Assessment of Posterosuperior Rotator Cuff tear risk based on shoulder CT-A Novel Scoring System

Assessment of Posterosuperior Rotator Cuff tear risk based on shoulder CT-A Novel Scoring System | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessment of Posterosuperior Rotator Cuff tear risk based on shoulder CT-A Novel Scoring System Xieyu Wang, Guihu Liu, Xiaolong Wang, Guangsi Shen, Haibin Zhou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8010896/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 tear (RCT) is a primary cause of shoulder pain and a leading source of shoulder disability in later stages. Although various computed tomography (CT) based measurements of the shoulder have been identified as predictors for RCT, we hypothesize that a combination of predictors will provide superior diagnostic and predictive performance compared to individual predictors. Thus, the aims of this study are: (i) to integrate various shoulder CT-based measurement parameters for predicting rotator cuff tears, and (ii) to develop a scoring system based on these predictors for estimating the likelihood of posterosuperior rotator cuff tears ( RCT-PT). (iii) To provide a diagnostic basis and predict the risk of posterosuperior rotator cuff tears for patients with contraindications to magnetic resonance imaging(MRI), an inability to cooperate with or complete the examination, or for whom MRI is deemed unnecessary. Methods This retrospective study analyzed 326 cases who underwent both shoulder CT and MRI examinations at our hospital. Based on the shoulder MRI findings, patients were stratified into two groups: a rotator cuff tear group and a control group. The selected predictors included the Critical Shoulder Angle (CSA), Acromial Index (AI), Goutallier grade of fatty infiltration, Supraspinatus Occupation Ratio, the Hounsfield Unit (HU) ratio of the deltoid to supraspinatus muscle, and other variables such as gender, age, symptom duration, and BMI. These factors were analyzed using univariate and multivariate analyses. The factors identified in the multivariate analysis were subsequently integrated into a scoring system based on their odds ratios (OR). Results Multivariate analysis identified the following independent risk factors: age (p < 0.01, OR = 1.090), fatty infiltration grade (p = 0.047, OR = 2.252), symptom duration (p = 0.012, OR = 1.036), critical shoulder angle (p = 0.028, OR = 1.175), and acromial index (p = 0.034, OR = 1.068). A 7-point scoring system was subsequently developed. Based on the weighting derived from the multivariate analysis odds ratios, one point each was assigned to age, symptom duration, supraspinatus occupation ratio, critical shoulder angle, and acromial index, while two points were assigned to the fatty infiltration grade. A score of 4 points was established as the threshold for predicting posterosuperior rotator cuff tears, yielding a sensitivity of 0.866 and a specificity of 0.904. Conclusion The developed numerical score, which integrates shoulder CT measurements with clinical factors, serves as a practical tool for predicting rotator cuff tears. It facilitates risk assessment based on CT findings and overcomes the limitation of MRI contraindication by providing a reliable predictive alternative for such cases. Rotator Cuff Tear Critical Shoulder Angle Fatty Infiltration Acromial Index Figures Figure 1 Figure 2 Figure 3 Figure 4 Backgroud A rotator cuff tear is a common pathological condition that causes shoulder pain and functional impairment, and can even lead to disability in severe cases. [ 1 , 2 ]。Accurate diagnosis of rotator cuff tears is of particular importance. Shoulder radiographs can serve as an initial tool to assess bony abnormalities associated with shoulder impingement. Magnetic Resonance imaging is considered the preferred imaging modality for evaluating the shoulder, as it provides a comprehensive assessment of both bone and soft tissue abnormalities. Computed tomography, on the other hand, is excellent for evaluating bony details and detecting the presence of gas and calcific deposits. [ 3 ] For instance, such cases may include patients with cardiac pacemakers, implantable drug infusion pumps, magnetic aneurysm or vascular clips, claustrophobia, or critically ill patients in the ICU. In these scenarios, shoulder joint CT represents the optimal alternative, as various parameters derived from the imaging can effectively predict the risk of rotator cuff tears, thereby allowing preliminary assessment of the rotator cuff condition. Numerous studies have confirmed the correlation of the Critical Shoulder Angle (CSA) and Acromial Index (AI) with rotator cuff tears, establishing them as reliable predictive factors. [ 4 – 8 ]。Supporting this, Simone et al. reported a mean CSA of 36.7° in rotator cuff tear patients, with a clear trend of increasing tear severity alongside greater CSA values. [ 4 ]。Chronic rotator cuff tears are consistently associated with muscle atrophy and fatty infiltration. [ 9 – 11 ], Worsening fatty infiltration and muscle atrophy are closely linked to the severity of rotator cuff tears, making them critical indicators for predicting tear extent, assessing the feasibility of surgical repair, and determining patient outcomes. [ 12 – 14 ]。While current studies indicate a link between sarcopenia and rotator cuff tears [ 15 ], the causal relationship remains unclear. Reem et al. further demonstrated that rotator cuff tear-induced muscle atrophy disproportionately affects the deltoid and supraspinatus muscles. Consequently, using the deltoid as a reference for comparative studies may yield more reliable results than studying the supraspinatus in isolation. [ 16 ] Therefore, we aim to determine whether the Hounsfield Unit (HU) ratio of the deltoid to supraspinatus muscle can serve as a predictor for rotator cuff tears and to develop a systematic scoring system based on shoulder CT for assessing the risk of rotator cuff tears. Methods Patient Selection This study was a retrospective cohort analysis of consecutive patients who underwent both shoulder CT and MRI at the authors' institution between January 2021 and January 2024. The study protocol was approved by the Institutional Review Board. The inclusion criteria were patients with: (1) an MRI-confirmed posterosuperior rotator cuff tear. (2) availability of both shoulder CT and MRI examinations. (3) an intact subscapularis tendon. Patients were excluded based on the following criteria: (1) a history of trauma. (2) an isolated subscapularis tendon tear. (3) a history of shoulder fracture or tumor. (4) an irreparable rotator cuff tear, and (5) incomplete baseline data. (Fig. 1) Clinical Variables We documented patient variables including age, gender, body mass index, and duration of symptoms. Systemic conditions such as diabetes, hypertension, and frozen shoulder were also recorded. Using CT scans, we measured the acromial index, critical shoulder angle, fatty infiltration of the supraspinatus muscle, supraspinatus occupation ratio, and the deltoid-to-supraspinatus Hounsfield unit (HU) ratio. Supraspinatus occupation ratio on CT The supraspinatus occupation ratio was calculated on the most lateral oblique sagittal slice as the ratio of the cross-sectional area of the supraspinatus muscle to the area of the supraspinatus fossa. This specific slice is defined where the scapular body intersects with the spine of the scapula on the oblique sagittal plane. The supraspinatus fossa is the nearly enclosed space bounded superiorly by the clavicle, scapula, and the deep surface of the trapezius muscle. The cross-sectional areas of both the supraspinatus muscle and the fossa at this level were manually outlined using software, which then automatically computed their areas to derive the ratio (Fig. 2). Fatty infiltration was assessed on CT images according to the classification proposed by Goutallier et al. The Goutallier stages are defined as follows: Grade 0: The muscle exhibits completely normal, homogeneous soft-tissue density without any low-attenuation fatty streaks. Grade 1: Some minimal, linear or stippled low-attenuation (fatty) streaks are present within the muscle. Grade 2: There is evident fatty infiltration, but the amount of fat remains less than the amount of muscle tissue (i.e., fat < 50%). Grade 3: The quantity of fat is approximately equal to the quantity of muscle tissue (i.e., fat ≈ 50%). The muscle density is markedly heterogeneous, and muscle volume often begins to decrease (atrophy). Grade 4: The amount of fat substantially exceeds that of muscle tissue (i.e., fat > 50%). The muscle is severely atrophied and is largely replaced by extensive areas of low-attenuation fat, with only few residual strands of soft-tissue density. On the most lateral oblique sagittal slice, the cross-sectional contour of the supraspinatus muscle was manually outlined as accurately as possible. The mean Hounsfield Unit (HU) value within this defined