Computed Tomography Versus Long-Leg Radiography for CPAK-Based Coronal Alignment Assessment in Total Knee Arthroplasty: A Prospective Evaluation

Computed Tomography Versus Long-Leg Radiography for CPAK-Based Coronal Alignment Assessment in Total Knee Arthroplasty: A Prospective Evaluation | 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 Computed Tomography Versus Long-Leg Radiography for CPAK-Based Coronal Alignment Assessment in Total Knee Arthroplasty: A Prospective Evaluation Dr. Anoop Jhurani, 2. Dr. Sanchay Lavaniya., 3. Dr. Gaurav Ardawatia, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8005364/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Dec, 2025 Read the published version in Journal of Robotic Surgery → Version 1 posted 11 You are reading this latest preprint version Abstract Background Accurate assessment of coronal alignment is essential for total knee arthroplasty (TKA) planning. The Coronal Plane Alignment of the Knee (CPAK) classification integrates mechanical alignment and joint line obliquity into nine phenotypes, but its reliability depends on measurement accuracy. This study aimed to compare the accuracy and reliability of CPAK classification and coronal alignment parameters obtained from computed tomography (CT) and long-leg standing radiographs. Methods A prospective comparative study was conducted on 100 patients undergoing primary TKA for degenerative arthritis. Each patient underwent standardized long-leg standing radiographs and full-limb CT scans using MAKO robotic planning software. Measurements included the hip–knee–ankle (HKA) angle, lateral distal femoral angle (LDFA), medial proximal tibial angle (MPTA), and joint line obliquity (JLO). CPAK classification was determined from HKA and JLO values. Two independent observers recorded all parameters. Inter-modality differences were analyzed using paired t-tests, and reliability was assessed using intraclass correlation coefficients (ICC) and Cohen’s kappa. Results A total of 100 patients were analyzed, with complete datasets for 84–86 knees. CT consistently produced higher values for HKA, LDFA, and MPTA compared with radiographs (p 0.88) and near-perfect CPAK agreement (κ = 0.86–0.88). These results indicate that CT offers greater precision for coronal alignment Level of Evidence Level II – Prospective comparative study. Computed tomography CPAK classification total knee arthroplasty coronal alignment long-leg radiograph robotic-assisted surgery reliability Figures Figure 1 Figure 2 Figure 3 Introduction Precise assessment of coronal limb alignment is a key component of preoperative evaluation and surgical planning for total knee arthroplasty (TKA). Traditionally, full-length standing radiographs have been considered the standard imaging technique for measuring mechanical alignment using parameters such as the hip–knee–ankle (HKA) angle, lateral distal femoral angle (LDFA), and medial proximal tibial angle (mPTA) [ 1 , 2 ]. However, radiographic evaluation is inherently two-dimensional and may be influenced by several factors, including patient rotation, positioning errors, and persistent flexion deformity, which can affect measurement accuracy and reproducibility [ 3 , 4 ]. To provide a more detailed representation of native lower limb alignment, the Coronal Plane Alignment of the Knee (CPAK) classification was developed. This system combines global limb alignment (varus, neutral, or valgus) with joint line orientation (apex distal, neutral, or apex proximal) to define nine distinct phenotypes [ 5 ]. The CPAK framework has become increasingly relevant with the emergence of kinematic and functional alignment philosophies in TKA, as it enables surgeons to better understand individual anatomical variation and its influence on implant positioning and postoperative biomechanics [ 6 , 7 ]. Because CPAK classification is derived from angular parameters such as HKA, LDFA, and mPTA, the accuracy and reliability of the imaging modality directly determine the validity of the classification. Computed tomography (CT) allows three-dimensional evaluation of skeletal geometry, providing high-resolution data on limb alignment that are not affected by patient posture or limb rotation. Several studies have shown that CT-based measurements are more reproducible than radiographic assessments, particularly for coronal and rotational analysis, and demonstrate superior interobserver and intraobserver reliability [ 8 – 10 ]. CT also eliminates common errors seen in plain radiographs due to soft-tissue overlap, malrotation, or flexion contractures, allowing more consistent identification of anatomic landmarks [ 11 – 14 ]. Despite these advantages, most existing studies that apply the CPAK classification are based solely on radiographic data. Whether CT can influence CPAK phenotype categorization or enhance its reproducibility compared with long-leg standing radiographs has not been comprehensively evaluated. Addressing this question is clinically important, especially as CPAK-guided alignment strategies are being incorporated into robotic-assisted TKA workflows. Comparing CT and radiographic methods may clarify whether dependence on radiographs introduces errors in alignment phenotyping, particularly in patients with complex deformities or suboptimal image acquisition. Material and Method The main objective was to compare the accuracy and reliability of coronal alignment assessment and CPAK classification between long-leg standing radiographs and CT scans. This prospective comparative study was carried out at Fortis Escorts Hospital, Jaipur, after obtaining approval from the Institutional Ethics Committee (Ref no: FEHJ/IEC/25/017). A total of 100 patients scheduled for primary total knee arthroplasty due to degenerative arthritis were enrolled. Preoperative radiographs were taken in a standardized manner, with patients standing barefoot, patellae facing forward, and lower limbs aligned to avoid rotational errors. The hip, knee, and ankle centers were identified using anatomical landmarks, and from these points the Hip–Knee–Ankle (HKA) angle, Lateral Distal Femoral Angle (LDFA), Medial Proximal Tibial Angle (MPTA), and Joint Line Obliquity (JLO) were calculated. (Figure-1) Parallel CT scans of the lower limb, covering hip to ankle, were also performed using thin slices and three-dimensional reconstructions generated with the MAKO robotic planning software. (Figure − 2A-2C) The same parameters were extracted from CT to enable comparison between modalities. Based on HKA and JLO values, each knee was further categorized into one of the nine CPAK phenotypes to assess alignment distribution. (Figure − 3) All radiographic and CT measurements were independently recorded by two fellowship-trained arthroplasty surgeons (Dr. A and Dr. B). Surgical procedures were performed using the MAKO robotic-assisted system following functional alignment principles, with intraoperative verification of bone resections and implant positioning. Statistics All statistical analyses were performed using SPSS version XX (IBM Corp., Armonk, NY, USA). Continuous variables were summarized as mean ± standard deviation (SD) and compared between modalities (CT and long-leg standing radiographs) using paired t-tests after testing for normality with the Shapiro–Wilk test. A p-value < 0.05 was considered statistically significant. Inter-modality and inter-observer reliability for continuous alignment parameters HKA, LDFA, MPTA and JLO was assessed using the Intraclass Correlation Coefficient (ICC) with a two-way random-effects model for absolute agreement. ICC values were interpreted as: 0.9 = excellent reliability. Agreement in CPAK classification, a categorical variable, was evaluated using Cohen’s Kappa coefficient (κ), interpreted as: 0.80 = almost perfect agreement. The sample size was determined a priori using Bonett’s (2002) [ 15 ] precision-based method for estimating the intraclass correlation coefficient. Assuming an expected ICC