\u0026lambda;). A final subset of 18 key features (K\u0026thinsp;=\u0026thinsp;18) was selected, as it demonstrated superior predictive performance in the primary discovery cohort while maintaining a favorable feature-to-sample ratio to mitigate the risk of overfitting. As illustrated in the LASSO coefficient path plot \u003cstrong\u003e(Supplementary Figure S3)\u003c/strong\u003e, the convergence of features under varying regularization strengths is clearly visualized. The selected 18 features encompass morphological, textural, and density-based metrics of trunk muscles, rotator cuff muscles, and osseous structures, providing a robust foundation for subsequent classification model development.\u003c/p\u003e\n \u003cp\u003eFollowing determination of the optimal feature subset size, systematic horizontal performance benchmarking was executed across multiple machine learning architectures. Quantitative analysis revealed that under the K\u0026thinsp;=\u0026thinsp;18 feature configuration, gradient boosted decision tree algorithms exhibited significantly superior discrimination for diagnosing rotator cuff tears compared to linear models and ensemble forest algorithms, indicating their ability to capture non-linear interactions across heterogeneous feature types (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). In the fused feature space, XGBoost achieved the numerically highest median AUC (0.9517), while LightGBM (0.9514) and CatBoost\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003ePerformance comparison of machine learning algorithms in the development cohort.\u003c/strong\u003e Evaluation of seven candidate classifiers (XGBoost, LightGBM, CatBoost, Random Forest, SVM, Logistic Regression, Linear Regression) utilizing the hybrid feature signature. Performance metrics include AUC, Accuracy, Sensitivity, and Specificity. The XGBoost classifier demonstrated the optimal balance of predictive performance and computational efficiency. \u003cstrong\u003eAbbreviations\u003c/strong\u003e: AUC area under the curve, SVM support vector machine.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eK\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eXGBoost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.9516802784844024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLightGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.9514169679118133\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCatBoost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.9506761279957157\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLightGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.9502476904538759\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCatBoost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.9487035301468291\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eXGBoost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.9486365867809167\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLinear Regression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.9483286472977194\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRandom Forest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.9478421921720891\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRandom Forest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.9473959030660062\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLightGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.9442317133038782\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRandom Forest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.9441759271656179\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eXGBoost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.9440308832061409\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCatBoost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.9439103851474986\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRandom Forest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.9433592181014862\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRandom Forest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.9423773820681037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLightGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.9421252287231668\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLogistic Regression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.9415405899941983\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eXGBoost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.941098763779176\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCatBoost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.9396483241844067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCatBoost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.9372249743383765\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eXGBoost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.9371357165171597\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLightGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.9353996518944971\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLogistic Regression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.9341678939617085\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLinear Regression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.9333556477886374\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLinear Regression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.9262507252197973\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLinear Regression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.9214218770919803\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLogistic Regression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.9188066229303342\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLogistic Regression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.9156915249698756\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLinear Regression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.9149551479448387\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSVM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.9147074574909626\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSVM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.9109385459900923\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSVM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.910295889677333\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSVM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.9082228767795779\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSVM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.9077341902084171\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLogistic Regression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.906533672513054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e(0.9507) yielded highly comparable discrimination, suggesting that differences among leading boosting models were marginal in this setting.\u003c/p\u003e\n \u003cp\u003eFor the final end-to-end screening workflow, the decision model was selected based not only on point-estimate AUC in the primary cohort but also on robustness considerations essential for multi-center translation. We therefore chose CatBoost as the core classifier because it demonstrated more stable fold-to-fold performance under cross-validation and class imbalance, and improved tolerance to residual cross-center feature distribution shifts after harmonization, resulting in more consistent generalization behavior in heterogeneous external cohorts. This stability-oriented selection strategy was adopted to prioritize reliable real-world inference over marginal AUC differences.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eHybrid model performance evaluation and multi-center external validation\u003c/h2\u003e\n \u003cp\u003eIn the hybrid feature space integrating quantitative structural features and deep learning representations, the multimodal fusion model (Hybrid Model) manifested excellent diagnostic performance. As illustrated in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, this Catboost-based model attained a median AUC of 0.956 (95% CI, 0.943\u0026ndash;0.969) in the primary center validation set. In comparison, single-modality models exhibited relatively inferior performance, with the machine learning model (ML Model) predicated on 18 features achieving a median AUC of 0.9507, while the pure deep learning model (DL Model) attained a comparatively lower median AUC. To further quantify performance differences between models, DeLong tests were executed. Statistical analysis (as delineated in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) revealed that the hybrid model exhibited superior diagnostic precision over the standalone DL framework (\u0026Delta;AUC, 0.047; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), substantiating that introducing quantitative muscle structural features plays a pivotal role in augmenting screening accuracy. Additionally, the machine learning model also manifested a trend of outperforming the deep learning model (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, although the hybrid model\u0026apos;s AUC numerically exceeded that of the single machine learning model, the statistical difference between the two did not reach significance (P\u0026thinsp;=\u0026thinsp;0.35), indicating that quantitative structural features contributed the primary discriminative weight in the current prediction task, while deep features served important roles in performance enhancement and robustness supplementation.\u003c/p\u003e\n \u003cp\u003eIn cross-center generalization performance validation, the hybrid model maintained high stability across two independent external validation cohorts (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Despite significant heterogeneity in imaging acquisition equipment and scan slice thickness between the Dongying center (N\u0026thinsp;=\u0026thinsp;131) and Pingyu center (N\u0026thinsp;=\u0026thinsp;50), the model attained an AUC of 0.893 (95% CI, 0.829\u0026ndash;0.945) at the Dongying center and 0.858 (95% CI, 0.725\u0026ndash;0.967) at the Pingyu center. Confusion matrix analysis revealed that the model exhibited excellent sensitivity for rotator cuff tears of varying severity while maintaining high specificity. These results compellingly demonstrate that through ComBat harmonization algorithm and feature-level fusion strategy, this screening workflow can effectively surmount imaging heterogeneity in real clinical scenarios, possessing the potential for large-scale opportunistic screening across multi-tier healthcare institutions.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eSubgroup analysis and model robustness validation\u003c/h2\u003e\n \u003cp\u003eTo further validate the stable performance of the Hybrid model across disparate demographic characteristics and clinical imaging acquisition protocols, we executed comprehensive subgroup analyses, as delineated in \u003cstrong\u003eTable\u0026nbsp;3\u003c/strong\u003e. Across age subgroups, the model manifested high predictive consistency, with AUCs of 0.965 for the 50\u0026ndash;59 years population, 0.962 for the 60\u0026ndash;69 years population, and 0.944 for the older 70\u0026ndash;79 years cohort. These results corroborate the algorithm\u0026apos;s applicability for screening aging populations with varying degrees of anatomical degeneration.