region of interest (ROI) was recorded as the supraspinatus HU value. On axial CT images, a slice displaying a complete and well-defined deltoid muscle contour was identified (Fig. 3). The ROI was manually outlined on three separate axial slices to best approximate the cross-sectional area of the deltoid. The mean HU value from these three ROIs was calculated and recorded as the deltoid HU value. The CSA was defined as the angle between the line connecting the superior and inferior margins of the glenoid and the line connecting the most lateral aspect of the acromion to the inferior glenoid margin. The AI was defined as the ratio of the horizontal distance from the glenoid rim (a reference line drawn perpendicular to the plane of the glenoid) to the most lateral point of the acromion, to the horizontal distance from the same glenoid reference line to the most lateral point of the humeral head. All measurements were performed by an independent orthopedic surgeon. For each parameter, three separate measurements were obtained, and the average of these three values was used for the final analysis. New Scoring Design The scoring system for rotator cuff tears was developed utilizing patient clinical characteristics and various measurements derived from CT imaging. Receiver operating characteristic (ROC) curve analysis was employed to determine the optimal cut-off values for the predictive variables, with the Youden index used to define the threshold. Subsequently, a multivariable logistic regression model was applied to calculate the odds ratios (ORs) for a posterosuperior rotator cuff tear (PSTR). Points were assigned to each category based on the likelihood ratio of its association with PSTR, thereby establishing the PSTR score. Statistical Analysis All statistical analyses were performed using SPSS (version 20.0; IBM). A p-value of < 0.05 was considered statistically significant. In the univariate analysis, comparisons of means were conducted using the Student's t-test. Continuous variables were analyzed using either the independent samples t-test or the Mann-Whitney U test, depending on their distribution. Categorical variables were compared using the Chi-square test or Fisher's exact test, as appropriate. For the multivariate analysis, logistic regression was employed to identify independent variables associated with rotator cuff tears and to estimate their odds ratios (ORs). The ORs derived from the logistic regression model were used to determine the score assigned to each independent risk factor. The performance of this scoring system was then evaluated by applying it to the study cohort; its sensitivity and specificity were validated using values obtained from multiple imputation. Results Univariate analysis of each variable Univariate analysis (Table 1 ) identified the following factors influencing rotator cuff tears: age (p < 0.01), gender (p < 0.01), supraspinatus occupation ratio (p < 0.01), critical shoulder angle (p < 0.01), deltoid-to-supraspinatus HU ratio (p < 0.01), acromial index (p < 0.01), symptom duration (p < 0.01), and Goutallier grade (p < 0.01). In contrast, body mass index (BMI, p = 0.59), diabetes (p = 0.104), hypertension (p = 0.737), and frozen shoulder (p = 0.66) were not significantly associated. Table 1 Comparison of clinical information between tne two groups of patients No- RCT group RCT group T(Z) p Age, y, 43.29 ± 11.805 53.65 ± 13.095 -10.979 < 0.01 Sex, n (%) - - - < 0.01 Male 68 71 - - female 57 130 - - BMI, kg/m² 25.15 ± 2.94 24.84 ± 2.86 0.54 0.59 SSPOR (%), mean ± SD 76.03 ± 7.25 57.99 ± 12.99 -11.774 < 0.01 CSA °, mean ± SD 33.48 ± 2.856 37.36 ± 3.477 -9.573 < 0.01 DTHU/ SSPHU, mean ± SD 0.83 ± 0.12 0.97 ± 0.28 -5.3 < 0.01 AI, mean ± SD 0.76 ± 0.086 0.51 ± 0.41 -7.327 < 0.01 DOS(m), mean ± SD 7.27 ± 13.90 12.55 ± 20.21 -4.423 < 0.01 SSPFI, n (%) - - - < 0.01 Stage 0 22(17.6) 1(0.5) - - Stage 1 91(72.8) 50(24.9) - - Stage 2 10(8) 116(57.7) - - Stage 3 2(1.6) 29(8.6) - - Stage 4 0(0) 5(2.5) - - Diabetes, n (%) 9(7.2) 26(12.94) - 0.104 Hypertension, n (%) 81(40.3) 48(38.4) - 0.737 Frozen shoulder, n (%) 51(40.8) 87(43.28) - 0.66 SSPOR: Supraspinatus Occupancy Rate; CSA: Critical Shoulder Angle; DTHU/SSPHU: Deltoid Hounsfield Units /Supraspinatus Hounsfield Units; DOS: Duration Of Symptoms; AI: Acromion Index; SSPFI: Supraspinatus Fatty Infiltration; SD: Standard Deviation; BMI: Body Mass Index Independent risk factors Multivariable logistic regression analysis (Table 2 ) identified the following independent risk factors: age, fatty infiltration grade (Goutallier grade), symptom duration, critical shoulder angle, and acromial index. Conversely, the supraspinatus occupation ratio was identified as a protective factor. Table 2 Risk factors associated with rotator cuff tears in multivariate analysis variable B SE Wald χ² p OR(Exp(B)) 95% CI(OR) Age 0.086 0.020 18.109 < 0.01 1.090 [1.047–1.134] Sex 0.463 0.403 1.318 0.251 1.589 [0.721-3.500] SSPFI 0.812 0.409 3.950 0.047 2.252 [1.011–5.016] DOS 0.035 0.014 6.252 0.012 1.036 [1.008–1.065] DTHU/ SSPHU -0.004 0.016 0.061 0.806 0.996 [0.965–1.028] SSPOR -0.128 0.028 20.982 < 0.01 0.880 [0.833–0.929] CSA 0.162 0.073 4.833 0.028 1.175 [1.018–1.357] AI 0.065 0.031 4.520 0.034 1.068 [1.005–1.134] constant -7.218 3.344 4.659 0.031 0.001 - SSPFI: Supraspinatus Fatty Infiltration; DOS: Duration Of Symptoms; DTHU/SSPHU: Deltoid Hounsfield Units /Supraspinatus Hounsfield Units; SSPOR: Supraspinatus Occupancy Rate; CSA: Critical Shoulder Angle; AI: Acromion Index; OR, Odds Ratio. The thresholds of various risk factors and the AUC The study determined the following optimal cut-off values for the predictors: age (50 years), fatty infiltration grade (Goutallier Stage 2), symptom duration (4.5 months), supraspinatus occupation ratio (0.69), critical shoulder angle (34.5°), and acromial index (0.80) (Table 3 ). However, prediction based on any single factor is inherently limited. Therefore, a combined approach that integrates these risk factors is necessary for a more robust and accurate prediction model. Table 3 Thresholds and AUC of each influencing factor Risk factors Region Standard Error Asymptotic Significance 95% Asymptotic CI Threshold Lower Upper Age 0.861 0.022 < 0.01 0.818 0.905 50 SSPFI 0.848 0.022 < 0.01 0.804 0.891 2 DOS 0.645 0.032 < 0.01 0.583 0.707 4.5 SSPOR 0.888 0.017 < 0.01 0.854 0.922 0.69 CSA 0.814 0.024 < 0.01 0.767 0.861 34.5 AI 0.741 0.030 < 0.01 0.683 0.799 0.80 SSPFI: Supraspinatus Fatty Infiltration; DOS: Duration Of Symptoms; SSPOR: Supraspinatus Occupancy Rate; CSA: Critical Shoulder Angle; AI: Acromion Index. Scoring system design The CT-based RCT-PT score was determined by weighting each variable according to its odds ratio (OR) for influencing rotator cuff tear risk (Table 4 ). Points were assigned as follows: age ≥ 50 years (OR = 1.090, 1 point), fatty infiltration grade ≥ Stage 2 (OR = 2.252, 2 points), symptom duration ≥ 4.5 months (OR = 1.036, 1 point), critical shoulder angle ≥ 34.5° (OR = 1.175, 1 point), and acromial index ≥ 0.80 (OR = 1.068, 1 point). The supraspinatus occupation ratio, identified as the strongest protective factor, was assigned 1 point when < 0.69 (OR = 0.880). Table 4 Weighted scores of each factor after multivariate analysis P OR 95% CI Points of each parameter Age ≥ 50 < 0.01 1.090 [1.047–1.134] 1 SSPFI ≥ 2 0.047 2.252 [1.011–5.016] 2 DOS ≥ 4.5 0.012 1.036 [1.008–1.065] 1 SSPOR < 0.69 < 0.01 0.880 [0.833–0.929] 1 CSA ≥ 34.5° 0.028 1.175 [1.018–1.357] 1 AI ≥ 0.80 0.034 1.068 [1.005–1.134] 1 SSPFI: Supraspinatus Fatty Infiltration; DOS: Duration Of Symptoms; SSPOR: Supraspinatus Occupancy Rate; CSA: Critical Shoulder Angle; AI: Acromion Index; OR,Odds Ratio. Research population under the RCT-PT Scoring system When applied to the study population, the scoring system revealed that the rotator cuff tear group had a mean score of 5.42 (range: 1–7), while the control group (non-tear group) had a mean score of 1.67 (range: 0–6) (Table 5 ). Using a cut-off score of 4 points, the RCT-PT score demonstrated a sensitivity of 86.6% and a specificity of 90.4% for predicting rotator cuff tears. Table 5 Implementation of the New Scoring System in the Study Cohort Score No- RCT group(n a ) RCT group(n a ) Sensitivity, % Specificity, % Ppv, % 0 20 0 100.0 0.0 61.7 1 47 3 100.0 16.0 65.7 2 33 9 98.5 53.6 77.3 3 13 15 94.0 80.0 88.3 4 6 25 86.6 90.4 93.5 5 3 28 74.1 95.2 96.1 6 3 63 60.2 97.6 97.6 7 0 58 100.0 100.0 100.0 Ppv: positive predictive value; a: Distribution of patients across the new scoring system; RCT: Rotator Cuff Tear. THE RCT-PT is used to study the results obtained from the population In the study population, the Receiver Operating Characteristic (ROC) analysis of the RCT-PT scoring system yielded an area under the curve (AUC) of 0.947 (p < 0.01) (Table 6 ). The determined cut-off value was 4, which corresponded to a sensitivity of 86.6% and a specificity of 90.4% (Fig. 4). Table 6 Analysis of ROC Curve Results for Predicting