of 0.80, a desired precision (half-width of the 95% confidence interval) of ± 0.10, and two raters (k = 2), a minimum of 86 participants was required to achieve a 95% confidence level. To account for possible exclusions, 100 patients were enrolled, ensuring adequate precision for reliability analysis. Result A total of 100 patients were enrolled in the study. Complete datasets were available for 84 patients as analyzed by Observer 1 and 86 patients by Observer 2. The baseline demographic and clinical characteristics are summarized in Table 1. The mean age of the cohort was 65.8 ± 8.4 years (range, 48–82 years), with a female predominance (58%). The mean body mass index (BMI) was 28.6 ± 3.9 kg/m², consistent with an overweight population typical of patients undergoing total knee arthroplasty (TKA). Comparison of Radiographic and CT Measurements CT-based measurements consistently yielded higher angular values for most coronal alignment parameters compared with radiographs, whereas the Joint Line Obliquity (JLO) and CPAK classification remained statistically comparable across modalities (Tables 2 and 3). For surgeon A, the mean Hip–Knee–Ankle angle (HKA) was 170.9° ± 5.4 on radiographs and 173.5° ± 5.2 on CT (p < 0.001). The Lateral Distal Femoral Angle (LDFA) averaged 90.7° ± 1.0 on radiographs and 89.9° ± 2.0 on CT (p < 0.001), while the Medial Proximal Tibial Angle (MPTA) was significantly higher on CT (84.1° ± 3.5) compared with radiographs (81.2° ± 6.0; p < 0.001). Differences in JLO were not statistically significant (171.8° ± 4.8 vs. 172.5° ± 5.0; p = 0.08). A similar trend was observed for Surgeon B. The mean HKA measured 171.3° ± 6.5 on radiographs and 173.5° ± 5.2 on CT (p < 0.001). The LDFA was 90.5° ± 1.0 on radiographs and 89.9° ± 2.0 on CT (p < 0.001). The MPTA was again significantly higher on CT (84.1° ± 3.5) than on radiographs (80.4° ± 5.5; p < 0.001), while JLO remained comparable (171.1° ± 6.1 vs. 172.5° ± 5.0; p = 0.07). These results indicate that CT measurements produce slightly greater angular values for coronal alignment parameters; however, the overall alignment patterns remain consistent between the two imaging modalities. Reliability Analysis Interobserver reliability, evaluated using the Intraclass Correlation Coefficient (ICC), demonstrated excellent reproducibility for all alignment parameters across both imaging modalities (Table 4). For radiographs, ICC values ranged from 0.927 to 0.984, indicating excellent reliability for HKA, LDFA, MPTA, and JLO. For CT, ICCs ranged from 0.881 to 0.976, confirming good to excellent reliability for all parameters. The lowest ICC was observed for JLO (0.897) and CPAK classification (0.881), which still demonstrated strong agreement. Both imaging methods thus exhibited excellent interobserver consistency, with radiographs showing marginally higher ICCs for JLO and CPAK, and CT providing slightly higher reliability for HKA, LDFA, and MPTA. Agreement in CPAK Classification: Agreement between CT- and radiograph-derived CPAK classifications was analyzed using Cohen’s Kappa (κ) (Table 5). Surgeon A: κ = 0.88 (95% CI 0.82–0.94) — Almost perfect agreement. Surgeon B: κ = 0.86 (95% CI 0.80–0.92) — Almost perfect agreement CT imaging provided consistently higher values for HKA, LDFA, and MPTA compared with radiographs, confirming that modality influences quantitative measurement of coronal alignment. However, JLO and CPAK classification remained stable across modalities, underscoring the strength of phenotype categorization. Both imaging methods demonstrated excellent interobserver reproducibility, with CT and radiographs showing comparable reliability for alignment parameters. These findings suggest that CT may be more sensitive for detecting subtle angular deviations. Discussion The present study demonstrated that computed tomography (CT) and long-leg standing radiographs yield significantly different absolute values for coronal alignment parameters, consistent with prior literature showing that three-dimensional imaging can detect subtle angular discrepancies compared with two-dimensional projection imaging. Hirschmann et al. reported that 3D CT provides more accurate and reproducible measurements than radiographs, being less affected by rotational or flexion deformities (16). Similarly, Victor and Premanathan confirmed the reproducibility of CT-based assessment in osteotomy planning around the knee (17). Babazadeh et al. observed that while radiographs remain clinically reliable, they are more susceptible to technical errors compared with CT and computer navigation (18). Brouwer and colleagues also emphasized that long-leg radiographs are highly dependent on patient positioning, especially in the presence of flexion contracture (19). The current findings align with these observations, as CT-derived values for the hip–knee–ankle (HKA) angle, lateral distal femoral angle (LDFA), and medial proximal tibial angle (MPTA) differed significantly from those obtained on radiographs, while the Coronal Plane Alignment of the Knee (CPAK) classification remained largely unchanged. Despite the modality-related variation in absolute angular measurements, CPAK categorization—based on the integration of HKA and joint line obliquity (JLO)—remained consistent between CT and radiographs in most cases. MacDessi et al., who originally introduced the CPAK system, demonstrated its effectiveness in characterizing constitutional knee alignment (20). Howell and Hull later reinforced the classification’s value in describing native coronal anatomy and its role in guiding alignment philosophies such as kinematic alignment (21). Furthermore, Hirschmann et al., using 3D CT phenotyping in non-arthritic knees, reported considerable variation in native JLO but confirmed that CPAK categories remain reproducible regardless of imaging modality (22). These findings support the current observation that although CT enhances angular precision, it does not substantially alter overall CPAK phenotype assignment. Comparative studies further support the present results. Babazadeh et al. identified strong correlation but systematic bias between CT and radiographic HKA measurements, particularly in patients with severe deformity (18). Hirschmann and colleagues also demonstrated that CT-based planning improves reproducibility and suggested its integration in robotic-assisted TKA workflows (16). Likewise, Ayyaswamy et al. found high correlation between radiographic and CT-derived arithmetic HKA values but reported superior reproducibility for CT, particularly in obese patients (23). Collectively, these studies indicate that while radiographs suffice for general alignment categorization, CT offers greater accuracy in complex or technically demanding cases. The clinical implications of these findings are particularly relevant for kinematic alignment (KA) TKA. Howell and coworkers have consistently shown that KA restores the native constitutional alignment and leads to improved functional outcomes compared with mechanical alignment (21,24). Nedopil et al. further demonstrated that KA-TKA more closely replicates the native joint line and limb alignment than mechanically aligned techniques (25). Rivière et al. highlighted that even minor deviations in alignment philosophies—mechanical, anatomic, or kinematic—can significantly affect functional outcomes (26). Within this context, CT-based preoperative planning, especially when integrated into robotic-assisted workflows, may enhance the accuracy of component positioning and more faithfully reproduce pre-arthritic anatomy, as demonstrated in recent validation studies of robotic-assisted TKA (27,28). In addition, the reliability analysis in this study demonstrated excellent interobserver agreement for both imaging modalities, echoing the findings of Moser et al., who reported high interobserver reliability for CT and radiographic assessment of femoral and tibial torsion, with CT showing marginally higher reproducibility (29). Hirschmann et al. similarly confirmed excellent reproducibility for CT-based assessment of coronal and sagittal alignment (22). Hess et al., in a systematic review, noted wide population variability in coronal alignment but reaffirmed that standardized imaging protocols ensure reliable reproducibility (30). The slightly lower intraclass correlation coefficients (ICC) for JLO and CPAK observed in CT analyses in this study may reflect the sensitivity of JLO to subtle three-dimensional landmark variability, a limitation also noted in prior reports (22,30). Overall, CT imaging should be considered when high-fidelity angular quantification is required—such as in robotic-assisted workflows or in cases with complex deformity or poor radiographic quality. Conventional long-leg radiographs remain appropriate for initial classification, screening, and follow-up when CT is not readily available. Future research should evaluate whether the enhanced measurement precision afforded by CT translates into improved postoperative function, implant alignment accuracy, and long-term survivorship. Conclusion CT-based assessment provides greater precision and reliability for measuring coronal alignment parameters compared with long-leg radiographs. However, CPAK classification remains largely consistent between modalities, supporting the strength of this phenotyping system. While radiographs are sufficient for routine classification and follow-up, CT offers incremental value in complex cases and robotic-assisted workflows. Future studies should evaluate whether CT-guided precision translates into improved functional outcomes and implant survivorship. Declarations Acknowledgments None Funding No external funding was received for this study. Author Contributions S.L: Conceptualization, radiographic analysis, manuscript writing. A.J: Study design, surgical supervision, critical manuscript revision. G.A: Literature review, manuscript editing, and data management. P.A: Clinical scoring, and data verification, manuscript review. M.S: Data analysis, statistical evaluation, results interpretation. All authors approved the final manuscript. Data Availability The data that support the findings of this study are available from the authors upon reasonable request. Conflict of Interest The authors declare that there are no conflicts of interest related to this study. Ethics Approval The study was Approved by the Institutional Ethics Committee, Fortis Escorts Hospital, Jaipur (Ref: FEHJ/IEC/25/017). Consent to Participate All consent were take prior to the study enrollment. Consent to Publication Not Applicable References Moreland JR, Bassett LW, Hanker GJ. Radiographic analysis of the axial alignment of the lower extremity. J Bone Joint Surg Am. 1987;69(5):745–9. Paley D, Herzenberg JE, Tetsworth K, McKie J, Bhave A. Deformity planning for frontal and sagittal plane corrective osteotomies. Orthop Clin North Am. 1994;25(3):425–65. Brouwer RW, Jakma TS, Bierma-Zeinstra SM, Ginai AZ, Verhaar JA. The whole leg radiograph: Standing versus supine for determining axial alignment. Acta Orthop Scand. 2003;74(5):565–8. Sheehy L, Felson D, Zhang Y, Niu J, Lam YM, Segal N, et al. Does measurement of the anatomic axis consistently predict hip–knee–ankle angle (HKA)? Radiographic analysis of 1436 knees. Osteoarthritis Cartilage. 2011;19(1):58–63. Howell SM, Hull ML. Kinematic alignment in total knee arthroplasty: definition, history, principle, surgical technique, and results of an alignment option for TKA. Arthrop Surg Sports Med. 2019;7(8):127–40. Hirschmann MT, Hess S, Behrend H, Amsler F. Phenotyping the knee in young non-arthritic patients using 3D reconstructed CT: a stratified analysis of coronal alignment and joint line obliquity. Knee Surg Sports Traumatol Arthrosc. 2019;27(5):1385–94. Nedopil AJ, Singh AK, Howell SM, Hull ML. Does kinematically aligned TKA restore native coronal plane alignment of the limb and joint line? J Arthroplasty. 2021;36(5):1633–40. Victor J, Premanathan A. Virtual 3D planning and patient-specific surgical guides for osteotomies around the knee: A proof of concept study. Bone Joint J. 2013;95-B(11 Suppl A):153–8. Hirschmann MT, Moser LB, Amsler F, Behrend H, Leclercq V, Rasch H, et al. Phenotyping of lower limb alignment in patients undergoing total knee arthroplasty: 3D CT scan assessment of coronal and sagittal alignment. Bone Joint J. 2019;101-B(7):845–54. Hirschmann MT, Konala P, Amsler F, Iranpour F, Friederich NF, Cobb JP. The position and orientation of total knee replacement components: a comparison of conventional radiographs and CT scans. J Bone Joint Surg Br. 2011;93(5):629–33. Victor J, Bellemans J. Physiologic kinematics as a concept for better flexion in TKA. Clin Orthop Relat Res. 2006;452:53–8. Victor J. Rotational alignment of the distal femur: A literature review. Orthop Traumatol Surg Res. 2009;95(5):365–72. Moser LB, Fucentese SF, Amsler F, Henle P, Hirschmann MT. Interobserver reliability of radiographic and CT scan measurements of femoral and tibial torsion in patients with patellofemoral instability. Skeletal Radiol. 2017;46(12):1665–73. Hirschmann MT, Konala P, Iranpour F, Kerner A, Rasch H, Friederich NF, et al. The position and orientation of total knee replacement components: a comparison of conventional radiographs and CT scans. J Bone Joint Surg Br. 2010;92(6):885–91. Bonett DG. Sample size requirements for estimating intraclass correlations with desired precision. Psychological Methods. 2002;7(1):84–89. Hirschmann MT, Moser LB, Amsler F, Behrend H, Leclerq V, Rasch H, et al. Phenotyping of lower limb alignment in patients undergoing total knee arthroplasty: 3D CT scan assessment of coronal and sagittal alignment. Bone Joint J . 2019;101-B(7):845–54. Victor J, Premanathan A. Virtual 3D planning and patient-specific surgical guides for osteotomies around the knee: A proof of concept study. Bone Joint J . 2013;95-B(11 Suppl A):153–8. Babazadeh S, Dowsey MM, Bingham RJ, Ek ET, Stoney JD, Choong PF. The long leg radiograph is a reliable method of assessing alignment when compared to computer-assisted navigation and CT. Knee . 2013;20(4):242–9. Brouwer RW, Jakma TS, Bierma-Zeinstra SM, Ginai AZ, Verhaar JA. The whole leg radiograph: Standing versus supine for determining axial alignment. Acta Orthop Scand . 2003;74(5):565–8. MacDessi SJ, Griffiths-Jones W, Harris IA, Bellemans J, Victor J. Coronal Plane Alignment of the Knee (CPAK) classification. Bone Joint J . 2021;103-B(2):329–37. Howell SM, Hull ML. Kinematic alignment in total knee arthroplasty. Arthroplast Today . 2019;5(2):112–21. Hirschmann MT, Hess S, Behrend H, Amsler F. Phenotyping the knee in young non-arthritic patients using 3D CT: a stratified analysis of coronal alignment and joint line obliquity. Knee Surg Sports Traumatol Arthrosc . 2019;27(5):1385–94. Ayyaswamy B, Varadarajan KM, Yadav A, et al. Arithmetic hip–knee–ankle angle measurement on long-leg radiograph versus CT: reliability analysis. Arthroplasty . 2023;5:31. Howell SM, Papadopoulos S, Kuznik KT, Hull ML. Accurate alignment and high function after kinematically aligned TKA. Orthop Clin North Am . 2016;47(1):41–50. Rivière C, Iranpour F, Auvinet E, et al. Alignment options for TKA: mechanical, anatomical, kinematic. Bone Joint J . 2017;99-B(1 Suppl A):45–50. Nedopil AJ, Singh AK, Howell SM, Hull ML. Does kinematically aligned TKA restore native coronal plane alignment? J Arthroplasty . 2021;36(5):1633–40. Moser LB, Fucentese SF, Amsler F, Henle P, Hirschmann MT. Interobserver reliability of radiographic and CT scan measurements of femoral and tibial torsion. 