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Purpose Quantitative computed tomography (CT) measures are increasingly being developed and used to characterize lung disease. With recent advances in CT technologies, we sought to evaluate the quantitative accuracy of lung imaging at low and ultra-low radiation doses with the use of iterative reconstruction (IR), tube current modulation (TCM), and spectral shaping. Methods We investigated the effect of five independent CT protocols reconstructed with IR on quantitative airway measures and global lung measures using an in-vivo large animal model as a human subject surrogate. A control protocol was chosen (NIH-SPIROMICS + TCM) and five independent protocols investigating TCM, low and ultra-low radiation dose, and spectral shaping. For all scans quantitative global parenchymal measurements (mean, median and standard deviation of the parenchymal HU, along with measures of emphysema) and global airway measurements (number of segmented airways and pi10) were generated. In addition, selected individual airway measurements (minor and major inner diameter, wall thickness, inner and outer area, inner and outer perimeter, wall area fraction, and inner equivalent circle diameter) were evaluated. Comparisons were made between control and target protocols using difference and repeatability measures. Results Estimated CT volume dose index (CTDIvol) across all protocols ranged from 7.32 mGy to 0.32 mGy. Low and ultra-low dose protocols required more manual editing and resolved fewer airway branches; yet, comparable pi10 whole lung measures were observed across all protocols. Similar trends in acquired parenchymal and airway measurements were observed across all protocols, with increased measurement differences using the ultra-low dose protocols. However, for small airways (1.9 ± 0.2mm) and medium airways (5.7 ± 0.4mm) the measurement differences across all protocols were comparable to the control protocol repeatability across breath-holds. Diameters, wall thickness, wall area fraction, and equivalent diameter had smaller measurement differences, than area and perimeter measurements. Conclusions In conclusion, the use of IR with low and ultra-low dose CT protocols with CT volume dose indices down to 0.32 mGy maintains selected quantitative parenchymal and airway measurements relevant to pulmonary disease characterization.

Purpose: Computed tomography (CT) is an effective method for detecting and characterizing lung nodules in vivo. With the growing use of chest CT, the detection frequency of lung nodules is increasing. Noninvasive methods to distinguish malignant from benign nodules have the potential to decrease the clinical burden, risk, and cost involved in follow-up procedures on the large number of false-positive lesions detected. This study examined the benefit of including perinodular parenchymal features in machine learning (ML) tools for pulmonary nodule assessment. Methods: Lung nodule cases with pathology confirmed diagnosis (74 malignant, 289 benign) were used to extract quantitative imaging characteristics from computed tomography scans of the nodule and perinodular parenchyma tissue. A ML tool development pipeline was employed using k-medoids clustering and information theory to determine efficient predictor sets for different amounts of parenchyma inclusion and build an artificial neural network classifier. The resulting ML tool was validated using an independent cohort (50 malignant, 50 benign). Results: The inclusion of parenchymal imaging features improved the performance of the ML tool over exclusively nodular features (P < 0.01). The best performing ML tool included features derived from nodule diameter-based surrounding parenchyma tissue quartile bands. We demonstrate similar high-performance values on the independent validation cohort (AUC-ROC = 0.965). A comparison using the independent validation cohort with the Fleischner pulmonary nodule follow-up guidelines demonstrated a theoretical reduction in recommended follow-up imaging and procedures. Conclusions: Radiomic features extracted from the parenchyma surrounding lung nodules contain valid signals with spatial relevance for the task of lung cancer risk classification. Through standardization of feature extraction regions from the parenchyma, ML tool validation performance of 100% sensitivity and 96% specificity was achieved.

Our data suggests imaging features can be used to distinguish malignancy in NF1-realted tumors, which could improve MPNST risk assessment and positively impact clinical management of NF1 patients.

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Pregnancy-related pain in the sacroiliac joint (SIJ), lumbosacral region, pubic symphysis, or in any combination of these joints has been coined as pelvic girdle pain (PGP) and has been estimated to affect almost half of all pregnant women. SIJ dysfunction in pregnancy is due to multiple biomechanical mechanisms, such as increased weight, change in posture, increased abdominal and intrauterine pressure, and laxity of the spine and pelvic structures. Moreover, when compared to men, women have increased SIJ mobility due to increased pubic angle and decreased SIJ curvature. These differences may assist in parturition where hormones, such as relaxin and estrogen, cause symphysiolysis. A retrospective review of the literature was conducted in the PubMed database using the search term "pregnancy-related sacroiliac joint pain." All peer-reviewed studies were included. Around 8%-10% of women with PGP continue to have pain for one to two years postpartum. Patients that were treated with SIJ fusion show statistically significant improvement in pain scores when compared to patients that had non-operative treatment. Although we have a number of studies following patients after sacroiliac (SI) joint fusion for pelvic pain with SI joint dysfunction, further research is needed to study sacroiliac fusion for SI joint dysfunction in postpartum women to better tailor and optimize surgical outcomes for this patient population.

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Background: Stroke risk has been attributed to many pathological and behavioral conditions. Various modifiable and non-modifiable risk factors have been recognized and found consistent throughout epidemiological studies. Herein, we investigate the effect of comorbidities seen with patient’s suffering from ischemic stroke and its effect on in-hospital mortality. Methods: We identified patients >18 year old in the National Inpatient Sample database with diseases of interest utilizing the tenth International Classification of Disease 10 diagnostic codes from the years 2016 to 2018. Interval data were analyzed using one-way ANOVA. Post hoc analysis was performed using Bonferroni correction methods. To determine independent predictors of in-hospital mortality, odds ratios were calculated using binary logistic regression for each comorbidity. Descriptive and numerical statistics, imputation, and logistic regression were calculated using SPSS software version 25. Results: Patients hospitalized with ischemic stroke were found to have the following comorbidities: atrial fibrillation (7.5%), carotid artery stenosis (1.1%), diabetes mellitus type 2 (11.4%), congestive heart failure (CHF) (7.5%), essential hypertension (21.2%), and ischemic heart disease (IHD) (2.3%). In-hospital mortality rates were higher in patients hospitalized with ischemic stroke and concomitant IHD (28.2%, P < 0.001). Hospital length of stay was longest in patients with concomitant CHF (5.96 days, P < 0.001). Similarly, patients with CHF accrued the greatest in-hospital costs (69,174 USD, P < 0.001). Conclusion: Patients hospitalized from ischemic stroke suffered from the coexistence of other comorbidities. Of the comorbidities studied, IHD was identified as having the most significant impact on in-hospital mortality.