RCT with RCT-PT Cutoff Value Youden Index J Sensitivity Specificity AUC Scoring ≥ 4 0.77 0.866 0.904 0.947 AUC: Area Under the Cur Discussion The primary finding of this study was the confirmation of risk factors associated with posterosuperior rotator cuff tears. These factors include age, Goutallier grade of fatty infiltration in the supraspinatus muscle, symptom duration, critical shoulder angle, and acromial index. These independent risk factors were utilized to develop the RCT-PT score, a novel scoring system ranging from 0 to 7 points. At the optimal cut-off value of 4 points, the scoring system demonstrated a positive predictive value of 93.5% and a sensitivity of 86.6% for diagnosing rotator cuff tears. Despite existing literature on risk factors like age, BMI, acromial index, critical shoulder angle, and diabetes [ 4 , 5 , 17 , 18 ], a comprehensive scoring system that integrates these variables based on shoulder CT to predict rotator cuff tears is still lacking. Prior studies have identified elevated acromial index and critical shoulder angle as significant risk factors for rotator cuff tears. It has been documented that the mean CSA is approximately 36.7° in patients with full-thickness tears, 34.6° in those with partial-thickness tears, and 33.1° in individuals without rotator cuff tears [ 4 , 5 ].Armstrong first proposed that mechanical conflict between the acromion and the supraspinatus tendon was the cause of supraspinatus tendon degeneration; this concept was subsequently popularized by Neer [ 19 , 20 ].The critical shoulder angle (CSA) represents the extent of acromial coverage over the humeral head. Consequently, a higher CSA value increases the superiorly directed vector of the deltoid muscle, leading to elevated stress on the rotator cuff [ 21 ]. This functional effect is considered more relevant than direct contact between the acromion and the rotator cuff. Based on MRI and radiological assessments respectively, Nyffeler et al. and Banas et al. reported on the lateral acromial angle and the acromial index. Their studies established a statistically significant relationship where a smaller lateral acromial angle and a larger acromial index were associated with a higher incidence of shoulder impingement syndrome and subacromial pathology [ 22 , 23 ]. Since the introduction of the grading system by Goutallier in 1994, fatty infiltration and muscle atrophy have been consistently linked to postoperative outcomes following arthroscopic surgery [ 12 , 24 – 26 ]. However, no studies have attempted to utilize fatty infiltration and muscle atrophy for predicting rotator cuff tears. Stengaard et al. and Frich et al. demonstrated that the supraspinatus muscle exhibits early acute inflammation, initial muscle degeneration, and significant signs of fatty infiltration following a rotator cuff tear [ 27 , 28 ].Furthermore, Wang et al. showed that delayed rotator cuff repair leads to persistent muscle atrophy and fatty infiltration, particularly when repair is delayed beyond 6 weeks, resulting in poorer shoulder function [ 29 ].Concurrently, Rubino et al. confirmed that significant muscle atrophy and fatty infiltration are evident as early as six weeks after supraspinatus tendon detachment. Moreover, the observed muscle atrophy and fatty infiltration in rotator cuff tears progress from the tendon-muscle junction towards the muscle origin over time [ 30 ].The present study found that fatty infiltration in the control group (without rotator cuff tears) was primarily distributed at Goutallier grades 0 and 1. In contrast, the tear group showed a significant shift towards higher grades (grade 2 and 3), indicating that degenerative changes and minor fatty infiltration can occur even in intact rotator cuffs. The mean supraspinatus occupation ratio was approximately 76% in the control group compared to about 58% in the tear group. This finding further confirms that muscle atrophy progresses from the tendon-muscle junction towards the muscle origin and advances continuously with the presence and progression of a rotator cuff tear. Previous studies have reported associations between rotator cuff pathology and factors such as age, hypertension, and diabetes [ 31 – 33 ]. Consistent with this literature, our study identified age as one of the strongest risk factors, with rotator cuff tear risk steadily increasing with advancing age, reflecting the combined effects of biological degeneration and cumulative mechanical stress on tendons over time. However, contrary to some previous reports, we found no statistically significant differences in the prevalence of hypertension, diabetes, or frozen shoulder between the two patient groups. The relationship between these comorbidities and rotator cuff tears, including the underlying mechanisms, may require further investigation in future studies. Ashry et al. found that in intact rotator cuffs, muscle atrophy and fatty infiltration increase with age. However, in the presence of a tear, atrophy disproportionately affects the supraspinatus compared to the deltoid. Consequently, using the deltoid as an internal reference for comparative assessment may be more reliable than evaluating the supraspinatus in isolation [ 16 , 34 ]. In our study, while the deltoid-to-supraspinatus Hounsfield unit (HU) ratio showed a significant difference between the two patient groups on univariate analysis, it was not identified as an independent predictor in the multivariable logistic regression model (p = 0.806). A plausible explanation for this finding is that the processes of fatty infiltration and muscle atrophy concurrently alter the HU values of both the deltoid and supraspinatus muscles. This likely induced a high degree of multicollinearity between the HU ratio and the other direct measures of atrophy and infiltration (e.g., Goutallier grade, occupation ratio). Consequently, the HU ratio was unable to demonstrate a statistically independent contribution within the multivariable model. When applied in clinical practice, the scoring system provides the following guidance: A patient score of ≤ 3 suggests a low probability of a rotator cuff tear, recommending initial observation or conservative management. A score of 4 points raises a high suspicion for a tear, warranting further diagnostic confirmation with MRI. A score of ≥ 5 indicates a high probability of a rotator cuff tear, suggesting a need for active intervention (e.g., surgery). Limitations This study has several limitations. First, it was a single-center investigation with a relatively small sample size. Patient clinical information did not include potential influencing factors such as hypercholesterolemia, smoking, or alcohol consumption. Second, the retrospective case-control design and reliance on historical records make the study susceptible to selection and information bias. The selection of cases and controls may not fully represent the general population, and data collection—including radiographic measurements and history taking—was not performed in a blinded manner, potentially introducing observer bias. Third, the assessment of "symptom duration" was based on patient recall, which is subject to recall bias. Furthermore, rotator cuff tears were treated as a binary outcome (present/absent) without considering tear size, location, or morphology, factors that might influence the weight of risk factors. Finally, it must be emphasized that this risk scoring system is intended as a screening and risk stratification tool, not a definitive diagnostic method. A high score should be regarded as an indication for more precise examination (e.g., MRI) and cannot replace the clinical gold standard. In conclusion, while recognizing the clinical value of this study, its limitations should be acknowledged. Future research should focus on validating and refining this scoring system through multicenter, large-scale, prospective cohort studies. Additionally, incorporating more comprehensive clinical variables, utilizing artificial intelligence for automated and precise measurement of imaging parameters, and exploring integration with molecular biomarkers represent promising directions for developing next-generation predictive models for rotator cuff tears. Conclusion A numerical scoring system based on shoulder CT and clinical factors was developed to predict rotator cuff tears. For clinical application, a patient score of ≤ 3 suggests a low probability of tear, recommending observation or conservative management. A score of 4 points indicates a high suspicion for tear, warranting further confirmatory imaging with MRI. A score of ≥ 5 points signifies a high probability of tear, suggesting active intervention such as surgery. Furthermore, this system provides a diagnostic alternative and reliable risk assessment for patients who are unable to undergo MRI due to specific contraindications. Declarations Ethics approval and consent to participate Ethical approval for the study was