\u003c/p\u003e\n \u003cp\u003eIn analyses targeting body habitus and anatomical variation, the model demonstrated robust adaptability. Within BMI subgroups, the model performed optimally in populations with a BMI of 24-27.9 kg/m2 (AUC\u0026thinsp;=\u0026thinsp;0.972) and 18.5\u0026ndash;23.9 kg/m2 (AUC\u0026thinsp;=\u0026thinsp;0.954). Although performance exhibited slight fluctuation in obese subjects (BMI\u0026ge;28kg/m2), likely due to increased soft tissue attenuation and physical noise, the AUC remained stable at 0.934. Furthermore, stratified analyses by height and weight\u0026mdash;as well as sex\u0026mdash;confirmed model robustness across disparate phenotypes, with AUCs reaching 0.970 for subjects weighing 70\u0026ndash;79 kg.A critical finding was the model\u0026apos;s sensitivity to disease severity. As shown in \u003cstrong\u003eTable\u0026nbsp;3\u003c/strong\u003e, the model achieved an outstanding AUC of 0.984 in identifying early-stage pathology (Normal/Mild Goutallier Grade 0\u0026ndash;1). For patients with moderate fatty infiltration (Goutallier Grade 2), the model maintained satisfactory\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3 | Subgroup analysis of diagnostic performance.\u003c/strong\u003e Diagnostic metrics of the Hybrid Model stratified by clinical covariates: Age groups, Height and Weight groups, BMI categories, Goutallier Grades and Tube Voltage (kVp). Data are presented with 95% CIs. The model maintained high AUCs across all subgroups, confirming robustness in opportunistic screening settings. \u003cstrong\u003eAbbreviations\u003c/strong\u003e: AUC area under the curve, AUPRC area under the precision-recall curve, PPV positive predictive value, NPV negative predictive value.\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n \u003cp\u003ediscriminative power with an AUC of 0.940.\u003c/p\u003e\n \u003cp\u003eSensitivity analyses for clinical acquisition parameters revealed strong generalization capabilities across mainstream scanning protocols. Standardized 120 kVp and 100 kVp low-dose protocols attained high diagnostic performance (AUC\u0026thinsp;=\u0026thinsp;0.973 and 0.931, respectively).\u003c/p\u003e\n \u003cp\u003eFinally, as illustrated in \u003cstrong\u003eSupplementary Figure S4\u003c/strong\u003e, the model\u0026apos;s cross-vendor generalizability was evaluated across scanners from four major manufacturers: GE, UIH, Siemens, and Philips. While the ComBat harmonization algorithm was implemented to mitigate non-biological biases, the model demonstrated varying yet functional performance levels across different hardware platforms. This consistent performance across diverse clinical, demographic, and hardware parameters strongly substantiates the portability and large-scale deployment potential of this screening tool within real-world, multi-tier healthcare systems.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eModel interpretability analysis and biological hypothesis validation\u003c/h2\u003e\n \u003cp\u003eTo elucidate the internal logic underlying the hybrid model\u0026rsquo;s prediction of rotator cuff tears, we implemented a multi-level interpretability analysis, progressing from global feature importance to local spatial attention.\u003c/p\u003e\n \u003cp\u003eIn the global feature contribution dimension, the SHAP interpretability framework was employed to quantify the decision weights of both quantitative structural features and the aggregated 256-dimensional deep features \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea\u003cstrong\u003e)\u003c/strong\u003e. The results revealed that mean CT values and texture features, representing muscle mass and spatial heterogeneity respectively, occupied the dominant positions in the hybrid model\u0026apos;s decision-making process. Specifically, the model identified Infraspinatus density as the most critical predictor, followed by features related to the triceps brachii and pectoralis minor, suggesting a heavy reliance on quantitative tissue quality over mere geometric shape.\u003c/p\u003e\n \u003cp\u003eComplementing this global quantitative perspective, we further explored the model\u0026apos;s visual focus in the spatial dimension using the Grad-SAM heatmap analysis. As illustrated in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb,c, for RCT patients confirmed by the reference standard, the high-weight attention regions autonomously and consistently localized to the core shoulder girdle muscle groups. Through anatomical tracing of these heatmaps, we discerned that the model not only focused on in situ atrophic changes in rotator cuff muscles (e.g., supraspinatus and infraspinatus) but also manifested significant spatial responsiveness to compensatory regions, encompassing trunk motor muscle groups such as the latissimus dorsi and trapezius. This spatial weight distribution pattern exhibits high concordance with the clinical biomechanical patterns of muscle degeneration and kinetic chain imbalance induced by RCT\u003c/p\u003e\n \u003cp\u003eTo corroborate the biological plausibility of the model\u0026rsquo;s decision-making process, we conducted a comprehensive univariate analysis on the training set. A total of 55 features demonstrated statistically significant differences between the rotator cuff tear (RCT) and control groups (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; detailed in \u003cstrong\u003eSupplementary Table S2\u003c/strong\u003e and visualized in \u003cstrong\u003eSupplementary Figure S5\u003c/strong\u003e). These features were categorized into three clinical dimensions: rotator cuff degeneration, synergistic trunk muscle compensation, and skeletal mineral density. In the rotator cuff dimension, RCT patients exhibited widespread muscle atrophy and fatty infiltration. Infraspinatus density\u0026mdash;identified as the top predictor in SHAP analysis\u0026mdash;was significantly lower in the RCT group compared to controls (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), accompanied by significant decreases in supraspinatus and teres minor densities. Concurrently, significantly elevated texture entropy in these muscles suggested increased spatial heterogeneity and microstructural disruption associated with chronic tears.