Yoon, Joo Hee

Objective: Investigate the performance of multiparametric MRI radiomic features, alone or combined with current standard-of-care methods, for pulmonary nodule classification. Assess the impact of segmentation variability on feature reproducibility and reliability. Methods: Radiomic features were extracted from 74 pulmonary nodules of 68 patients who underwent nodule resection or biopsy after MRI exam. The MRI features were compared with histopathology and conventional quantitative imaging values (maximum standardized uptake value [SUVmax] and mean Hounsfield unit [HU]) to determine whether MRI radiomic features can differentiate types of nodules and associate with SUVmax and HU using Wilcoxon rank sum test and linear regression. Diagnostic performance of features and four machine learning (ML) models were evaluated with area under the receiver operating characteristic curve (AUC) and 95% confidence intervals (CIs). Concordance correlation coefficient (CCC) assessed the segmentation variation impact on feature reproducibility and reliability. Results: Elevn diffusion-weighted features distinguished malignant from benign nodules (adjusted p < 0.05, AUC: 0.73–0.81). No features differentiated cancer types. Sixty-seven multiparametric features associated with mean CT HU and 14 correlated with SUVmax. All significant MRI features outperformed traditional imaging parameters (SUVmax, mean HU, apparent diffusion coefficient [ADC], T1, T2, dynamic contrast-enhanced imaging values) in distinguishing malignant from benign nodules with some achieving statistical significance (p < 0.05). Adding ADC and smoking history improved feature performance. Machine learning models demonstrated strong performance in nodule classification, with extreme gradient boosting (XGBoost) having the highest discrimination (AUC = 0.83, CI=[0.727, 0.932]). We found good to excellent inter- and intrareader feature reproducibility and reliability (CCC≥0.80). Conclusion: Eleven MRI radiomic features differentiated malignant from benign lung nodules, outperforming traditional quantitative methods. MRI radiomic ML models demonstrated good nodule classification performances with XGBoost superior to three others. There was good to excellent inter- and intrareader feature reproducibility and reliability. Advances in knowledge: Our study identified MRI radiomic features that successfully differentiated malignant from benign lung nodules and demonstrated high performance of our MR radiomic feature-based ML models for nodule classification. These new findings could help further establish thoracic MRI as a non-invasive and radiation-free alternative to standard practice for pulmonary nodule assessment.

Background: Although prophylactic antibiotics are considered the standard of care, data with regard to the comparative efficacy of specific antibiotics in the prevention of periprosthetic joint infection (PJI) have remained limited. This study evaluated whether perioperative antibiotic choice affects rates of PJI development in shoulder arthroplasty. Methods: From 2000 to 2019, all primary shoulder arthroplasty types (hemiarthroplasty, anatomic total shoulder arthroplasty, reverse shoulder arthroplasty) performed for elective and trauma indications with perioperative antibiotic data and a minimum follow-up of 2 years were identified from a single institution. Demographic characteristics, PJI risk factors, and PJI-free survivorship data were retrieved. Multivariable analyses were conducted to determine the association between the antibiotic administered and the development of PJI. Results: Of 7,713 shoulder arthroplasties, cefazolin was administered in 6,879 procedures (89.2%) and non-cefazolin antibiotics consisting of vancomycin (465 procedures [6.0%]), clindamycin (345 procedures [4.5%]), and alternative regimens (24 procedures [0.31%]) were administered in 834 procedures (10.8%). PJIs occurred in 101 shoulder arthroplasties (1.3%), with Cutibacterium acnes as the most common pathogen (44 procedures [43.6%]). PJI-free survivorship was greater in shoulder arthroplasties in which cefazolin was administered compared with those in which non-cefazolin antibiotics were administered, with 0.91% greater survival free of PJI at 1 month, 1.4% at 1 year, and 2.7% at 15 years (p < 0.001). Cefazolin administration, compared with non-cefazolin administration, was associated with a 69% reduction in all-cause PJI risk and a 78% reduction in C. acnes PJI risk (p < 0.001). A higher risk of PJI for both groups was observed with vancomycin; the hazard ratio [HR] was 2.32 (95% confidence interval [CI], 1.22 to 4.40; p = 0.010) for all-cause PJI and 2.94 (95% CI, 1.12 to 7.49; p = 0.028) for C. acnes PJI. A higher risk of PJI was also observed for both groups for clindamycin; the HR was 5.07 (95% CI, 2.83 to 9.05; p < 0.001) for all-cause PJI and 8.01 (95% CI, 3.63 to 17.42; p < 0.001) for C. acnes PJI. Conclusions: In primary shoulder arthroplasty, cefazolin administration was associated with a significantly lower rate of PJI compared with non-cefazolin alternatives, including both vancomycin and clindamycin. These risk discrepancies were observed across all infectious pathogens and may be considered even greater when C. acnes was the infecting bacterium. Level of Evidence: Therapeutic Level III . See Instructions for Authors for a complete description of levels of evidence.

Case: We present a case of a 71-year-old woman with history of left-sided shoulder arthritis who underwent standard total shoulder arthroplasty using a novel hybrid glenoid component. After 3 years, she sustained traumatic dissociation of the interface between the polyethylene face and central titanium post, a unique failure mechanism of the hybrid glenoid component. This was successfully corrected through conversation to reverse shoulder arthroplasty (RSA). Follow-up after 2 years demonstrated marked improvement in pain and function. Conclusion: Hybrid glenoid components present a unique failure mechanism as compared to traditional glenoid loosening and can be effectively corrected with RSA.