received from Ethics Committee of the Second Affiliated Hospital of Soochow University (NO. JDHG2024005). The research was conducted in accordance with the Declaration of Helsinki and relevant ethical guidelines. The requirement for informed consent was waived by the IRB due to the retrospective nature of the study and the use of de-identified data, which posed minimal risk to participants. Consent for publication All participants provided consent for the publication of data collected during this study. No identifiable personal information is included in this manuscript. Availability of data and materials The data generated and analyzed during this study are not publicly available due to privacy and ethical restrictions, as they contain sensitive information related to human participants. However, de-identified data may be available from the corresponding author upon reasonable request, subject to approval by the institutional review board of SecondSoochowU. Competing interests We declare that they have no competing interests. Funding This study was supported by the Suzhou Key Disciplines (No. SZXK2025）. Authors' contributions All authors contributed to the study conception and design. X.W. wrote the main manuscript text. X.W. , G.L. and L.W. collected the data. X.W. and G.L. completed the statistical analysis and created the tables1-6. L.W. prepared figures 1-4. G.S. and H.Z. revised the paper.All authors reviewed the manuscript. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. All authors approved the declaration. References Harryman D. Repairs of rotator cuff Correlation of functional results with integrity of the cuff. J Bone Joint Surg Am 1994, 73. Gerber C, Fuchs B, Hodler J. 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J Shoulder Elb Surg. 2010;19(1):116–20. Applegate KA, Thiese MS, Merryweather AS, Kapellusch J, Drury DL, Wood E, Kendall R, Foster J, Garg A, Hegmann KT. Association Between Cardiovascular Disease Risk Factors and Rotator Cuff Tendinopathy: A Cross-Sectional Study. J Occup Environ Med. 2017;59(2):154–60. Giri A, O'Hanlon D, Jain NB. Risk factors for rotator cuff disease: A systematic review and meta-analysis of diabetes, hypertension, and hyperlipidemia. Ann Phys Rehabil Med. 2023;66(1):101631. Ravn MK, Ostergaard TI, Schroeder HD, Nyengaard JR, Lambertsen KL, Frich LH. Supraspinatus and deltoid muscle fiber composition in rotator cuff tear conditions. JSES Int. 2020;4(3):431–7. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8010896","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":570349668,"identity":"79f0299f-730c-4864-a3d6-2d4267e85b6a","order_by":0,"name":"Xieyu Wang","email":"","orcid":"","institution":"Second Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Xieyu","middleName":"","lastName":"Wang","suffix":""},{"id":570349669,"identity":"d0c6cd61-8153-49f2-8c6a-54ac62b39886","order_by":1,"name":"Guihu Liu","email":"","orcid":"","institution":"Second Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Guihu","middleName":"","lastName":"Liu","suffix":""},{"id":570349670,"identity":"486a1241-3ec5-4a85-af9d-05f5246cda0c","order_by":2,"name":"Xiaolong Wang","email":"","orcid":"","institution":"Second Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Xiaolong","middleName":"","lastName":"Wang","suffix":""},{"id":570349673,"identity":"4549a0c1-1421-476a-ac62-dc89090c93f6","order_by":3,"name":"Guangsi 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14:43:26","extension":"html","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":129729,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8010896/v1/1901770ed2dabd4747596ecb.html"},{"id":99814846,"identity":"26b3fe97-8d72-44c9-91bf-d38bdfc70aea","added_by":"auto","created_at":"2026-01-08 14:42:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":530596,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchat. CT: Computer tomography; MRI: Magnetic resonance imaging.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8010896/v1/d0ee82b1b4b8299ce7a6ac06.png"},{"id":99814902,"identity":"bdcb71e5-d1e1-4118-aa49-628deb6514fe","added_by":"auto","created_at":"2026-01-08 14:43:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":964552,"visible":true,"origin":"","legend":"\u003cp\u003eMeasurement of supraspinatus fossa and the supraspinatus muscle. We measured the cross-sectional areas of the supraspinous fossa and the supraspinatus muscle in the outermost oblique sagittal plane through the boundary between muscle and bone. A1: The area of the supraspinous fossa was measured using 3D- slicer to be 9.712 cm². A2: The area of the supraspinatus muscle was 3.826 cm².\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8010896/v1/6957108d76e9a9414ba08319.png"},{"id":99814887,"identity":"9725b043-aefa-419b-9344-2b09ef8cff36","added_by":"auto","created_at":"2026-01-08 14:43:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1586318,"visible":true,"origin":"","legend":"\u003cp\u003eMeasurement of HU values of Supraspinatus and deltoid .The Hounsfield Unit (HU) values of the supraspinatus muscle were measured on the most lateral oblique sagittal plane, and the HU values of the deltoid muscle were measured on the axial plane. B1: The entire supraspinatus muscle was delineated for HU measurement. The HU value of the supraspinatus muscle in this patient was 60.34. B2-B4: Based on three adjacent axial slices that fully visualized the deltoid muscle, its HU values were measured. The three HU measurement results were 49.27, 48.69, and 49.08, respectively.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8010896/v1/a745cb26c8a188b202e8cd03.png"},{"id":99814696,"identity":"8ded4bf6-2a0c-456b-b144-535c96fcc91b","added_by":"auto","created_at":"2026-01-08 14:42:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":99306,"visible":true,"origin":"","legend":"\u003cp\u003eThe receiver operating characteristic (ROC) analysis in the study population for the new scoring system (RCT-PT) resulted in an area under the curve (AUC) of 0.781 (P \u0026lt; .001), with a determined cutoff value 4, achieving a sensitivity of 86.6% and specificity of 90.4%.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8010896/v1/c142fe25bcbda3dfa575f17d.png"},{"id":101089385,"identity":"ebfe98c6-c1b9-4be2-8ef0-5115e8a06274","added_by":"auto","created_at":"2026-01-25 17:39:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4052399,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8010896/v1/f12d48c2-d5c3-43e5-9212-4a9a7aee7c53.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessment of Posterosuperior Rotator Cuff tear risk based on shoulder CT-A Novel Scoring System","fulltext":[{"header":"Backgroud ","content":"\u003cp\u003eA rotator cuff tear is a common pathological condition that causes shoulder pain and functional impairment, and can even lead to disability in severe cases. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]。Accurate diagnosis of rotator cuff tears is of particular importance. Shoulder radiographs can serve as an initial tool to assess bony abnormalities associated with shoulder impingement. Magnetic Resonance imaging is considered the preferred imaging modality for evaluating the shoulder, as it provides a comprehensive assessment of both bone and soft tissue abnormalities. Computed tomography, on the other hand, is excellent for evaluating bony details and detecting the presence of gas and calcific deposits. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] For instance, such cases may include patients with cardiac pacemakers, implantable drug infusion pumps, magnetic aneurysm or vascular clips, claustrophobia, or critically ill patients in the ICU. In these scenarios, shoulder joint CT represents the optimal alternative, as various parameters derived from the imaging can effectively predict the risk of rotator cuff tears, thereby allowing preliminary assessment of the rotator cuff condition. Numerous studies have confirmed the correlation of the Critical Shoulder Angle (CSA) and Acromial Index (AI) with rotator cuff tears, establishing them as reliable predictive factors. [\u003cspan additionalcitationids=\"CR5 CR6 CR7\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e–\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]。Supporting this, Simone et al. reported a mean CSA of 36.7° in rotator cuff tear patients, with a clear trend of increasing tear severity alongside greater CSA values. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]。Chronic rotator cuff tears are consistently associated with muscle atrophy and fatty infiltration. [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e–\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], Worsening fatty infiltration and muscle atrophy are closely linked to the severity of rotator cuff tears, making them critical indicators for predicting tear extent, assessing the feasibility of surgical repair, and determining patient outcomes. [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e–\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]。