\u003c/p\u003e\n \u003cp\u003eRegarding trunk muscle compensation, we observed inverse regulatory patterns compared to the rotator cuff. Patients in the RCT group exhibited significantly higher density and lower entropy in the triceps brachii and pectoralis minor (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting compensatory muscular hypertrophy and enhanced structural homogeneity due to synergistic recruitment. Latissimus dorsi density also trended higher, further substantiating the model\u0026rsquo;s ability to capture the functional redistribution of the shoulder girdle.Finally, in the osteoporosis-related dimension, significant bone density reductions were observed in the RCT group across the scapula, clavicle, and proximal humerus\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). These findings provide direct data support for the known association between rotator cuff pathology and systemic-local bone mineral density loss.\u003c/p\u003e\n \u003cp\u003eAdditionally, correlation analysis between deep learning features and clinical quantitative metrics further enhanced model semantic transparency. As illustrated in the correlation bubble plot in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, among the 256-dimensional deep features generated by multiple instance learning, key factors exhibited significant Spearman rank correlation (\u0026rho;\u0026thinsp;\u0026gt;\u0026thinsp;0.6) with muscle texture contrast and entropy. This consistency substantiates that the deep learning model constructed in this study does not merely execute superficial data fitting but genuinely captures microscopic pathological signatures refractory to visual quantification, with its output features essentially corresponding to the evolutionary processes of intramuscular lipid infiltration and fibrosis. Integrating spatial heatmaps, feature weights, and biological statistical differences (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e \u003cstrong\u003eand Supplementary Figure S6\u003c/strong\u003e), this study not only corroborates the perceptual capability of routine chest CT for rotator cuff pathological status but also furnishes direct digital imaging evidence for the compensatory hypothesis of functional reorganization secondary to rotator cuff injury.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study validated the clinical feasibility of opportunistic RCT screening using routine chest CT by leveraging a synergistic deep learning-radiomics architecture. The main findings of this study were threefold. First, the automated hybrid model achieved high diagnostic discrimination in the primary center, yielding an AUC of 0.956 (95% CI, 0.943\u0026ndash;0.969). Second, the model demonstrated robust generalizability across heterogeneous external validation cohorts, maintaining AUCs of 0.893 and 0.858 despite variations in scanner manufacturers and slice thicknesses. Third, the system exhibited high sensitivity for identifying early-stage injuries (Goutallier Grade 0\u0026ndash;1, AUC\u0026thinsp;=\u0026thinsp;0.984), confirming its potential for secondary prevention. Collectively, these results establish that identifying occult RCTs is achievable through the repurposing of existing radiological data without incurring incremental examination costs or additional radiation exposure.\u003c/p\u003e \u003cp\u003eWhile MRI remains the diagnostic reference standard, its implementation in population-based screening is fundamentally restricted by substantial financial costs and acquisition time. On routine chest CT, direct visualization of rotator cuff tendons is frequently compromised by limited soft-tissue contrast. Although recent advances in dual-energy CT have demonstrated potential for the physical enhancement of tendon conspicuity, such hardware-dependent solutions lack universal accessibility in primary care settings\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Addressing this limitation, this study establishes a diagnostic paradigm that circumvents the reliance on direct morphological delineation. Instead of pursuing physical visualization of the local lesion, the proposed framework strategically exploits the systemic sequelae of the pathology. By synergizing intramuscular texture heterogeneity with kinetic chain compensatory remodeling, the model captures the multidimensional footprint of rotator cuff failure. The statistical superiority of the hybrid model over the standalone deep learning model (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) empirically validates this combinatorial strategy. These results demonstrate that even when the primary anatomical disruption is indistinct, secondary physiological adaptations provide robust diagnostic signals, thereby offering a scalable pathway for opportunistic screening on standard imaging platforms.