Is the EOS imaging system as accurate as conventional radiography in grading osteoarthritis of the knee?

What is New in Osteoarthritis Imaging?

Osteoarthritis (OA) is the leading joint disorder globally, affecting a significant proportion of the population. Recent studies have changed our understanding of OA, viewing it as a complex pathology of the whole joint with a multifaceted etiology, encompassing genetic, biological, and biomechanical elements. This review highlights the role of imaging in diagnosing and monitoring OA. Today's role of radiography is discussed, while also elaborating on the advances in computed tomography and magnetic resonance imaging, discussing semiquantitative methods, quantitative morphologic and compositional techniques, and giving an outlook on the potential role of artificial intelligence in OA research.

Mechanically aligned total knee arthroplasty does not yield uniform outcomes across all coronal plane alignment of the knee (CPAK) phenotypes

PURPOSE

Patient dissatisfaction rates following total knee arthroplasty (TKA) reported in the literature reach 20%. The optimal coronal alignment is still under debate. The aim of this retrospective study was to compare clinical outcomes in different coronal plane alignment of the knee (CPAK) phenotypes undergoing mechanically aligned (MA) TKA. The hypothesis was that knees with preoperative varus arithmetic hip-knee-ankle angle (aHKA) would achieve inferior clinical outcomes after surgery compared to other aHKA categories. Additionally, another objective was to assess CPAK phenotypes distribution in the study population.

METHODS

A retrospective selection was made of 180 patients who underwent MA TKA from April 2021 to December 2022, with a 1-year follow-up. Coronal knee alignment was classified according to the CPAK classification. Clinical outcome evaluations were measured using the Knee Society Score (KSS), Oxford Knee Score (OKS), Short Form Survey 12 and Forgotten Joint Score (FJS). Differences in clinical outcomes were considered statistically significant with a p value <0 .05.

RESULTS

Patients with varus aHKA achieved significantly inferior outcomes at final follow-up compared to other aHKA categories in KSS pt. 1 (79.7 ± 17.2 vs. 85.6 ± 14.7; p = 0.028), OKS (39.2 ± 9.2 vs. 42.2 ± 7.2; p = 0.019) and FJS (75.4 ± 31.0 vs. 87.4 ± 22.9; p =0 .003). The most common aHKA category was the varus category (39%). The most common CPAK phenotypes were apex distal Types I (23.9%), II (22.8%) and III (13.3%).

CONCLUSION

MA TKA does not yield uniform outcomes across all CPAK phenotypes. Varus aHKA category shows significantly inferior results at final follow-up. The most prevalent CPAK categories are varus aHKA and apex distal JLO, with phenotypes I, II and III being the most common. However, their gender distribution varies significantly.

LEVEL OF EVIDENCE

Level IV.

EOS® is reliable to evaluate spinopelvic parameters: a validation study

BACKGROUND

Sagittal and coronal standing radiographs have been the standard imaging for assessing spinal alignment. However, their disadvantages include distortion at the image edges and low interobserver reliability in some parameters. EOS® is a low-dose biplanar digital radiographic imaging system that can avoid distortion by obtaining high-definition images.

METHODS

This study aimed to evaluate spinopelvic parameters in conventional lateral C1S1 upright radiographs and EOS® images and compare them. Patients with non-deformity changes were subjected to routine clinical examinations. Plain AP and lateral X-ray radiographs were obtained along the entire spine length. Patients were also referred for full-length EOS® of the spine. Thoracic Kyphosis (TK), Lumbar Lordosis (LL), Pelvic Tilt (PT), Sacral Slope (SS), Pelvic Incidence (PI), and Sagittal Vertical Axis (SVA) were measured in the two studies by an orthopedic surgeon and a radiologist using PACS software. Also, the orthopedic surgeon evaluated the studies again after two weeks. Intra- and inter-observer reliability was then assessed using the interclass correlation coefficient (ICC). Also, the coefficient of variation was used to assess intra- and inter-observer reliability. Bland-Altman plots were drawn for each parameter.

RESULTS

The mean age was 48.2 ± 6.6 years. Among the 50 patients, 30 (60%) were female. The mean ICC for TK, LL, PT, SS, PI, and SVA in EOS® images are 0.95, 0.95, 0.92, 0.90, 0.94, and 0.98, respectively, and in C1S1 radiography images, it was 0.92, 0.87, 0.94, 0.88, 0.93, and 0.98, respectively which shows good to excellent results. The coefficient of variation for intraobserver reliability was relatively low (< 18.6%), while it showed higher percentages in evaluating interobserver reliability (< 54.5%). Also, the Bland-Altman plot showed good agreement for each parameter.

CONCLUSION

Spinopelvic parameters, e.g., TK, LL, SS, PI, and SS, in EOS® are reliable and comparable to those in conventional lateral upright C1S1 radiographs.