While current studies indicate a link between sarcopenia and rotator cuff tears [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], the causal relationship remains unclear. Reem et al. further demonstrated that rotator cuff tear-induced muscle atrophy disproportionately affects the deltoid and supraspinatus muscles. Consequently, using the deltoid as a reference for comparative studies may yield more reliable results than studying the supraspinatus in isolation. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] Therefore, we aim to determine whether the Hounsfield Unit (HU) ratio of the deltoid to supraspinatus muscle can serve as a predictor for rotator cuff tears and to develop a systematic scoring system based on shoulder CT for assessing the risk of rotator cuff tears.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Methods","content":"\u003ch2\u003ePatient Selection\u003c/h2\u003e\u003cp\u003eThis study was a retrospective cohort analysis of consecutive patients who underwent both shoulder CT and MRI at the authors' institution between January 2021 and January 2024. The study protocol was approved by the Institutional Review Board. The inclusion criteria were patients with: (1) an MRI-confirmed posterosuperior rotator cuff tear. (2) availability of both shoulder CT and MRI examinations. (3) an intact subscapularis tendon. Patients were excluded based on the following criteria: (1) a history of trauma. (2) an isolated subscapularis tendon tear. (3) a history of shoulder fracture or tumor. (4) an irreparable rotator cuff tear, and (5) incomplete baseline data. (Fig.\u0026nbsp;1)\u003c/p\u003e\n\u003ch3\u003eClinical Variables\u003c/h3\u003e\n\u003cp\u003eWe documented patient variables including age, gender, body mass index, and duration of symptoms. Systemic conditions such as diabetes, hypertension, and frozen shoulder were also recorded. Using CT scans, we measured the acromial index, critical shoulder angle, fatty infiltration of the supraspinatus muscle, supraspinatus occupation ratio, and the deltoid-to-supraspinatus Hounsfield unit (HU) ratio.\u003c/p\u003e\n\u003ch3\u003eSupraspinatus occupation ratio on CT\u003c/h3\u003e\n\u003cp\u003eThe supraspinatus occupation ratio was calculated on the most lateral oblique sagittal slice as the ratio of the cross-sectional area of the supraspinatus muscle to the area of the supraspinatus fossa. This specific slice is defined where the scapular body intersects with the spine of the scapula on the oblique sagittal plane. The supraspinatus fossa is the nearly enclosed space bounded superiorly by the clavicle, scapula, and the deep surface of the trapezius muscle. The cross-sectional areas of both the supraspinatus muscle and the fossa at this level were manually outlined using software, which then automatically computed their areas to derive the ratio (Fig.\u0026nbsp;2). Fatty infiltration was assessed on CT images according to the classification proposed by Goutallier et al. The Goutallier stages are defined as follows: Grade 0: The muscle exhibits completely normal, homogeneous soft-tissue density without any low-attenuation fatty streaks. Grade 1: Some minimal, linear or stippled low-attenuation (fatty) streaks are present within the muscle. Grade 2: There is evident fatty infiltration, but the amount of fat remains less than the amount of muscle tissue (i.e., fat\u0026thinsp;\u0026lt;\u0026thinsp;50%). Grade 3: The quantity of fat is approximately equal to the quantity of muscle tissue (i.e., fat\u0026thinsp;\u0026asymp;\u0026thinsp;50%). The muscle density is markedly heterogeneous, and muscle volume often begins to decrease (atrophy). Grade 4: The amount of fat substantially exceeds that of muscle tissue (i.e., fat\u0026thinsp;\u0026gt;\u0026thinsp;50%). The muscle is severely atrophied and is largely replaced by extensive areas of low-attenuation fat, with only few residual strands of soft-tissue density.\u003c/p\u003e \u003cp\u003eOn the most lateral oblique sagittal slice, the cross-sectional contour of the supraspinatus muscle was manually outlined as accurately as possible. The mean Hounsfield Unit (HU) value within this defined region of interest (ROI) was recorded as the supraspinatus HU value. On axial CT images, a slice displaying a complete and well-defined deltoid muscle contour was identified (Fig.\u0026nbsp;3). The ROI was manually outlined on three separate axial slices to best approximate the cross-sectional area of the deltoid. The mean HU value from these three ROIs was calculated and recorded as the deltoid HU value.\u003c/p\u003e \u003cp\u003eThe CSA was defined as the angle between the line connecting the superior and inferior margins of the glenoid and the line connecting the most lateral aspect of the acromion to the inferior glenoid margin. The AI was defined as the ratio of the horizontal distance from the glenoid rim (a reference line drawn perpendicular to the plane of the glenoid) to the most lateral point of the acromion, to the horizontal distance from the same glenoid reference line to the most lateral point of the humeral head. All measurements were performed by an independent orthopedic surgeon. For each parameter, three separate measurements were obtained, and the average of these three values was used for the final analysis.\u003c/p\u003e\n\u003ch3\u003eNew Scoring Design\u003c/h3\u003e\n\u003cp\u003eThe scoring system for rotator cuff tears was developed utilizing patient clinical characteristics and various measurements derived from CT imaging. Receiver operating characteristic (ROC) curve analysis was employed to determine the optimal cut-off values for the predictive variables, with the Youden index used to define the threshold. Subsequently, a multivariable logistic regression model was applied to calculate the odds ratios (ORs) for a posterosuperior rotator cuff tear (PSTR). Points were assigned to each category based on the likelihood ratio of its association with PSTR, thereby establishing the PSTR score.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using SPSS (version 20.0; IBM). A p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant. In the univariate analysis, comparisons of means were conducted using the Student's t-test. Continuous variables were analyzed using either the independent samples t-test or the Mann-Whitney U test, depending on their distribution. Categorical variables were compared using the Chi-square test or Fisher's exact test, as appropriate. For the multivariate analysis, logistic regression was employed to identify independent variables associated with rotator cuff tears and to estimate their odds ratios (ORs). The ORs derived from the logistic regression model were used to determine the score assigned to each independent risk factor. The performance of this scoring system was then evaluated by applying it to the study cohort; its sensitivity and specificity were validated using values obtained from multiple imputation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eUnivariate analysis of each variable\u003c/h2\u003e \u003cp\u003eUnivariate analysis (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) identified the following factors influencing rotator cuff tears: age (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), gender (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), supraspinatus occupation ratio (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), critical shoulder angle (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), deltoid-to-supraspinatus HU ratio (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), acromial index (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), symptom duration (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and Goutallier grade (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In contrast, body mass index (BMI, p\u0026thinsp;=\u0026thinsp;0.59), diabetes (p\u0026thinsp;=\u0026thinsp;0.104), hypertension (p\u0026thinsp;=\u0026thinsp;0.737), and frozen shoulder (p\u0026thinsp;=\u0026thinsp;0.66) were not significantly associated.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of clinical information between tne two groups of patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo- RCT group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRCT group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT(Z)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, y,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.29\u0026thinsp;\u0026plusmn;\u0026thinsp;11.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.65\u0026thinsp;\u0026plusmn;\u0026thinsp;13.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-10.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.15\u0026thinsp;\u0026plusmn;\u0026thinsp;2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.84\u0026thinsp;\u0026plusmn;\u0026thinsp;2.