Morphological changes after open lumbar microdiscectomy at 2-year follow-up.

BACKGROUND: It is known that a possible decrease in disc height (DH) and foraminal size after open lumbar microdiscectomy (OLM) may cause pain in the long term. However, there is still insufficient information about the short- or long-term pathoanatomical and morphological effects of microdiscectomy. For example, the exact temporal course of the change in DH is not well known. OBJECTIVE: The purpose of this study was to examine morphological changes in DH and foramen dimensions after OLM. METHODS: In patients who underwent OLM for single-level lumbar disc herniation, MRI scans were obtained before surgery, and at an average of two years after surgery. In addition to DH measurements, foraminal area (FA), foraminal height (FH), superior foraminal width (SFW), and inferior foraminal width (IFW), were measured bilaterally. RESULTS: A postoperative increase in DH was observed at all vertebral levels, with an average of 5.5%. The mean right FHs were 15.3 mm and 15.7 mm before and after surgery, respectively (p= 0.062), while the left FHs were 14.8 mm and 15.8 mm before and after surgery (p= 0.271). The mean right SFW was 5.4 mm before surgery and 5.7 mm after surgery, while the mean right IFW ranged from 3.6 mm to 3.9 mm. The mean left SFW was 4.8 mm before surgery and 5.2 mm after surgery, while the mean left IFW ranged from 3.5 mm to 3.9 mm. Before surgery, the FAs were, on average, 77.1 mm2 and 75.6 mm2 on the right and left sides, respectively. At the 2-year follow-up, the mean FAs were 84.0 mm2 and 80.2 mm2 on the right and left sides, respectively. CONCLUSIONS: Contrary to prevalent belief, in patients who underwent single-level unilateral OLM, we observed that there may be an increase rather than a decrease in DH or foramen size at the 2-year follow-up. Our findings need to be confirmed by studies with larger sample sizes and longer follow-ups.

Is Metabolic Score for Visceral Fat (METS-VF) a Better Index Than Other Adiposity Indices for the Prediction of Visceral Adiposity.

Background: Visceral adiposity is an important risk factor for cardiometabolic diseases. Objective: To determine whether the Metabolic Score for Visceral Fat (METS-VF) is more effective than other adiposity indices in predicting visceral fat area (VFA). Methods: In this single-center and cross-sectional study, we included patients aged 20-50 years, without diabetes and coronary artery disease, who underwent computed tomography (CT) including the third lumbar vertebra. Age, blood pressure, waist circumference (WC), hip circumference, fasting lipids, and glucose were assessed. VFA was measured by cross-sectional examination of CT. The correlation of WC, body mass index (BMI), waist-hip ratio (WHR), lipid accumulation product (LAP), visceral adiposity index (VAI), a body shape index (ABSI), body roundness index (BRI), and METS-VF with VFA was analyzed by correlation analysis. The cut-off values and area under the curve (AUC) for identifying increased VFA (>130 cm2) were determined. Results: We included 185 individuals with mean age 38.2 ± 8 and female predominance (58.4%). There was a significant positive correlation between all indices and VFA (p<0.001). ROC analysis revealed that METS-VF and WC demonstrated the highest predictive value for identifying increased VFA. In both men (p=0.001) and women (p<0.001), METS-VF (AUC 0.922 and 0.939, respectively) showed a significant superiority over ABSI (AUC 0.702 and 0.658, respectively), and VAI (AUC 0.731 and 0.725, respectively). Additionally, in women, its superiority over WHR (AUC 0.807) was also statistically significant (p=0.003). We identified a METS-VF cut-off point >6.4 in males >6.5 in females and WC cut-off point >88 cm in males (AUC 0.922), >90.5 cm in females (AUC 0.938). Conclusion: METS-VF is strongly associated with visceral adiposity and better to predict increased VFA. However, its superiority over WC, BMI, BRI, and LAP was not significant. The results emphasize that WC is more appealing as screening indicator for visceral adiposity considering its easy use. Clinical Trial Registry Name: Clinicaltrials.gov (http://www.clinicaltrials.gov). Clinical Trial Registry Url: https://clinicaltrials.gov/ct2/show/NCT05648409. Clinical Trial Registry Number: NCT05648409.

Pectoralis muscle area measured at T4 level is closely associated with adverse COVID-19 outcomes in hospitalized patients.