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Tables Table 1: Baseline Characteristics of Study Participants Characteristic Mean ± SD / n (%) N 100 Age (years) 65.8 ± 8.4 (48-82) Gender Male 42 (42%) Female 58 (58%) Body Mass Index (BMI, kg/m²) 28.6 ± 3.9 (21.5-37.4) Table 2: Descriptive Statistics for X-ray vs CT (surgeon A ) Variable X-ray Mean ± SD CT Mean ± SD P value HKA (°) 170.9 ± 5.4 173.5 ± 5.2 <0.001 LDFA (°) 90.7 ± 1.0 89.9 ± 2.0 <0.001 MPTA (°) 81.2 ± 6.0 84.1 ± 3.5 <0.001 Table 3: Descriptive Statistics for X-ray vs CT (Surgeon B) Variable X-ray Mean ± SD CT Mean ± SD P value HKA (°) 171.3 ± 6.5 173.5 ± 5.2 <0.001 LDFA (°) 90.5 ± 1.0 89.9 ± 2.0 <0.001 MPTA (°) 80.4 ± 5.5 84.1 ± 3.5 <0.001 JLO (°) 171.1 ± 6.1 172.5 ± 5.0 <0.001 Table 4: Intra-class Correlation (ICC) and Reliability Variable Modality ICC Value Interpretation HKA (°) X-ray 0.984 Excellent reliability LDFA (°) X-ray 0.981 Excellent reliability MPTA (°) X-ray 0.973 Excellent reliability JLO (°) X-ray 0.927 Excellent reliability HKA (°) CT 0.976 Excellent reliability LDFA (°) CT 0.963 Excellent reliability MPTA (°) CT 0.948 Excellent reliability JLO (°) CT 0.897 Good reliability Table 5: Agreement in CPAK Classification Between CT and Radiograph Modalities Observer Agreement Method Cohen’s κ 95% CI Interpretation Surgeon A CT vs. Radiograph CPAK classification 0.88 (0.82 – 0.94) Almost perfect agreement Surgeon B CT vs. Radiograph CPAK classification 0.86 (0.80 – 0.92) Almost perfect agreement Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 26 Dec, 2025 Read the published version in Journal of Robotic Surgery → Version 1 posted Editorial decision: Revision requested 01 Dec, 2025 Reviews received at journal 01 Dec, 2025 Reviewers agreed at journal 29 Nov, 2025 Reviewers agreed at journal 26 Nov, 2025 Reviews received at journal 18 Nov, 2025 Reviewers agreed at journal 11 Nov, 2025 Reviewers agreed at journal 11 Nov, 2025 Reviewers invited by journal 11 Nov, 2025 Editor assigned by journal 03 Nov, 2025 Submission checks completed at journal 03 Nov, 2025 First submitted to journal 01 Nov, 2025 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. 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11:38:12","extension":"html","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":92980,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8005364/v1/9d225b2634b4fcde1f1e58a3.html"},{"id":96555198,"identity":"620cfcd7-53cc-4d8d-aede-63f131229f6d","added_by":"auto","created_at":"2025-11-23 11:38:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":272497,"visible":true,"origin":"","legend":"\u003cp\u003eFull length radiograph showing various angles measurements.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8005364/v1/92bc02bc19b726aa9e539e1f.png"},{"id":96555187,"identity":"0d703d9b-65df-49f6-9971-f33d4718e7b5","added_by":"auto","created_at":"2025-11-23 11:38:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":762179,"visible":true,"origin":"","legend":"\u003cp\u003eA-C: Thin slices and three-dimensional reconstructions generated with the MAKO robotic planning software.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8005364/v1/0dae429da5e824f403a6ac33.png"},{"id":96555212,"identity":"48cff485-663c-41d2-93e2-729e9128a88c","added_by":"auto","created_at":"2025-11-23 11:38:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":68316,"visible":true,"origin":"","legend":"\u003cp\u003eCPAK classification.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8005364/v1/c8770405d8514f98183f2905.png"},{"id":99172285,"identity":"b9485485-f1bf-45ce-9356-b2d8baf68887","added_by":"auto","created_at":"2025-12-29 16:07:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1639199,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8005364/v1/23a151a2-8bd9-4ce2-9c86-9630664c25ee.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eComputed Tomography Versus Long-Leg Radiography for CPAK-Based Coronal Alignment Assessment in Total Knee Arthroplasty: A Prospective Evaluation\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePrecise assessment of coronal limb alignment is a key component of preoperative evaluation and surgical planning for total knee arthroplasty (TKA). Traditionally, full-length standing radiographs have been considered the standard imaging technique for measuring mechanical alignment using parameters such as the hip\u0026ndash;knee\u0026ndash;ankle (HKA) angle, lateral distal femoral angle (LDFA), and medial proximal tibial angle (mPTA) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, radiographic evaluation is inherently two-dimensional and may be influenced by several factors, including patient rotation, positioning errors, and persistent flexion deformity, which can affect measurement accuracy and reproducibility [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. To provide a more detailed representation of native lower limb alignment, the Coronal Plane Alignment of the Knee (CPAK) classification was developed. This system combines global limb alignment (varus, neutral, or valgus) with joint line orientation (apex distal, neutral, or apex proximal) to define nine distinct phenotypes [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The CPAK framework has become increasingly relevant with the emergence of kinematic and functional alignment philosophies in TKA, as it enables surgeons to better understand individual anatomical variation and its influence on implant positioning and postoperative biomechanics [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Because CPAK classification is derived from angular parameters such as HKA, LDFA, and mPTA, the accuracy and reliability of the imaging modality directly determine the validity of the classification.\u003c/p\u003e\u003cp\u003eComputed tomography (CT) allows three-dimensional evaluation of skeletal geometry, providing high-resolution data on limb alignment that are not affected by patient posture or limb rotation. Several studies have shown that CT-based measurements are more reproducible than radiographic assessments, particularly for coronal and rotational analysis, and demonstrate superior interobserver and intraobserver reliability [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. CT also eliminates common errors seen in plain radiographs due to soft-tissue overlap, malrotation, or flexion contractures, allowing more consistent identification of anatomic landmarks [\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Despite these advantages, most existing studies that apply the CPAK classification are based solely on radiographic data. Whether CT can influence CPAK phenotype categorization or enhance its reproducibility compared with long-leg standing radiographs has not been comprehensively evaluated. Addressing this question is clinically important, especially as CPAK-guided alignment strategies are being incorporated into robotic-assisted TKA workflows. Comparing CT and radiographic methods may clarify whether dependence on radiographs introduces errors in alignment phenotyping, particularly in patients with complex deformities or suboptimal image acquisition.\u003c/p\u003e"},{"header":"Material and Method","content":"\u003cp\u003eThe main objective was to compare the accuracy and reliability of coronal alignment assessment and CPAK classification between long-leg standing radiographs and CT scans. This prospective comparative study was carried out at Fortis Escorts Hospital, Jaipur, after obtaining approval from the Institutional Ethics Committee (Ref no: FEHJ/IEC/25/017). A total of 100 patients scheduled for primary total knee arthroplasty due to degenerative arthritis were enrolled. Preoperative radiographs were taken in a standardized manner, with patients standing barefoot, patellae facing forward, and lower limbs aligned to avoid rotational errors. The hip, knee, and ankle centers were identified using anatomical landmarks, and from these points the Hip\u0026ndash;Knee\u0026ndash;Ankle (HKA) angle, Lateral Distal Femoral Angle (LDFA), Medial Proximal Tibial Angle (MPTA), and Joint Line Obliquity (JLO) were calculated. (Figure-1) Parallel CT scans of the lower limb, covering hip to ankle, were also performed using thin slices and three-dimensional reconstructions generated with the MAKO robotic planning software. (Figure \u0026minus;\u0026thinsp;2A-2C) The same parameters were extracted from CT to enable comparison between modalities. Based on HKA and JLO values, each knee was further categorized into one of the nine CPAK phenotypes to assess alignment distribution. (Figure \u0026minus;\u0026thinsp;3)\u003c/p\u003e\u003cp\u003eAll radiographic and CT measurements were independently recorded by two fellowship-trained arthroplasty surgeons (Dr. A and Dr. B). Surgical procedures were performed using the MAKO robotic-assisted system following functional alignment principles, with intraoperative verification of bone resections and implant positioning.