Characterization of Demographical Histologic Diversity in Small Renal Masses With the Clear Cell Likelihood Score.

OBJECTIVE: This study aimed to develop a diagnostic model to estimate the distribution of small renal mass (SRM; ≤4 cm) histologic subtypes for patients with different demographic backgrounds and clear cell likelihood score (ccLS) designations. MATERIALS AND METHODS: A bi-institution retrospective cohort study was conducted where 347 patients (366 SRMs) underwent magnetic resonance imaging and received a ccLS before pathologic confirmation between June 2016 and November 2021. Age, sex, race, ethnicity, socioeconomic status, body mass index (BMI), and the ccLS were tabulated. The socioeconomic status for each patient was determined using the Area Deprivation Index associated with their residential address. The magnetic resonance imaging-derived ccLS assists in the characterization of SRMs by providing a likelihood of clear cell renal cell carcinoma (ccRCC). Pathological subtypes were grouped into four categories (ccRCC, papillary renal cell carcinoma, other renal cell carcinomas, or benign). Generalized estimating equations were used to estimate probabilities of the pathological subtypes across different patient subgroups. RESULTS: Race and ethnicity, BMI, and ccLS were significant predictors of histology (all P < 0.001). Obese (BMI, ≥30 kg/m 2 ) Hispanic patients with ccLS of ≥4 had the highest estimated rate of ccRCC (97.1%), and normal-weight (BMI, <25 kg/m 2 ) non-Hispanic Black patients with ccLS ≤2 had the lowest (0.2%). The highest estimated rates of papillary renal cell carcinoma were found in overweight (BMI, 25-30 kg/m 2 ) non-Hispanic Black patients with ccLS ≤2 (92.3%), and the lowest, in obese Hispanic patients with ccLS ≥4 (<0.1%). CONCLUSIONS: Patient race, ethnicity, BMI, and ccLS offer synergistic information to estimate the probabilities of SRM histologic subtypes.

Deep learning generated lower extremity radiographic measurements are adequate for quick assessment of knee angular alignment and leg length determination.

PURPOSE: Angular and longitudinal deformities of leg alignment create excessive stresses across joints, leading to pain and impaired function. Multiple measurements are used to assess these deformities on anteroposterior (AP) full-length radiographs. An artificial intelligence (AI) software automatically locates anatomical landmarks on AP full-length radiographs and performs 13 measurements to assess knee angular alignment and leg length. The primary aim of this study was to evaluate the agreements in LLD and knee alignment measurements between an AI software and two board-certified radiologists in patients without metal implants. The secondary aim was to assess time savings achieved by AI. METHODS: The measurements assessed in the study were hip-knee-angle (HKA), anatomical-tibiofemoral angle (aTFA), anatomical-mechanical-axis angle (AMA), joint-line-convergence angle (JLCA), mechanical-lateral-proximal-femur-angle (mLPFA), mechanical-lateral-distal-femur-angle (mLDFA), mechanical-medial-proximal-tibia-angle (mMPTA), mechanical-lateral-distal-tibia- angle (mLDTA), femur length, tibia length, full leg length, leg length discrepancy (LLD), and mechanical axis deviation (MAD). These measurements were performed by two radiologists and the AI software on 164 legs. Intraclass-correlation-coefficients (ICC) and Bland-Altman analyses were used to assess the AI's performance. RESULTS: The AI software set incorrect landmarks for 11/164 legs. Excluding these cases, ICCs between the software and radiologists were excellent for 12/13 variables (11/13 with outliers included), and the AI software met performance targets for 11/13 variables (9/13 with outliers included). The mean reading time for the AI algorithm and two readers, respectively, was 38.3, 435.0, and 625.0 s. CONCLUSION: This study demonstrated that, with few exceptions, this AI-based software reliably generated measurements for most variables in the study and provided substantial time savings.

Artificial intelligence-generated hip radiological measurements are fast and adequate for reliable assessment of hip dysplasia : an external validation study.

AIMS: Hip dysplasia (HD) leads to premature osteoarthritis. Timely detection and correction of HD has been shown to improve pain, functional status, and hip longevity. Several time-consuming radiological measurements are currently used to confirm HD. An artificial intelligence (AI) software named HIPPO automatically locates anatomical landmarks on anteroposterior pelvis radiographs and performs the needed measurements. The primary aim of this study was to assess the reliability of this tool as compared to multi-reader evaluation in clinically proven cases of adult HD. The secondary aims were to assess the time savings achieved and evaluate inter-reader assessment. METHODS: A consecutive preoperative sample of 130 HD patients (256 hips) was used. This cohort included 82.3% females (n = 107) and 17.7% males (n = 23) with median patient age of 28.6 years (interquartile range (IQR) 22.5 to 37.2). Three trained readers' measurements were compared to AI outputs of lateral centre-edge angle (LCEA), caput-collum-diaphyseal (CCD) angle, pelvic obliquity, Tönnis angle, Sharp's angle, and femoral head coverage. Intraclass correlation coefficients (ICC) and Bland-Altman analyses were obtained. RESULTS: Among 256 hips with AI outputs, all six hip AI measurements were successfully obtained. The AI-reader correlations were generally good (ICC 0.60 to 0.74) to excellent (ICC > 0.75). There was lower agreement for CCD angle measurement. Most widely used measurements for HD diagnosis (LCEA and Tönnis angle) demonstrated good to excellent inter-method reliability (ICC 0.71 to 0.86 and 0.82 to 0.90, respectively). The median reading time for the three readers and AI was 212 (IQR 197 to 230), 131 (IQR 126 to 147), 734 (IQR 690 to 786), and 41 (IQR 38 to 44) seconds, respectively. CONCLUSION: This study showed that AI-based software demonstrated reliable radiological assessment of patients with HD with significant interpretation-related time savings.Cite this article: Bone Jt Open 2022;3(11):877-884.