\u003c/p\u003e \u003cp\u003eThe SHAP interpretability analysis further elucidates the biological logic of our model, validating the efficacy of secondary compensatory mechanisms as robust surrogate markers for rotator cuff pathology. First, regarding the rotator cuff itself, Infraspinatus density was identified as the most critical predictor, exhibiting a statistically significant reduction in the tear group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This confirms the expected pathological trajectory of fatty infiltration and muscle atrophy\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Second, we observed a distinct inverse pattern in synergistic muscles. The density of the latissimus dorsi, triceps brachii, and pectoralis minor was significantly higher in the tear group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This aligns with the biomechanical principle of force couple rebalancing\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Following the functional failure of the rotator cuff, these synergistic stabilizers undergo compensatory hypertrophy and recruitment to preserve essential upper limb adduction and depression. Finally, regarding osseous adaptation, profound bone density loss was confirmed in the scapula and proximal humerus (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This finding is consistent with Wolff\u0026rsquo;s law\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Chronic pain-induced immobilization reduces mechanical loading on the greater tuberosity, precipitating local trabecular rarefaction. By keenly capturing these stable, statistically significant secondary signatures, our model effectively circumvents the limitations of direct tendon visualization such as artifacts and field-of-view truncation thereby ensuring high diagnostic specificity.\u003c/p\u003e \u003cp\u003eThe clinical translational value of this study resides in the model's high scalability and diagnostic precision, which directly addresses the implementation barriers of population-based screening. Unlike traditional radiomics pipelines that rely on labor-intensive manual segmentation, our fully automated framework achieved a median Dice Similarity Coefficient of 0.91 for key anatomical structures. This high anatomical localization accuracy eliminates the need for manual intervention, enabling the high-throughput processing of large-scale datasets that would be infeasible with human readers. Based on this automated workflow, the model functions as a cost-effective gatekeeper for stratified imaging. Specifically, quantitative analysis confirmed its robust discrimination for early-stage pathology (Goutallier Grade 0\u0026ndash;1, AUC\u0026thinsp;=\u0026thinsp;0.984) and maintained high sensitivity even in the asymptomatic subgroup (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.85). These results indicate that the system can effectively identify high-risk individuals from the general population who truly require confirmatory MRI. By repurposing existing CT data without incremental examination costs, this workflow shifts the diagnostic window to the latent stage of disease, optimizing public health resource allocation for secondary prevention.\u003c/p\u003e \u003cp\u003eOur study entails several limitations. First, we strictly defined the time window between CT and MRI to within seven days. This criterion was necessary to obviate confounding effects from muscle atrophy, though it constrained the eligible sample size for external validation. Second, radiomic texture features are theoretically sensitive to scanning parameters. Although we implemented the ComBat harmonization algorithm to mitigate non-biological biases, future validation on larger-scale data remains warranted. Third, we did not establish reader control experiments. This is because radiologists do not proactively assess rotator cuff injuries on chest CT in routine practice. The objective of this study is to augment clinical attention rather than supplant manual diagnosis. Finally, refined stratified analysis of tear severity was not executed. Different tendon involvement patterns may correspond to disparate compensatory phenotypes. This represents an important direction for future investigation.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study establishes the clinical feasibility of opportunistic RCT screening leveraging routine chest CT. The proposed system transcends the traditional paradigm of local lesion visualization. Instead, it relies on systematic musculoskeletal compensation and osseous degeneration. Our multi-center validation confirms its robustness across healthcare institutions of varying tiers. This risk-stratification paradigm provides a practical digital solution for early intervention. Ultimately, it informs the judicious allocation of public health resources through the repurposing of existing radiological data.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eData availability\u003c/h2\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available due to the presence of patient identifiable information and strict ethical restrictions regarding clinical imaging data but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003ch2\u003eCode availability\u003c/h2\u003e\n\u003cp\u003eThe underlying code for this study is not publicly available but may be made available to qualified researchers on reasonable request from the corresponding author.\u003c/p\u003e\n\u003ch2\u003eAuthor contributions\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eY.W.\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;J.L.\u0026nbsp;\u003c/strong\u003econtributed equally to this work.\u003cstrong\u003eY.W.\u003c/strong\u003eand \u003cstrong\u003eJ.L.\u003c/strong\u003e conceived the study, designed the experiments, and drafted the original manuscript. \u003cstrong\u003eY.W.\u003c/strong\u003e developed the deep learning architecture, performed the radiomic feature extraction, and executed the statistical analyses. \u003cstrong\u003eJ.L.\u003c/strong\u003e coordinated the data collection at the primary center, performed the radiological annotations, and oversaw the image quality control. \u003cstrong\u003eM.K.\u003c/strong\u003e and \u003cstrong\u003eJ.Y.\u003c/strong\u003e were responsible for data acquisition and curation for the external validation cohorts at Shengli Oilfield Central Hospital and Pingyu County People’s Hospital, respectively. \u003cstrong\u003eS.T.\u003c/strong\u003e provided expertise in clinical research design and validated the statistical methodology. \u003cstrong\u003eJ.X.