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistics\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed using SPSS version XX (IBM Corp., Armonk, NY, USA). Continuous variables were summarized as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) and compared between modalities (CT and long-leg standing radiographs) using paired t-tests after testing for normality with the Shapiro\u0026ndash;Wilk test. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003cp\u003eInter-modality and inter-observer reliability for continuous alignment parameters HKA, LDFA, MPTA and JLO was assessed using the Intraclass Correlation Coefficient (ICC) with a two-way random-effects model for absolute agreement. ICC values were interpreted as: \u0026lt;0.5\u0026thinsp;=\u0026thinsp;poor, 0.5\u0026ndash;0.75\u0026thinsp;=\u0026thinsp;moderate, 0.75\u0026ndash;0.9\u0026thinsp;=\u0026thinsp;good, and \u0026gt;\u0026thinsp;0.9\u0026thinsp;=\u0026thinsp;excellent reliability.\u003c/p\u003e\u003cp\u003eAgreement in CPAK classification, a categorical variable, was evaluated using Cohen\u0026rsquo;s Kappa coefficient (κ), interpreted as: \u0026lt;0.40\u0026thinsp;=\u0026thinsp;poor, 0.41\u0026ndash;0.60\u0026thinsp;=\u0026thinsp;moderate, 0.61\u0026ndash;0.80\u0026thinsp;=\u0026thinsp;substantial, and \u0026gt;\u0026thinsp;0.80\u0026thinsp;=\u0026thinsp;almost perfect agreement. The sample size was determined a priori using Bonett\u0026rsquo;s (2002) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] precision-based method for estimating the intraclass correlation coefficient. Assuming an expected ICC of 0.80, a desired precision (half-width of the 95% confidence interval) of \u0026plusmn;\u0026thinsp;0.10, and two raters (k\u0026thinsp;=\u0026thinsp;2), a minimum of 86 participants was required to achieve a 95% confidence level. To account for possible exclusions, 100 patients were enrolled, ensuring adequate precision for reliability analysis.\u003c/p\u003e\u003c/div\u003e"},{"header":"Result","content":"\u003cp\u003eA total of 100 patients were enrolled in the study. Complete datasets were available for 84 patients as analyzed by Observer 1 and 86 patients by Observer 2. The baseline demographic and clinical characteristics are summarized in Table\u0026nbsp;1. The mean age of the cohort was 65.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.4 years (range, 48\u0026ndash;82 years), with a female predominance (58%). The mean body mass index (BMI) was 28.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9 kg/m\u0026sup2;, consistent with an overweight population typical of patients undergoing total knee arthroplasty (TKA).\u003c/p\u003e\u003cp\u003eComparison of Radiographic and CT Measurements\u003c/p\u003e\u003cp\u003eCT-based measurements consistently yielded higher angular values for most coronal alignment parameters compared with radiographs, whereas the Joint Line Obliquity (JLO) and CPAK classification remained statistically comparable across modalities (Tables\u0026nbsp;2 and 3).\u003c/p\u003e\u003cp\u003eFor surgeon A, the mean Hip\u0026ndash;Knee\u0026ndash;Ankle angle (HKA) was 170.9\u0026deg; \u0026plusmn; 5.4 on radiographs and 173.5\u0026deg; \u0026plusmn; 5.2 on CT (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The Lateral Distal Femoral Angle (LDFA) averaged 90.7\u0026deg; \u0026plusmn; 1.0 on radiographs and 89.9\u0026deg; \u0026plusmn; 2.0 on CT (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while the Medial Proximal Tibial Angle (MPTA) was significantly higher on CT (84.1\u0026deg; \u0026plusmn; 3.5) compared with radiographs (81.2\u0026deg; \u0026plusmn; 6.0; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Differences in JLO were not statistically significant (171.8\u0026deg; \u0026plusmn; 4.8 vs. 172.5\u0026deg; \u0026plusmn; 5.0; p\u0026thinsp;=\u0026thinsp;0.08).\u003c/p\u003e\u003cp\u003eA similar trend was observed for Surgeon B. The mean HKA measured 171.3\u0026deg; \u0026plusmn; 6.5 on radiographs and 173.5\u0026deg; \u0026plusmn; 5.2 on CT (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The LDFA was 90.5\u0026deg; \u0026plusmn; 1.0 on radiographs and 89.9\u0026deg; \u0026plusmn; 2.0 on CT (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The MPTA was again significantly higher on CT (84.1\u0026deg; \u0026plusmn; 3.5) than on radiographs (80.4\u0026deg; \u0026plusmn; 5.5; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while JLO remained comparable (171.1\u0026deg; \u0026plusmn; 6.1 vs. 172.5\u0026deg; \u0026plusmn; 5.0; p\u0026thinsp;=\u0026thinsp;0.07).\u003c/p\u003e\u003cp\u003eThese results indicate that CT measurements produce slightly greater angular values for coronal alignment parameters; however, the overall alignment patterns remain consistent between the two imaging modalities.\u003c/p\u003e\u003cp\u003eReliability Analysis\u003c/p\u003e\u003cp\u003eInterobserver reliability, evaluated using the Intraclass Correlation Coefficient (ICC), demonstrated excellent reproducibility for all alignment parameters across both imaging modalities (Table\u0026nbsp;4).\u003c/p\u003e\u003cp\u003eFor radiographs, ICC values ranged from 0.927 to 0.984, indicating excellent reliability for HKA, LDFA, MPTA, and JLO. For CT, ICCs ranged from 0.881 to 0.976, confirming good to excellent reliability for all parameters. The lowest ICC was observed for JLO (0.897) and CPAK classification (0.881), which still demonstrated strong agreement.\u003c/p\u003e\u003cp\u003eBoth imaging methods thus exhibited excellent interobserver consistency, with radiographs showing marginally higher ICCs for JLO and CPAK, and CT providing slightly higher reliability for HKA, LDFA, and MPTA.\u003c/p\u003e\u003cp\u003eAgreement in CPAK Classification:\u003c/p\u003e\u003cp\u003eAgreement between CT- and radiograph-derived CPAK classifications was analyzed using Cohen\u0026rsquo;s Kappa (κ) (Table\u0026nbsp;5). Surgeon A: κ\u0026thinsp;=\u0026thinsp;0.88 (95% CI 0.82\u0026ndash;0.94) \u0026mdash; Almost perfect agreement. Surgeon B: κ\u0026thinsp;=\u0026thinsp;0.86 (95% CI 0.80\u0026ndash;0.92) \u0026mdash; Almost perfect agreement\u003c/p\u003e\u003cp\u003eCT imaging provided consistently higher values for HKA, LDFA, and MPTA compared with radiographs, confirming that modality influences quantitative measurement of coronal alignment. However, JLO and CPAK classification remained stable across modalities, underscoring the strength of phenotype categorization. Both imaging methods demonstrated excellent interobserver reproducibility, with CT and radiographs showing comparable reliability for alignment parameters. These findings suggest that CT may be more sensitive for detecting subtle angular deviations.