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSPOR (%), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76.03\u0026thinsp;\u0026plusmn;\u0026thinsp;7.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.99\u0026thinsp;\u0026plusmn;\u0026thinsp;12.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-11.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSA \u0026deg;, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.48\u0026thinsp;\u0026plusmn;\u0026thinsp;2.856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.36\u0026thinsp;\u0026plusmn;\u0026thinsp;3.477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-9.573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDTHU/ SSPHU, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDOS(m), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.27\u0026thinsp;\u0026plusmn;\u0026thinsp;13.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.55\u0026thinsp;\u0026plusmn;\u0026thinsp;20.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSPFI, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22(17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91(72.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50(24.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10(8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e116(57.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29(8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9(7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26(12.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81(40.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48(38.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.737\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrozen shoulder, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51(40.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87(43.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSSPOR: Supraspinatus Occupancy Rate; CSA: Critical Shoulder Angle; DTHU/SSPHU: Deltoid Hounsfield Units /Supraspinatus Hounsfield Units; DOS: Duration Of Symptoms; AI: Acromion Index; SSPFI: Supraspinatus Fatty Infiltration; SD: Standard Deviation; BMI: Body Mass Index\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eIndependent risk factors\u003c/h2\u003e \u003cp\u003eMultivariable logistic regression analysis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) identified the following independent risk factors: age, fatty infiltration grade (Goutallier grade), symptom duration, critical shoulder angle, and acromial index. Conversely, the supraspinatus occupation ratio was identified as a protective factor.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRisk factors associated with rotator cuff tears in multivariate analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWald χ\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR(Exp(B))\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% CI(OR)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.047\u0026ndash;1.134]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.721-3.500]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSPFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.011\u0026ndash;5.016]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.008\u0026ndash;1.065]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDTHU/ SSPHU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.965\u0026ndash;1.028]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSPOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.833\u0026ndash;0.929]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.018\u0026ndash;1.357]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.005\u0026ndash;1.134]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003econstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-7.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eSSPFI: Supraspinatus Fatty Infiltration; DOS: Duration Of Symptoms; DTHU/SSPHU: Deltoid Hounsfield Units /Supraspinatus Hounsfield Units; SSPOR: Supraspinatus Occupancy Rate; CSA: Critical Shoulder Angle; AI: Acromion Index; OR, Odds Ratio.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eThe thresholds of various risk factors and the AUC\u003c/h2\u003e \u003cp\u003eThe study determined the following optimal cut-off values for the predictors: age (50 years), fatty infiltration grade (Goutallier Stage 2), symptom duration (4.5 months), supraspinatus occupation ratio (0.69), critical shoulder angle (34.5\u0026deg;), and acromial index (0.80) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, prediction based on any single factor is inherently limited. Therefore, a combined approach that integrates these risk factors is necessary for a more robust and accurate prediction model.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThresholds and AUC of each influencing factor\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAsymptotic Significance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e95% Asymptotic CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThreshold\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSPFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSPOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eSSPFI: Supraspinatus Fatty Infiltration; DOS: Duration Of Symptoms; SSPOR: Supraspinatus Occupancy Rate; CSA: Critical Shoulder Angle; AI: Acromion Index.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eScoring system design\u003c/h2\u003e \u003cp\u003eThe CT-based RCT-PT score was determined by weighting each variable according to its odds ratio (OR) for influencing rotator cuff tear risk (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Points were assigned as follows: age\u0026thinsp;\u0026ge;\u0026thinsp;50 years (OR\u0026thinsp;=\u0026thinsp;1.090, 1 point), fatty infiltration grade\u0026thinsp;\u0026ge;\u0026thinsp;Stage 2 (OR\u0026thinsp;=\u0026thinsp;2.252, 2 points), symptom duration\u0026thinsp;\u0026ge;\u0026thinsp;4.5 months (OR\u0026thinsp;=\u0026thinsp;1.036, 1 point), critical shoulder angle\u0026thinsp;\u0026ge;\u0026thinsp;34.5\u0026deg; (OR\u0026thinsp;=\u0026thinsp;1.175, 1 point), and acromial index\u0026thinsp;\u0026ge;\u0026thinsp;0.80 (OR\u0026thinsp;=\u0026thinsp;1.068, 1 point). The supraspinatus occupation ratio, identified as the strongest protective factor, was assigned 1 point when \u0026lt;\u0026thinsp;0.69 (OR\u0026thinsp;=\u0026thinsp;0.880).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWeighted scores of each factor after multivariate analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePoints of each parameter\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026ge;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[1.047\u0026ndash;1.134]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSPFI\u0026thinsp;\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[1.011\u0026ndash;5.016]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDOS\u0026thinsp;\u0026ge;\u0026thinsp;4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[1.008\u0026ndash;1.065]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSPOR \u0026lt; 0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[0.833\u0026ndash;0.929]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSA\u0026thinsp;\u0026ge;\u0026thinsp;34.5\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[1.018\u0026ndash;1.357]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI\u0026thinsp;\u0026ge;\u0026thinsp;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[1.005\u0026ndash;1.134]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSSPFI: Supraspinatus Fatty Infiltration; DOS: Duration Of Symptoms; SSPOR: Supraspinatus Occupancy Rate; CSA: Critical Shoulder Angle; AI: Acromion Index; OR,Odds Ratio.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eResearch population under the RCT-PT Scoring system\u003c/h2\u003e \u003cp\u003eWhen applied to the study population, the scoring system revealed that the rotator cuff tear group had a mean score of 5.42 (range: 1\u0026ndash;7), while the control group (non-tear group) had a mean score of 1.67 (range: 0\u0026ndash;6) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Using a cut-off score of 4 points, the RCT-PT score demonstrated a sensitivity of 86.6% and a specificity of 90.4% for predicting rotator cuff tears.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eImplementation of the New Scoring System in the Study Cohort\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScore\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo- RCT group(n\u003csup\u003ea\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRCT group(n\u003csup\u003ea\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSensitivity, %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpecificity, %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePpv, %\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e61.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e65.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e53.