Multivariate use of MRI biomarkers to classify histologically confirmed necrosis in symptomatic total hip arthroplasty

Diffusion-weighted MRI of total hip arthroplasty for classification of synovial reactions: A pilot study

BACKGROUND

Conventional quantitative diffusion-weighted imaging (DWI) is sensitive to changes in tissue microstructure, but its application to evaluating patients with orthopaedic hardware has generally been limited due to metallic susceptibility artifacts. The apparent diffusion coefficient (ADC) and T2-values from a multi-spectral imaging (MSI) DWI combined with 2D multi-spectral imaging with a 2D periodically rotated overlapping parallel lines with enhanced reconstruction (2D-MSI PROPELLER DWI) based sequence and a MAVRIC based T2 mapping sequence, respectively, may mitigate the artifact and provide additional quantitative information on synovial reactions in individuals with total hip arthroplasty (THA). The aim of this pilot study is to utilize a 2D-MSI PROPELLER DWI and a MAVRIC-based T2 mapping to evaluate ADC and T2-values of synovial reactions in patients with THA.

METHODS

Coronal morphologic MRIs from THA patients underwent evaluation of the synovium and were assigned a synovial classification of 'normal', or 'grouped abnormal' (consisting of sub-groups 'infection', 'polymeric', 'metallosis', 'adverse local tissue reaction' [ALTR], or 'non-specific') and type of synovial reaction present (fluid-like, solid-like, or mixed). Regions of interest (ROIs) were placed in synovial reactions for measurement of ADC and T2-values, obtained from the 2D-MSI PROPELLER DWI and T2-MAVRIC sequences, respectively. A one-way analysis of variance (ANOVA) and Kruskal-Wallis rank sum tests were used to compare the differences in ADC and T2-values across the different synovial reaction classifications. A Kruskal-Wallis test was used to compare the ROI areas for the ADC and T2-values. A principal component analysis (PCA) was performed to evaluate the possible effects of ADC values, size of the ADC ROI, T2-values, and size of the T2 ROI with respect to synovial reaction classification.

RESULTS

Differences of ADC and T2 among the individual synovial reactions were not found. A difference of ADC between 'normal' and 'grouped abnormal' synovial reactions was also not detected even as the ADC area of 'grouped abnormal' synovial reactions were significantly larger (p = 0.02). The 'grouped abnormal' synovial reactions had significantly shorter T2-values than 'normal' synovial reactions (p = 0.02), and that the T2 area of 'grouped abnormal' synovial reactions were significantly larger (p = 0.01). A larger ROI area on the T2-maps was observed in the mixed synovial reaction type as compared to the fluid-like reaction type area (p = 0.01). Heterogeneity was noted in calculated ADC and T2 maps. PCA analysis revealed obvious clustering by the 'normal' and 'grouped abnormal' classifications.

CONCLUSIONS

2D-MSI PROPELLER DWI and MAVRIC-T2 generate quantitative images of periprosthetic tissues within clinically feasible scan times. The combination of derived ADC and T2-values with area of synovial reaction may aid in differentiating normal from abnormal synovial reactions between types of synovial reactions in patients with THA.

MRI Characteristics of Adverse Local Tissue Reactions Associated With Intraoperative Tissue Damage and Poor Revision Surgery Outcomes: A Systematic Review

BACKGROUND

Metal artifact reductions sequence (MARS) MRI has been established as a cross-sectional imaging modality for diagnosis of adverse local tissue reaction (ALTR). This study aims to evaluate the current literature regarding the characteristics of MARS MRI associated with intraoperative tissue necrosis and postoperative complications in patients undergoing revision THA because of ALTR.

METHODS

We systematically searched the literature that included reporting MRI characteristics of ALTR in failed THA and their correlation with intraoperative findings and postoperative outcomes.

RESULTS

A total of 617 studies were assessed, and 15 studies met the inclusion criteria. Synovial wall thickness correlated with increasing ALVAL grade, however, weakly with tissue necrosis. Synovial composition of mixed or solid features and the presence of abductor disruption correlated with complications and tissue necrosis. Tissue edema was a poor predictor of aseptic lymphocyte-dominant vasculitis-associated lesion (ALVAL) grade or tissue necrosis.

DISCUSSION

MARS MRI synovial composition including mixed and solid features and the presence of abductor disruption provide the best prognostic characteristics associated with intraoperative soft-tissue necrosis and revision surgery outcome. These prognostic characteristics on MRI may assist surgeons with improved criteria for intervention and preoperative patient counseling.

Artificial Learning and Machine Learning Decision Guidance Applications in Total Hip and Knee Arthroplasty: A Systematic Review

Background

Artificial intelligence (AI) and machine learning (ML) modeling in hip and knee arthroplasty (total joint arthroplasty [TJA]) is becoming more commonplace. This systematic review aims to quantify the accuracy of current AI- and ML-based application for cognitive support and decision-making in TJA.

Methods

A comprehensive search of publications was conducted through the EMBASE, Medline, and PubMed databases using relevant keywords to maximize the sensitivity of the search. No limits were placed on level of evidence or timing of the study. Findings were reported according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Analysis of variance testing with post-hoc Tukey test was applied to compare the area under the curve (AUC) of the models.

Results

After application of inclusion and exclusion criteria, 49 studies were included in this review. The application of AI/ML-based models and average AUC is as follows: cost prediction-0.77, LOS and discharges-0.78, readmissions and reoperations-0.66, preoperative patient selection/planning-0.79, adverse events and other postoperative complications-0.84, postoperative pain-0.83, postoperative patient-reported outcomes measures and functional outcome-0.81. Significant variability in model AUC across the different decision support applications was found (P < .001) with the AUC for readmission and reoperation models being significantly lower than that of the other decision support categories.

Conclusions

AI/ML-based applications in TJA continue to expand and have the potential to optimize patient selection and accurately predict postoperative outcomes, complications, and associated costs. On average, the AI/ML models performed best in predicting postoperative complications, pain, and patient-reported outcomes and were less accurate in predicting hospital readmissions and reoperations.