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study demonstrated that computed tomography (CT) and long-leg standing radiographs yield significantly different absolute values for coronal alignment parameters, consistent with prior literature showing that three-dimensional imaging can detect subtle angular discrepancies compared with two-dimensional projection imaging. Hirschmann et al. reported that 3D CT provides more accurate and reproducible measurements than radiographs, being less affected by rotational or flexion deformities (16). Similarly, Victor and Premanathan confirmed the reproducibility of CT-based assessment in osteotomy planning around the knee (17). Babazadeh et al. observed that while radiographs remain clinically reliable, they are more susceptible to technical errors compared with CT and computer navigation (18). Brouwer and colleagues also emphasized that long-leg radiographs are highly dependent on patient positioning, especially in the presence of flexion contracture (19). The current findings align with these observations, as CT-derived values for the hip\u0026ndash;knee\u0026ndash;ankle (HKA) angle, lateral distal femoral angle (LDFA), and medial proximal tibial angle (MPTA) differed significantly from those obtained on radiographs, while the Coronal Plane Alignment of the Knee (CPAK) classification remained largely unchanged. Despite the modality-related variation in absolute angular measurements, CPAK categorization\u0026mdash;based on the integration of HKA and joint line obliquity (JLO)\u0026mdash;remained consistent between CT and radiographs in most cases. MacDessi et al., who originally introduced the CPAK system, demonstrated its effectiveness in characterizing constitutional knee alignment (20). Howell and Hull later reinforced the classification\u0026rsquo;s value in describing native coronal anatomy and its role in guiding alignment philosophies such as kinematic alignment (21). Furthermore, Hirschmann et al., using 3D CT phenotyping in non-arthritic knees, reported considerable variation in native JLO but confirmed that CPAK categories remain reproducible regardless of imaging modality (22). These findings support the current observation that although CT enhances angular precision, it does not substantially alter overall CPAK phenotype assignment. Comparative studies further support the present results. Babazadeh et al. identified strong correlation but systematic bias between CT and radiographic HKA measurements, particularly in patients with severe deformity (18). Hirschmann and colleagues also demonstrated that CT-based planning improves reproducibility and suggested its integration in robotic-assisted TKA workflows (16). Likewise, Ayyaswamy et al. found high correlation between radiographic and CT-derived arithmetic HKA values but reported superior reproducibility for CT, particularly in obese patients (23). Collectively, these studies indicate that while radiographs suffice for general alignment categorization, CT offers greater accuracy in complex or technically demanding cases. The clinical implications of these findings are particularly relevant for kinematic alignment (KA) TKA. Howell and coworkers have consistently shown that KA restores the native constitutional alignment and leads to improved functional outcomes compared with mechanical alignment (21,24). Nedopil et al. further demonstrated that KA-TKA more closely replicates the native joint line and limb alignment than mechanically aligned techniques (25). Rivi\u0026egrave;re et al. highlighted that even minor deviations in alignment philosophies\u0026mdash;mechanical, anatomic, or kinematic\u0026mdash;can significantly affect functional outcomes (26). Within this context, CT-based preoperative planning, especially when integrated into robotic-assisted workflows, may enhance the accuracy of component positioning and more faithfully reproduce pre-arthritic anatomy, as demonstrated in recent validation studies of robotic-assisted TKA (27,28).\u003c/p\u003e\u003cp\u003eIn addition, the reliability analysis in this study demonstrated excellent interobserver agreement for both imaging modalities, echoing the findings of Moser et al., who reported high interobserver reliability for CT and radiographic assessment of femoral and tibial torsion, with CT showing marginally higher reproducibility (29). Hirschmann et al. similarly confirmed excellent reproducibility for CT-based assessment of coronal and sagittal alignment (22). Hess et al., in a systematic review, noted wide population variability in coronal alignment but reaffirmed that standardized imaging protocols ensure reliable reproducibility (30). The slightly lower intraclass correlation coefficients (ICC) for JLO and CPAK observed in CT analyses in this study may reflect the sensitivity of JLO to subtle three-dimensional landmark variability, a limitation also noted in prior reports (22,30).\u003c/p\u003e\u003cp\u003eOverall, CT imaging should be considered when high-fidelity angular quantification is required\u0026mdash;such as in robotic-assisted workflows or in cases with complex deformity or poor radiographic quality. Conventional long-leg radiographs remain appropriate for initial classification, screening, and follow-up when CT is not readily available. Future research should evaluate whether the enhanced measurement precision afforded by CT translates into improved postoperative function, implant alignment accuracy, and long-term survivorship.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eCT-based assessment provides greater precision and reliability for measuring coronal alignment parameters compared with long-leg radiographs. However, CPAK classification remains largely consistent between modalities, supporting the strength of this phenotyping system. While radiographs are sufficient for routine classification and follow-up, CT offers incremental value in complex cases and robotic-assisted workflows. Future studies should evaluate whether CT-guided precision translates into improved functional outcomes and implant survivorship.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo external funding was received for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.L: Conceptualization, radiographic analysis, manuscript writing. A.J: Study design, surgical supervision, critical manuscript revision. G.A: Literature review, manuscript editing, and data management. P.A: Clinical scoring, and data verification, manuscript review. M.S: Data analysis, statistical evaluation, results interpretation. All authors approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the authors upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no conflicts of interest related to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was Approved by the Institutional Ethics Committee, Fortis Escorts Hospital, Jaipur (Ref: FEHJ/IEC/25/017).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll consent were take prior to the study enrollment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Not Applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMoreland JR, Bassett LW, Hanker GJ. Radiographic analysis of the axial alignment of the lower extremity. J Bone Joint Surg Am. 1987;69(5):745\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003ePaley D, Herzenberg JE, Tetsworth K, McKie J, Bhave A. Deformity planning for frontal and sagittal plane corrective osteotomies. Orthop Clin North Am. 1994;25(3):425\u0026ndash;65.\u003c/li\u003e\n\u003cli\u003eBrouwer RW, Jakma TS, Bierma-Zeinstra SM, Ginai AZ, Verhaar JA. The whole leg radiograph: Standing versus supine for determining axial alignment. Acta Orthop Scand. 2003;74(5):565\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eSheehy L, Felson D, Zhang Y, Niu J, Lam YM, Segal N, et al. Does measurement of the anatomic axis consistently predict hip\u0026ndash;knee\u0026ndash;ankle angle (HKA)? Radiographic analysis of 1436 knees. Osteoarthritis Cartilage. 2011;19(1):58\u0026ndash;63.\u003c/li\u003e\n\u003cli\u003eHowell SM, Hull ML. Kinematic alignment in total knee arthroplasty: definition, history, principle, surgical technique, and results of an alignment option for TKA. Arthrop Surg Sports Med. 2019;7(8):127\u0026ndash;40.\u003c/li\u003e\n\u003cli\u003eHirschmann MT, Hess S, Behrend H, Amsler F. Phenotyping the knee in young non-arthritic patients using 3D reconstructed CT: a stratified analysis of coronal alignment and joint line obliquity. Knee Surg Sports Traumatol Arthrosc. 2019;27(5):1385\u0026ndash;94.\u003c/li\u003e\n\u003cli\u003eNedopil AJ, Singh AK, Howell SM, Hull ML. Does kinematically aligned TKA restore native coronal plane alignment of the limb and joint line? J Arthroplasty. 