OBJECTIVES: Skeletal muscle area (SMA) at T4 level on chest computed tomography (CT) is a newly available method that can be used as a surrogate sarcopenia marker. The objective of this study is to evaluate association of SMA with adverse COVID-19 outcomes in hospitalized patients. METHODS: Hospitalized COVID-19 patients were prospectively recorded in a database containing age, gender, date of admission, date of outcome (discharge, mortality, presence of intensive care unit (ICU) stay, additional coding information (comorbidities, superimposed conditions). Admission CT-scans were retrospectively evaluated for segmentation (bilateral pectoralis major/minor, erector spinae, levator scapulae, rhomboideus minor and major and transversospinalis muscles) and SMA calculation using 3-D slicer software. RESULTS: 167 cases were evaluated (68 male, 72 female, 140 survived, 27 dead). Muscle area was lower in patients with ICU stay (p=0.023, p=0.018, p=0.008) and mortality outcome (p=0.004, p=0.007, p=0.002) for pectoralis, back and SMA. In multivariate Cox-regression analysis, hazard ratio (HR) value for the pectoralis muscle area value below 2800 mm2 was found to be 3.138(95% CI: 1.171-8.413) for mortality and 2.361(95% CI: 1.012-5.505) for ICU. CONCLUSIONS: Pectoralis muscle area measured at T4 level with 3-D slicer was closely associated with adverse outcomes (mortality, ICU stay) in hospitalized COVID-19 patients. Since early treatment methods for COVID-19 are being evaluated, this method may be a useful adjunct to clinical decision making in regard to prioritization.

Unilateral accessory scapular ossicle with sprengel deformity in a trauma patient: A case report.

Herein we present a rare anatomic variation of unilateral accessory scapular ossicle in a trauma patient and its rare association with a common scapular anomaly, Sprengel deformity. Foci that appear near the inferior angle of the scapula due to failure of bony fusion during bone maturation are called accessory scapular ossicles. Sprengel deformity is defined as the congenitally high position of the scapula. The recognition of the normal variants of scapula is important, since they could be confused with other pathologies, such as fracture and pulmonary nodule in a trauma patient. Therefore, radiologists should be familiar to these entities even rarely seen.

Comparison of Biparametric and Multiparametric Prostate Magnetic Resonance Imaging in Predicting Oncologic Outcomes After Radical Prostatectomy.

Objective: This study aimed to evaluate the difference in predicting the pathological stage of retropubic radical prostatectomy (RRP) and biochemical recurrence (BCR) in patients with Prostate Imaging Reporting and Data System (PIRADS) scores of 3 and 4 on biparametric prostate magnetic resonance imaging (bpMRI) compared to patients who upgraded from PIRADS 3 to PIRADS 4 based on the contrast-enhanced PIRADS version 2.1. Methods: This study evaluated 107 patients who underwent RRP and had preoperative multiparametric prostate magnetic resonance imaging (mpMRI) and were followed regularly. Group 1 included 31 patients evaluated as PIRADS 3 in both bpMRI and mpMRI, group 2 included 31 patients evaluated as PIRADS 3 in bpMRI and PIRADS 4 in mpMRI, and group 3 included 45 patients evaluated as PIRADS 4 without contrast. Comparisons were made between groups 1 and 2 and between groups 2 and 3. Results: No significant difference was found between the groups in terms of demographic data, preoperative or postoperative radiology, and pathology findings. Extraprostatic extension positivity and BCR were more common in group 2 compared to group 1 although not significant. Multivariate regression analysis was performed to determine the risk factors in predicting BCR, which revealed the positivity of seminal vesicle invasion and high pathological stage in the pathology report as significant factors. Prostate-specific antigen (PSA) and PSA density were higher in group 3 than in group 2, but without significance. Conclusions: This study revealed that mpMRI did not contribute in predicting BCR after RRP compared to bpMRI.

Radiomics Features Based on MRI-ADC Maps of Patients with Breast Cancer: Relationship with Lesion Size, Features Stability, and Model Accuracy.