The early impact of medicaid expansion on urologic malignancies in the United States.

PURPOSE: To assess the effects of variable adoption of Medicaid Expansion (ME) of the Affordable Care Act among different states on urologic malignancies using a new variable that defines ME status of patient's residence in a nationwide cancer registry. BASIC PROCEDURES: The National Cancer Database was queried for urologic malignancies (bladder, prostate, kidney and testis) from 2011 to 2016, spanning the period surrounding the primary ME which took place in 2014. Trends in insurance status at time of diagnosis and effects on stage at presentation and survival after ME were evaluated using a difference-in-differences estimator and stratified Cox proportional hazards regression model. MAIN FINDINGS: The percentage of patients with Medicaid coverage at the time of diagnosis increased significantly after adoption of ME in ME states across all urologic malignancies. Concurrently, there was a significant decrease in percentage of uninsured patients diagnosed with testis cancer, but not other urologic malignancies, in ME states. A change in the stage at presentation was not observed across all urologic malignancies for patients in ME states after adoption of ME. No difference in overall survival was noted among patients living in a ME state compared to non-ME states with adoption of ME in 2014. PRINCIPAL CONCLUSIONS: Despite increases in the proportion of patients with Medicaid coverage after 2014 in states that enrolled in ME, there was not an associated change in stage at presentation or survival for patients with genitourinary malignancy.

The association of lumbosacral transitional vertebral anomalies with acetabular dysplasia in adult patients with hip-spine syndrome : a cross-sectional evaluation of a prospective hip registry cohort.

AIMS: Some patients presenting with hip pain and instability and underlying acetabular dysplasia (AD) do not experience resolution of symptoms after surgical management. Hip-spine syndrome is a possible underlying cause. We hypothesized that there is a higher frequency of radiological spine anomalies in patients with AD. We also assessed the relationship between radiological severity of AD and frequency of spine anomalies. METHODS: In a retrospective analysis of registry data, 122 hips in 122 patients who presented with hip pain and and a final diagnosis of AD were studied. Two observers analyzed hip and spine variables using standard radiographs to assess AD. The frequency of lumbosacral transitional vertebra (LSTV), along with associated Castellvi grade, pars interarticularis defect, and spinal morphological measurements were recorded and correlated with radiological severity of AD. RESULTS: Out of 122 patients, 110 (90.2%) were female and 12 (9.8%) were male. We analyzed the radiographs of 122 hips (59 (48.4%) symptomatic left hips, and 63 (51.6%) symptomatic right hips). Average age at time of presentation was 34.2 years (SD 11.2). Frequency of LSTV was high (39% to 43%), compared to historic records from the general population, with Castellvi type 3b being the most common (60% to 63%). Patients with AD have increased L4 and L5 interpedicular distance compared to published values. Frequency of pars interarticularis defect was 4%. Intraclass correlation coefficient for hip and spine variables assessed ranged from good (0.60 to 0.75) to excellent (0.75 to 1.00). Severity of AD did not demonstrate significant correlation with frequency of radiological spine anomalies. CONCLUSION: Patients with AD have increased frequency of spinal anomalies seen on standard hip radiographs. However, there exists no correlation between radiological severity of AD and frequency of spine anomalies. In managing AD patients, clinicians should also assess spinal anomalies that are easily found on standard hip radiographs. Cite this article: Bone Joint J 2021;103-B(8):1351-1357.

Polymer-coated cannulas for the reduction of backflow during intraparenchymal infusions.

Infusate backflow or leak-back along the cannula track can occur during intraparenchymal infusions resulting in non-specific targeting of therapeutic agents. The occurrence of backflow depends on several variables including cannula radius, infusate flow rate, and tip location. In this study, polymer coatings that swell in situ were developed and tested with in vitro hydrogel experiments for backflow reduction. Coatings were applied to the external cannula surface in a dual layer arrangement with a poly(vinyl alcohol) outer layer atop an inner poly(ethylene oxide) and alginate layer. Once these coated cannulas were inserted and allotted an 8-10 min waiting period for hydration, backflow during infusions of 4.0 µl of a macromolecular tracer (Evans Blue labeled albumin) was reduced significantly under flow rates of 0.3-0.6 µl/min, allowing for more effective distribution within targeted regions. Polymer coating thicknesses before and after hydrations were 0.035 and 0.370 mm, respectively. Also, backflow data was fit to a model to estimate the effective local compressive stress caused by the hydrated polymers. After withdrawal of the cannula from the insertion site, the hydrated polymer coatings remained within the cavity left in the hydrogel tissue phantom and formed a seal at the infusion site that prevented further backflow during needle withdrawal. Ex vivo infusions in excised porcine brain tissues also showed significant backflow reduction while also demonstrating the ability to leave a polymer seal in the tissue cavity after cannula removal. Thus, application of these polymers as needle or cannula coatings offers a potentially simple method to improve targeting for local drug delivery.