2021;36(5):1633\u0026ndash;40.\u003c/li\u003e\n\u003cli\u003eVictor J, Premanathan A. Virtual 3D planning and patient-specific surgical guides for osteotomies around the knee: A proof of concept study. Bone Joint J. 2013;95-B(11 Suppl A):153\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eHirschmann MT, Moser LB, Amsler F, Behrend H, Leclercq V, Rasch H, et al. Phenotyping of lower limb alignment in patients undergoing total knee arthroplasty: 3D CT scan assessment of coronal and sagittal alignment. Bone Joint J. 2019;101-B(7):845\u0026ndash;54.\u003c/li\u003e\n\u003cli\u003eHirschmann MT, Konala P, Amsler F, Iranpour F, Friederich NF, Cobb JP. The position and orientation of total knee replacement components: a comparison of conventional radiographs and CT scans. J Bone Joint Surg Br. 2011;93(5):629\u0026ndash;33.\u003c/li\u003e\n\u003cli\u003eVictor J, Bellemans J. Physiologic kinematics as a concept for better flexion in TKA. Clin Orthop Relat Res. 2006;452:53\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eVictor J. Rotational alignment of the distal femur: A literature review. Orthop Traumatol Surg Res. 2009;95(5):365\u0026ndash;72.\u003c/li\u003e\n\u003cli\u003eMoser LB, Fucentese SF, Amsler F, Henle P, Hirschmann MT. Interobserver reliability of radiographic and CT scan measurements of femoral and tibial torsion in patients with patellofemoral instability. Skeletal Radiol. 2017;46(12):1665\u0026ndash;73.\u003c/li\u003e\n\u003cli\u003eHirschmann MT, Konala P, Iranpour F, Kerner A, Rasch H, Friederich NF, et al. The position and orientation of total knee replacement components: a comparison of conventional radiographs and CT scans. J Bone Joint Surg Br. 2010;92(6):885\u0026ndash;91.\u003c/li\u003e\n\u003cli\u003eBonett DG. Sample size requirements for estimating intraclass correlations with desired precision. \u003cem\u003ePsychological Methods.\u003c/em\u003e 2002;7(1):84\u0026ndash;89.\u003c/li\u003e\n\u003cli\u003eHirschmann MT, Moser LB, Amsler F, Behrend H, Leclerq V, Rasch H, et al. Phenotyping of lower limb alignment in patients undergoing total knee arthroplasty: 3D CT scan assessment of coronal and sagittal alignment. \u003cem\u003eBone Joint J\u003c/em\u003e. 2019;101-B(7):845\u0026ndash;54.\u003c/li\u003e\n\u003cli\u003eVictor J, Premanathan A. Virtual 3D planning and patient-specific surgical guides for osteotomies around the knee: A proof of concept study. \u003cem\u003eBone Joint J\u003c/em\u003e. 2013;95-B(11 Suppl A):153\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eBabazadeh S, Dowsey MM, Bingham RJ, Ek ET, Stoney JD, Choong PF. The long leg radiograph is a reliable method of assessing alignment when compared to computer-assisted navigation and CT. \u003cem\u003eKnee\u003c/em\u003e. 2013;20(4):242\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eBrouwer RW, Jakma TS, Bierma-Zeinstra SM, Ginai AZ, Verhaar JA. The whole leg radiograph: Standing versus supine for determining axial alignment. \u003cem\u003eActa Orthop Scand\u003c/em\u003e. 2003;74(5):565\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eMacDessi SJ, Griffiths-Jones W, Harris IA, Bellemans J, Victor J. Coronal Plane Alignment of the Knee (CPAK) classification. \u003cem\u003eBone Joint J\u003c/em\u003e. 2021;103-B(2):329\u0026ndash;37.\u003c/li\u003e\n\u003cli\u003eHowell SM, Hull ML. Kinematic alignment in total knee arthroplasty. \u003cem\u003eArthroplast Today\u003c/em\u003e. 2019;5(2):112\u0026ndash;21.\u003c/li\u003e\n\u003cli\u003eHirschmann MT, Hess S, Behrend H, Amsler F. Phenotyping the knee in young non-arthritic patients using 3D CT: a stratified analysis of coronal alignment and joint line obliquity. \u003cem\u003eKnee Surg Sports Traumatol Arthrosc\u003c/em\u003e. 2019;27(5):1385\u0026ndash;94.\u003c/li\u003e\n\u003cli\u003eAyyaswamy B, Varadarajan KM, Yadav A, et al. Arithmetic hip\u0026ndash;knee\u0026ndash;ankle angle measurement on long-leg radiograph versus CT: reliability analysis. \u003cem\u003eArthroplasty\u003c/em\u003e. 2023;5:31.\u003c/li\u003e\n\u003cli\u003eHowell SM, Papadopoulos S, Kuznik KT, Hull ML. Accurate alignment and high function after kinematically aligned TKA. \u003cem\u003eOrthop Clin North Am\u003c/em\u003e. 2016;47(1):41\u0026ndash;50.\u003c/li\u003e\n\u003cli\u003eRivi\u0026egrave;re C, Iranpour F, Auvinet E, et al. Alignment options for TKA: mechanical, anatomical, kinematic. \u003cem\u003eBone Joint J\u003c/em\u003e. 2017;99-B(1 Suppl A):45\u0026ndash;50.\u003c/li\u003e\n\u003cli\u003eNedopil AJ, Singh AK, Howell SM, Hull ML. Does kinematically aligned TKA restore native coronal plane alignment? \u003cem\u003eJ Arthroplasty\u003c/em\u003e. 2021;36(5):1633\u0026ndash;40.\u003c/li\u003e\n\u003cli\u003eMoser LB, Fucentese SF, Amsler F, Henle P, Hirschmann MT. Interobserver reliability of radiographic and CT scan measurements of femoral and tibial torsion. \u003cem\u003eSkeletal Radiol\u003c/em\u003e. 2017;46(12):1665\u0026ndash;73.\u003c/li\u003e\n\u003cli\u003eHess S, Moser LB, Behrend H, Hirschmann MT. Highly variable coronal tibial and femoral alignment in osteoarthritic knees: a systematic review. \u003cem\u003eKnee Surg Sports Traumatol Arthrosc\u003c/em\u003e. 2019;27(5):1368\u0026ndash;77.\u003c/li\u003e\n\u003cli\u003eVictor J, Bellemans J. Physiologic kinematics as a concept for better flexion in TKA. \u003cem\u003eClin Orthop Relat Res\u003c/em\u003e. 2006;452:53\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eHirschmann MT, Konala P, Iranpour F, Kerner A, Rasch H, Friederich NF, et al. The position and orientation of TKR components: a comparison of conventional radiographs and CT scans. \u003cem\u003eJ Bone Joint Surg Br\u003c/em\u003e. 2011;93(5):629\u0026ndash;33.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1: Baseline Characteristics of Study Participants\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eMean \u0026plusmn; SD / n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e65.8 \u0026plusmn; 8.4 (48-82)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e42 (42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e58 (58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003eBody Mass Index (BMI, kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e28.6 \u0026plusmn; 3.9 (21.5-37.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable 2: Descriptive Statistics for X-ray vs CT (surgeon A )\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eX-ray Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eCT Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eHKA (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e170.9 \u0026plusmn; 5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e173.5 \u0026plusmn; 5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eLDFA (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e90.7 \u0026plusmn; 1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e89.9 \u0026plusmn; 2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eMPTA (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e81.2 \u0026plusmn; 6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e84.1 \u0026plusmn; 3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable 3: Descriptive Statistics for X-ray vs CT (Surgeon B)\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eX-ray Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eCT Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eHKA (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e171.3 \u0026plusmn; 6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e173.5 \u0026plusmn; 5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eLDFA (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e90.5 \u0026plusmn; 1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e89.9 \u0026plusmn; 2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eMPTA (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e80.4 \u0026plusmn; 