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e77.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e80.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e88.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e86.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e90.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e93.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e95.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e97.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003ePpv: positive predictive value;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003ea: Distribution of patients across the new scoring system; RCT: Rotator Cuff Tear.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eTHE RCT-PT is used to study the results obtained from the population\u003c/h2\u003e \u003cp\u003eIn the study population, the Receiver Operating Characteristic (ROC) analysis of the RCT-PT scoring system yielded an area under the curve (AUC) of 0.947 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The determined cut-off value was 4, which corresponded to a sensitivity of 86.6% and a specificity of 90.4% (Fig.\u0026nbsp;4).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of ROC Curve Results for Predicting RCT with RCT-PT\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCutoff Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYouden Index J\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScoring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.947\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAUC: Area Under the Cur\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe primary finding of this study was the confirmation of risk factors associated with posterosuperior rotator cuff tears. These factors include age, Goutallier grade of fatty infiltration in the supraspinatus muscle, symptom duration, critical shoulder angle, and acromial index. These independent risk factors were utilized to develop the RCT-PT score, a novel scoring system ranging from 0 to 7 points. At the optimal cut-off value of 4 points, the scoring system demonstrated a positive predictive value of 93.5% and a sensitivity of 86.6% for diagnosing rotator cuff tears.\u003c/p\u003e \u003cp\u003eDespite existing literature on risk factors like age, BMI, acromial index, critical shoulder angle, and diabetes [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], a comprehensive scoring system that integrates these variables based on shoulder CT to predict rotator cuff tears is still lacking. Prior studies have identified elevated acromial index and critical shoulder angle as significant risk factors for rotator cuff tears. It has been documented that the mean CSA is approximately 36.7\u0026deg; in patients with full-thickness tears, 34.6\u0026deg; in those with partial-thickness tears, and 33.1\u0026deg; in individuals without rotator cuff tears [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].Armstrong first proposed that mechanical conflict between the acromion and the supraspinatus tendon was the cause of supraspinatus tendon degeneration; this concept was subsequently popularized by Neer [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].The critical shoulder angle (CSA) represents the extent of acromial coverage over the humeral head. Consequently, a higher CSA value increases the superiorly directed vector of the deltoid muscle, leading to elevated stress on the rotator cuff [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. This functional effect is considered more relevant than direct contact between the acromion and the rotator cuff. Based on MRI and radiological assessments respectively, Nyffeler et al. and Banas et al. reported on the lateral acromial angle and the acromial index. Their studies established a statistically significant relationship where a smaller lateral acromial angle and a larger acromial index were associated with a higher incidence of shoulder impingement syndrome and subacromial pathology [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSince the introduction of the grading system by Goutallier in 1994, fatty infiltration and muscle atrophy have been consistently linked to postoperative outcomes following arthroscopic surgery [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. However, no studies have attempted to utilize fatty infiltration and muscle atrophy for predicting rotator cuff tears. Stengaard et al. and Frich et al. demonstrated that the supraspinatus muscle exhibits early acute inflammation, initial muscle degeneration, and significant signs of fatty infiltration following a rotator cuff tear [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].Furthermore, Wang et al. showed that delayed rotator cuff repair leads to persistent muscle atrophy and fatty infiltration, particularly when repair is delayed beyond 6 weeks, resulting in poorer shoulder function [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].Concurrently, Rubino et al. confirmed that significant muscle atrophy and fatty infiltration are evident as early as six weeks after supraspinatus tendon detachment. Moreover, the observed muscle atrophy and fatty infiltration in rotator cuff tears progress from the tendon-muscle junction towards the muscle origin over time [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].The present study found that fatty infiltration in the control group (without rotator cuff tears) was primarily distributed at Goutallier grades 0 and 1. In contrast, the tear group showed a significant shift towards higher grades (grade 2 and 3), indicating that degenerative changes and minor fatty infiltration can occur even in intact rotator cuffs. The mean supraspinatus occupation ratio was approximately 76% in the control group compared to about 58% in the tear group. This finding further confirms that muscle atrophy progresses from the tendon-muscle junction towards the muscle origin and advances continuously with the presence and progression of a rotator cuff tear.\u003c/p\u003e \u003cp\u003ePrevious studies have reported associations between rotator cuff pathology and factors such as age, hypertension, and diabetes [\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Consistent with this literature, our study identified age as one of the strongest risk factors, with rotator cuff tear risk steadily increasing with advancing age, reflecting the combined effects of biological degeneration and cumulative mechanical stress on tendons over time. However, contrary to some previous reports, we found no statistically significant differences in the prevalence of hypertension, diabetes, or frozen shoulder between the two patient groups. The relationship between these comorbidities and rotator cuff tears, including the underlying mechanisms, may require further investigation in future studies.\u003c/p\u003e \u003cp\u003eAshry et al. found that in intact rotator cuffs, muscle atrophy and fatty infiltration increase with age. However, in the presence of a tear, atrophy disproportionately affects the supraspinatus compared to the deltoid. Consequently, using the deltoid as an internal reference for comparative assessment may be more reliable than evaluating the supraspinatus in isolation [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In our study, while the deltoid-to-supraspinatus Hounsfield unit (HU) ratio showed a significant difference between the two patient groups on univariate analysis, it was not identified as an independent predictor in the multivariable logistic regression model (p\u0026thinsp;=\u0026thinsp;0.806). A plausible explanation for this finding is that the processes of fatty infiltration and muscle atrophy concurrently alter the HU values of both the deltoid and supraspinatus muscles. This likely induced a high degree of multicollinearity between the HU ratio and the other direct measures of atrophy and infiltration (e.g., Goutallier grade, occupation ratio). Consequently, the HU ratio was unable to demonstrate a statistically independent contribution within the multivariable model.\u003c/p\u003e \u003cp\u003eWhen applied in clinical practice, the scoring system provides the following guidance: A patient score of \u0026le;\u0026thinsp;3 suggests a low probability of a rotator cuff tear, recommending initial observation or conservative management. A score of 4 points raises a high suspicion for a tear, warranting further diagnostic confirmation with MRI. A score of \u0026ge;\u0026thinsp;5 indicates a high probability of a rotator cuff tear, suggesting a need for active intervention (e.g., surgery).