Faster Rate of Correction with Distal Femoral Transphyseal Screws Versus Plates in Hemiepiphysiodesis for Coronal-Plane Knee Deformity: Age- and Sex-Matched Cohorts of Skeletally Immature Patients

BACKGROUND

Hemiepiphysiodesis (guided-growth) procedures have become the primary method of treatment for coronal-plane knee deformities in skeletally immature patients. Two leading techniques involve the use of a transphyseal screw or a growth modulation plate. However, clinical references for the estimation of correction are lacking, and no consensus has been reached regarding the superiority of one technique over the other. Therefore, the purpose of this study was to compare the rates of correction for distal femoral transphyseal screws and growth modulation plates in age- and sex-matched cohorts with coronal deformities.

METHODS

Thirty-one knees were included in each cohort on the basis of propensity scoring by chronological age and sex, and radiographic images were retrospectively reviewed preoperatively and postoperatively. Each case was measured for limb length, mechanical axis deviation (MAD), mechanical lateral distal femoral angle (LDFA), and bone age.

RESULTS

Both the MAD and LDFA rate of correction significantly differed between the screw and plate cohorts. The MAD rate of correction was observed to be 0.42 ± 0.37 mm/week (1.69 mm/month) in the plate cohort and 0.66 ± 0.51 mm/week (2.64 mm/month) in the screw cohort. The LDFA rate of correction was observed to be 0.12° ± 0.13°/week (0.50°/month) in the plate cohort and 0.19° ± 0.19°/week (0.77°/month) in the screw cohort.

CONCLUSIONS

The current study provides simple clinical references for the rate of correction of MAD and the LDFA for 2 methods of hemiepiphysiodesis. The results suggest that transphyseal screws may correct coronal knee deformities during the initial treatment stage more quickly than growth modulation plates in distal femoral guided growth.

LEVEL OF EVIDENCE

Therapeutic Level III . See Instructions for Authors for a complete description of levels of evidence.

Osteoarthritis year in review 2022: imaging

PURPOSE

This narrative review summarizes original research focusing on imaging in osteoarthritis (OA) published between April 1st 2021 and March 31st 2022. We only considered English publications that were in vivo human studies.

METHODS

The PubMed, Medline, Embase, Scopus, and ISI Web of Science databases were searched for "Osteoarthritis/OA" studies based on the search terms: "Radiography", "Ultrasound/US", "Computed Tomography/CT", "DXA", "Magnetic Resonance Imaging/MRI", "Artificial Intelligence/AI", and "Deep Learning". This review highlights the anatomical focus of research on the structures within the tibiofemoral, patellofemoral, hip, and hand joints. There is also a noted focus on artificial intelligence applications in OA imaging.

RESULTS

Over the last decade, the increasing trend of using open-access large databases has reached a plateau (from 17 to 37). Compositional MRI has had the most prominent use in OA imaging and its biomarkers have been used in the detection of preclinical OA and prediction of OA outcomes. Most noteworthy, there has been an accelerated rate of publications on the implications of artificial intelligence, used in developing prediction models and performing trabecular texture analysis, in OA imaging (from 17 to 154).

CONCLUSIONS

While imaging has maintained its key role in OA research, publication trends have shown an emphasis on the integration of AI. During the past year, MRI has maintained the highest prevalence in usage while US and CT remain as readily available modalities. Finally, there has been a notable uptake in the development and validation of AI techniques used to perform texture analysis and predict OA progression.

Osteoarthritis year in review 2021: imaging

PURPOSE

To provide a narrative review of original articles on imaging of osteoarthritis (OA) published between January 1, 2020 and March 31, 2021, with a special focus on imaging of inflammation, imaging of bone, cartilage and bone-cartilage interactions, imaging of peri-articular tissues, imaging scoring methods for OA, and artificial intelligence (AI) applied to OA imaging.

METHODS

The Embase, Pubmed, Medline, Cochrane databases were searched for original research articles in the English language on human, in vivo, imaging of OA published between January 1, 2020 and March 31, 2021. Search terms related to osteoarthritis combined with all imaging modalities and artificial intelligence were applied. A selection of articles reporting on one of the focus topics was discussed further.

RESULTS

The search resulted in 651 articles, of which 214 were deemed relevant to human OA imaging. Among the articles included, the knee joint (69%) and magnetic resonance imaging (MRI) (52%) were the predominant anatomical area and imaging modality studied. There were also a substantial number of papers (n = 46) reporting on AI applications in the field of OA imaging.

CONCLUSION

Imaging continues to play an important role in the assessment of OA. Recent advances in OA imaging include quantitative, non-contrast, and hybrid imaging techniques for improved characterization of multiple tissue processes in OA. In addition, an increasing effort in AI techniques is undertaken to enhance OA imaging acquisition and analysis.