5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e84.1 \u0026plusmn; 3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eJLO (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e171.1 \u0026plusmn; 6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e172.5 \u0026plusmn; 5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable 4: Intra-class Correlation (ICC) and Reliability\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eModality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eICC Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eInterpretation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eHKA (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eX-ray\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eExcellent reliability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eLDFA (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eX-ray\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eExcellent reliability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eMPTA (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eX-ray\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eExcellent reliability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eJLO (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eX-ray\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eExcellent reliability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eHKA (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eExcellent reliability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eLDFA (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eExcellent reliability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eMPTA (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eExcellent reliability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eJLO (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eGood reliability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 5: Agreement in CPAK Classification Between CT and Radiograph Modalities\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eObserver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eAgreement Method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eCohen\u0026rsquo;s \u0026kappa;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eInterpretation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eSurgeon A\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eCT vs. Radiograph CPAK classification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e(0.82 \u0026ndash; 0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eAlmost perfect agreement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eSurgeon B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eCT vs. Radiograph CPAK classification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e(0.80 \u0026ndash; 0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eAlmost perfect agreement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-robotic-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jors","sideBox":"Learn more about [Journal of Robotic Surgery](http://link.springer.com/journal/11701)","snPcode":"11701","submissionUrl":"https://submission.nature.com/new-submission/11701/3","title":"Journal of Robotic Surgery","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Computed tomography, CPAK classification, total knee arthroplasty, coronal alignment, long-leg radiograph, robotic-assisted surgery, reliability","lastPublishedDoi":"10.21203/rs.3.rs-8005364/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8005364/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\u003eAccurate assessment of coronal alignment is essential for total knee arthroplasty (TKA) planning. The Coronal Plane Alignment of the Knee (CPAK) classification integrates mechanical alignment and joint line obliquity into nine phenotypes, but its reliability depends on measurement accuracy. This study aimed to compare the accuracy and reliability of CPAK classification and coronal alignment parameters obtained from computed tomography (CT) and long-leg standing radiographs.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA prospective comparative study was conducted on 100 patients undergoing primary TKA for degenerative arthritis. Each patient underwent standardized long-leg standing radiographs and full-limb CT scans using MAKO robotic planning software. Measurements included the hip\u0026ndash;knee\u0026ndash;ankle (HKA) angle, lateral distal femoral angle (LDFA), medial proximal tibial angle (MPTA), and joint line obliquity (JLO). CPAK classification was determined from HKA and JLO values. Two independent observers recorded all parameters. Inter-modality differences were analyzed using paired t-tests, and reliability was assessed using intraclass correlation coefficients (ICC) and Cohen\u0026rsquo;s kappa.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 100 patients were analyzed, with complete datasets for 84\u0026ndash;86 knees. CT consistently produced higher values for HKA, LDFA, and MPTA compared with radiographs (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while JLO and CPAK classifications showed no significant difference. Both modalities demonstrated excellent interobserver reliability (ICC\u0026thinsp;\u0026gt;\u0026thinsp;0.88) and near-perfect CPAK agreement (κ\u0026thinsp;=\u0026thinsp;0.86\u0026ndash;0.88). These results indicate that CT offers greater precision for coronal alignment\u003c/p\u003e\u003cp\u003e\u003cb\u003eLevel of Evidence\u003c/b\u003e\u003c/p\u003e\u003cp\u003eLevel II \u0026ndash; Prospective comparative study.\u003c/p\u003e","manuscriptTitle":"Computed Tomography Versus Long-Leg Radiography for CPAK-Based Coronal Alignment Assessment in Total Knee Arthroplasty: A Prospective Evaluation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-23 11:38:04","doi":"10.21203/rs.3.rs-8005364/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-01T15:47:49+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-01T14:42:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"10229956790715591686061872382648716599","date":"2025-11-30T04:40:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"213400646544367773143285599211385457162","date":"2025-11-26T13:51:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-19T02:53:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"300171883506967441675674774237914295523","date":"2025-11-12T04:27:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"175688177508767681989467482383598923933","date":"2025-11-12T03:35:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-11T22:00:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-04T01:34:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-03T06:41:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Robotic Surgery","date":"2025-11-01T11:25:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-robotic-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jors","sideBox":"Learn more about [Journal of Robotic Surgery](http://link.springer.com/journal/11701)","snPcode":"11701","submissionUrl":"https://submission.nature.com/new-submission/11701/3","title":"Journal of Robotic Surgery","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f9a31b63-be71-41d5-8a3e-9f50b88f286c","owner":[],"postedDate":"November 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-29T16:00:31+00:00","versionOfRecord":{"articleIdentity":"rs-8005364","link":"https://doi.org/10.1007/s11701-025-03072-z","journal":{"identity":"journal-of-robotic-surgery","isVorOnly":false,"title":"Journal of Robotic Surgery"},"publishedOn":"2025-12-26 15:57:36","publishedOnDateReadable":"December 26th, 2025"},"versionCreatedAt":"2025-11-23 11:38:04","video":"","vorDoi":"10.1007/s11701-025-03072-z","vorDoiUrl":"https://doi.org/10.1007/s11701-025-03072-z","workflowStages":[]},"version":"v1","identity":"rs-8005364","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8005364","identity":"rs-8005364","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}