\u003c/strong\u003e participated in the reference standard validation (reader study) and clinical data interpretation. \u003cstrong\u003eC.C.\u003c/strong\u003e, \u003cstrong\u003eH.L.\u003c/strong\u003e, and \u003cstrong\u003eJ.Z.\u003c/strong\u003e supervised the entire project, secured funding, and critically revised the manuscript for important intellectual content. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eFundings\u003c/h2\u003e\n\u003cp\u003eThis work was supported by National Natural Science Foundation of China (No. 82272579 and No. 82402827); China Postdoctoral Science Foundation (No. 2024M762039); The Shanghai Guidelines for the Application of the 2025 Annual High-quality Development Plan for Science and Technology Industry-Innovative Development of Biomedicine Project (No. 25S11907600); National Natural Science Foundation of China (No. 82504492); Natural Science Foundation of Jiangsu Higher Education Institutions of China (No. 25KJB320011); Suzhou Science and Technology Bureau project (No. SYW2025180); Young Elite Scientists Sponsorship Program of Jiangsu Province (No. JSTJ-2025-421); Suzhou Medical College-QiLu Medical Research Program of Soochow University (No. 24QL200114).\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMorimoto, T., Izumi, M., Ozaki, K., Sasanuma, H. \u0026amp; Ikeuchi, M. 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Harmonization of cortical thickness measurements across scanners and sites. \u003cem\u003eNeuroimage\u003c/em\u003e 167, 104\u0026ndash;120 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLundberg, S.M. \u0026amp; Lee, S.-I. in Proceedings of the 31st International Conference on Neural Information Processing Systems 4768\u0026ndash;4777 (Curran Associates Inc., Long Beach, California, USA; 2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarkan, O. et al. in Proceedings of the 30th ACM International Conference on Information \u0026amp; Knowledge Management 2882\u0026ndash;2887 (2021).\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"","lastPublishedDoi":"10.21203/rs.3.rs-8876993/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8876993/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eRotator cuff tears (RCT) constitute the predominant etiology of shoulder dysfunction among middle-aged and elderly populations yet remain substantially underdiagnosed in community settings and routine health screenings owing to the prohibitive cost of magnetic resonance imaging. Chest computed tomography (CT), as a ubiquitously accessible imaging modality in contemporary clinical practice, furnishes a natural data substrate for opportunistic RCT screening. Notwithstanding, real-world implementation confronts formidable challenges encompassing suboptimal soft tissue contrast, incomplete anatomical coverage, and pervasive equipment heterogeneity.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe developed a fully automated, opportunistic screening system validated across a diverse multi-center cohort (N\u0026thinsp;=\u0026thinsp;1,442) from national, municipal, and county-level hospitals. To overcome imaging variations, we implemented a rigorous preprocessing pipeline incorporating ComBat harmonization to minimize cross-center batch effects. The core architecture features a gated attention-based multiple instance learning (Attn-MIL) network to extract deep representations from 3D CT patches. These were synergistically fused with interpretable radiomic features quantifying muscle compensation and osseous degeneration.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn the primary national-center cohort, the hybrid model yielded excellent diagnostic discrimination (AUC, 0.956; 95% CI, 0.943\u0026ndash;0.969). Across highly heterogeneous real-world external validation cohorts, the model exhibited robust generalizability: AUC attained 0.893 at the municipal center and 0.858 at the county center. Subgroup analyses confirmed model robustness across divergent body habitus, scanner manufacturers and acquisition protocols. Notably, the model maintained high precision in identifying early-stage pathology, validating its sensitivity for occult injury screening. Interpretability analyses further corroborated that the model correctly captured kinetic chain reorganization patterns secondary to RCT.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study establishes the clinical feasibility of opportunistic RCT screening using routine chest CT. By effectively neutralizing batch effects and leveraging deep feature fusion, our system enables accurate, zero-cost risk stratification without additional radiation, facilitating proactive population health management.\u003c/p\u003e","manuscriptTitle":"Opportunistic rotator cuff tear screening from routine chest CT: a deep learning-radiomics hybrid model with multi-center validation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-07 10:04:45","doi":"10.21203/rs.3.rs-8876993/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":"1d6e55b7-e64b-4677-a677-ac7417c80283","owner":[],"postedDate":"April 7th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-01T15:44:52+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":65571371,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":65571372,"name":"Health sciences/Diseases"},{"id":65571373,"name":"Health sciences/Health care"},{"id":65571374,"name":"Physical sciences/Mathematics and computing"},{"id":65571375,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2026-05-01T15:55:04+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-07 10:04:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8876993","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8876993","identity":"rs-8876993","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}