\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations. First, it was a single-center investigation with a relatively small sample size. Patient clinical information did not include potential influencing factors such as hypercholesterolemia, smoking, or alcohol consumption. Second, the retrospective case-control design and reliance on historical records make the study susceptible to selection and information bias. The selection of cases and controls may not fully represent the general population, and data collection\u0026mdash;including radiographic measurements and history taking\u0026mdash;was not performed in a blinded manner, potentially introducing observer bias. Third, the assessment of \"symptom duration\" was based on patient recall, which is subject to recall bias. Furthermore, rotator cuff tears were treated as a binary outcome (present/absent) without considering tear size, location, or morphology, factors that might influence the weight of risk factors. Finally, it must be emphasized that this risk scoring system is intended as a screening and risk stratification tool, not a definitive diagnostic method. A high score should be regarded as an indication for more precise examination (e.g., MRI) and cannot replace the clinical gold standard.\u003c/p\u003e \u003cp\u003eIn conclusion, while recognizing the clinical value of this study, its limitations should be acknowledged. Future research should focus on validating and refining this scoring system through multicenter, large-scale, prospective cohort studies. Additionally, incorporating more comprehensive clinical variables, utilizing artificial intelligence for automated and precise measurement of imaging parameters, and exploring integration with molecular biomarkers represent promising directions for developing next-generation predictive models for rotator cuff tears.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eA numerical scoring system based on shoulder CT and clinical factors was developed to predict rotator cuff tears. For clinical application, a patient score of \u0026le;\u0026thinsp;3 suggests a low probability of tear, recommending observation or conservative management. A score of 4 points indicates a high suspicion for tear, warranting further confirmatory imaging with MRI. A score of \u0026ge;\u0026thinsp;5 points signifies a high probability of tear, suggesting active intervention such as surgery. Furthermore, this system provides a diagnostic alternative and reliable risk assessment for patients who are unable to undergo MRI due to specific contraindications.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for the study was received from Ethics Committee of the Second Affiliated Hospital of Soochow University (NO. JDHG2024005). The research was conducted in accordance with the Declaration of Helsinki and relevant ethical guidelines. The requirement for informed consent was waived by the IRB due to the retrospective nature of the study and the use of de-identified data, which posed minimal risk to participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants provided consent for the publication of data collected during this study. No identifiable personal information is included in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data generated and analyzed during this study are not publicly available due to privacy and ethical restrictions, as they contain sensitive information related to human participants. However, de-identified data may be available from the corresponding author upon reasonable request, subject to approval by the institutional review board of SecondSoochowU.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Suzhou\u0026nbsp;Key\u0026nbsp;Disciplines\u0026nbsp;(No. SZXK2025）.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design.\u0026nbsp;X.W. wrote the main manuscript text. X.W. , G.L. and L.W. collected the data. X.W. and G.L. completed the statistical analysis and created the tables1-6. L.W. prepared figures 1-4. G.S. and H.Z. revised the paper.All authors reviewed the manuscript.\u0026nbsp;All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. All authors approved the declaration.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHarryman D. Repairs of rotator cuff Correlation of functional results with integrity of the cuff. J Bone Joint Surg Am 1994, 73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGerber C, Fuchs B, Hodler J. The results of repair of massive tears of the rotator cuff. \u003cem\u003eJournal of Bone \u0026amp; Joint Surgery American Volume\u003c/em\u003e 2000.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePierce J, Anderson M. Update on Diagnostic Imaging of the Rotator Cuff. Clin Sports Med. 2023;42(1):25\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCerciello S, Mocini F, Proietti L, Candura D, Corona K. 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Ann Phys Rehabil Med. 2023;66(1):101631.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRavn MK, Ostergaard TI, Schroeder HD, Nyengaard JR, Lambertsen KL, Frich LH. Supraspinatus and deltoid muscle fiber composition in rotator cuff tear conditions. JSES Int. 2020;4(3):431\u0026ndash;7.\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":"Rotator Cuff Tear, Critical Shoulder Angle, Fatty Infiltration, Acromial Index","lastPublishedDoi":"10.21203/rs.3.rs-8010896/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8010896/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eRotator cuff tear (RCT) is a primary cause of shoulder pain and a leading source of shoulder disability in later stages. Although various computed tomography (CT) based measurements of the shoulder have been identified as predictors for RCT, we hypothesize that a combination of predictors will provide superior diagnostic and predictive performance compared to individual predictors. Thus, the aims of this study are: (i) to integrate various shoulder CT-based measurement parameters for predicting rotator cuff tears, and (ii) to develop a scoring system based on these predictors for estimating the likelihood of posterosuperior rotator cuff tears ( RCT-PT). (iii) To provide a diagnostic basis and predict the risk of posterosuperior rotator cuff tears for patients with contraindications to magnetic resonance imaging(MRI), an inability to cooperate with or complete the examination, or for whom MRI is deemed unnecessary.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis retrospective study analyzed 326 cases who underwent both shoulder CT and MRI examinations at our hospital. Based on the shoulder MRI findings, patients were stratified into two groups: a rotator cuff tear group and a control group. The selected predictors included the Critical Shoulder Angle (CSA), Acromial Index (AI), Goutallier grade of fatty infiltration, Supraspinatus Occupation Ratio, the Hounsfield Unit (HU) ratio of the deltoid to supraspinatus muscle, and other variables such as gender, age, symptom duration, and BMI. These factors were analyzed using univariate and multivariate analyses. The factors identified in the multivariate analysis were subsequently integrated into a scoring system based on their odds ratios (OR).\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMultivariate analysis identified the following independent risk factors: age (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, OR\u0026thinsp;=\u0026thinsp;1.090), fatty infiltration grade (p\u0026thinsp;=\u0026thinsp;0.047, OR\u0026thinsp;=\u0026thinsp;2.252), symptom duration (p\u0026thinsp;=\u0026thinsp;0.012, OR\u0026thinsp;=\u0026thinsp;1.036), critical shoulder angle (p\u0026thinsp;=\u0026thinsp;0.028, OR\u0026thinsp;=\u0026thinsp;1.175), and acromial index (p\u0026thinsp;=\u0026thinsp;0.034, OR\u0026thinsp;=\u0026thinsp;1.068). A 7-point scoring system was subsequently developed. Based on the weighting derived from the multivariate analysis odds ratios, one point each was assigned to age, symptom duration, supraspinatus occupation ratio, critical shoulder angle, and acromial index, while two points were assigned to the fatty infiltration grade. A score of 4 points was established as the threshold for predicting posterosuperior rotator cuff tears, yielding a sensitivity of 0.866 and a specificity of 0.904.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe developed numerical score, which integrates shoulder CT measurements with clinical factors, serves as a practical tool for predicting rotator cuff tears. It facilitates risk assessment based on CT findings and overcomes the limitation of MRI contraindication by providing a reliable predictive alternative for such cases.\u003c/p\u003e","manuscriptTitle":"Assessment of Posterosuperior Rotator Cuff tear risk based on shoulder CT-A Novel Scoring System","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-08 14:18:50","doi":"10.21203/rs.3.rs-8010896/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":"aadb2d95-7532-4e9c-96e1-69387a68899c","owner":[],"postedDate":"January 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-25T17:38:35+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-08 14:18:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8010896","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8010896","identity":"rs-8010896","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}