Objective: To predict breast cancer molecular subtypes with neural networks based on magnetic resonance imaging apparent diffusion coefficient (ADC) radiomics and to detect the relation of lesion size with the stability of radiomics features. Methods: This retrospective study included 221 consecutive patients (224 lesions) with breast cancer imaged between January 2015 and January 2020. Three sample size configurations were identified based on tumor size (experiment 1: all cases, experiment 2: >1 cm3, and experiment 3: >2 cm3). The tumors were segmented by three observers based on diffusion-weighted imaging-registered ADC maps, and the volumetric agreement of these segmentations was evaluated using the Dice coefficient. Stability of radiomics features (n=851) was evaluated with intraclass correlation coefficient (ICC, >0.75) and coefficient of variation (CoV, <0.15). Feature selection was made with variance inflation factor (VIF, <10) and least absolute shrinkage and selection operator regression. Outcomes were identified as molecular subtypes (Luminal A, Luminal B, HER2-enriched, triple-negative). Neural network performance was presented as an area under the curve and accuracies. Results: Of the 851 radiomics features, 611 had ICC >0.75, and 37 remained stable in the first experiment, 49 in the second, and 59 in the third based on CoV and VIF analysis. High accuracy was demonstrated by the Luminal B, HER2-enriched, and triple-negative models in the first experiment (>80%), all models in the second experiment, and HER2-enriched and triple-negative models in the third experiment. Conclusions: A positive stability is indicated by an increased lesion size related to radiomics features. Neural networks may predict moleculer subtypes of breast cancers over 1 cm3 with high accuracy.

Multivariable Diagnostic Prediction Model to Detect Hormone Secretion Profile From T2W MRI Radiomics with Artificial Neural Networks in Pituitary Adenomas.

Objective: This study aims to develop neural networks to detect hormone secretion profiles in the pituitary adenomas based on T2 weighted magnetic resonance imaging (MRI) radiomics. Methods: This retrospective model-development study included a cohort of patients with pituitary adenomas (n=130) from January 2015 to January 2020 in one tertiary center. The mean age was 46.49±13.69 years, and 76/130 (58.46%) were women. Three observers segmented lesions on coronal T2 weighted MRI, and an interrater agreement was evaluated using the Dice coefficient. Predictors were determined as radiomics features (n=851). Feature selection was based on intraclass correlation coefficient, coefficient variance, variance inflation factor, and LASSO regression analysis. Outcomes were identified as 7 hormone secretion profiles [non-functioning pituitary adenoma, growth hormone-secreting adenomas, prolactinomas, adrenocorticotropic hormone-secreting adenomas, pluri-hormonal secreting adenomas (PHA), follicle-stimulating hormone and luteinizing hormone-secreting adenomas, and thyroid-stimulating hormone adenomas]. A multivariable diagnostic prediction model was developed with artificial neural networks (ANN) for 7 outcomes. ANN performance was presented as an area under the receiver operating characteristic curve (AUC) and accepted as successful if the AUC was >0.85 and p-value was <0.01. Results: The performance of the ANN distinguishing prolactinomas from other adenomas was validated (AUC=0.95, p<0.001, sensitivity: 91%, and specificity: 98%). The model distinguishing PHA had the lowest AUC (AUC=0.74 and p<0.001). The AUC values for the other five ANN were >0.85 and p values were <0.001. Conclusions: This study was successful in training neural networks that could differentiate the hormone secretion profile of pituitary adenomas.

Predictive performance of CT for adverse outcomes among COVID-19 suspected patients: a two-center retrospective study.

The aim of the study was to compare the performance of various computed tomography (CT) reporting tools, including zonal CT visual score (ZCVS), the number of involved lobes, and Radiological Society of North America (RSNA) categorization in predicting adverse outcomes among patients hospitalized due to the lower respiratory symptoms during the coronavirus disease 2019 (COVID-19) pandemic. A total of 405 patients admitted with severe respiratory symptoms who underwent a chest CT were enrolled. The primary adverse outcome was intensive care unit (ICU) admission of patients. Predictive performances of reporting tools were compared using the area under the receiver operating characteristic curves (AUC ROC). Among the 405 patients, 39 (9.63%) required ICU support during their hospital stay. At least two or more observers reported a typical and indeterminate COVID-19 pneumonia CT pattern according to RSNA categorization in 70% (285/405) of patients. Among these, 63% (179/285) had a positive polymerase chain reaction (PCR test for the SARS-CoV-2 virus. The median number of lobes involved according to CT was higher in patients who required ICU support (median interquartile range [IQR], 5[3; 5] vs. 3[0; 5]). The median ZCVS score was higher among the patients that subsequently required ICU support (median [IQR], 4[0; 12] vs. 13[5.75; 24]). The bootstrap comparisons of AUC ROC showed significant differences between reporting tools, and the ZCVS was found to be superior (AUC ROC, 71-75%). The ZCVS score at the first admission showed a linear and significant association with adverse outcomes among patients with the lower respiratory tract symptoms during the COVID-19 pandemic.