Complex Morphologic Analysis of Cerebral Aneurysms Through the Novel Use of Fractal Dimension as a Predictor of Rupture Status: A Proof of Concept Study.

BACKGROUND: Aneurysm morphology has been correlated with rupture. Previous reports identified several morphologic indices that predict rupture status, but they measure only specific qualities of the morphology of an aneurysm in a semiquantitative fashion. Fractal analysis is a geometric technique whereby the overall complexity of a shape is quantified through the calculation of a fractal dimension (FD). By progressively altering the scale of measurement of a shape and determining the number of segments required to incorporate the entire shape, a noninteger value for the dimension of the shape is derived. We present a proof-of-concept study to calculate the FD of an aneurysm for a small cohort of patients with aneurysms in 2 specific locations to determine whether FD is associated with aneurysm rupture status. METHODS: Twenty-nine aneurysms of the posterior communicating and middle cerebral arteries were segmented from computed tomography angiograms in 29 patients. FD was calculated using a standard box-counting algorithm extended for use with three-dimensional shapes. Nonsphericity index and undulation index (UI) were used to validate the data against previously reported parameters associated with rupture status. RESULTS: Nineteen ruptured and 10 unruptured aneurysms were analyzed. Through logistic regression analysis, lower FD was found to be significantly associated with rupture status (P = 0.035; odds ratio, 0.64; 95% confidence interval, 0.42-0.97 per FD increment of 0.05). CONCLUSIONS: In this proof-of-concept study, we present a novel approach to quantify the geometric complexity of intracranial aneurysms through FD. These data suggest an association between FD and patient-specific aneurysm rupture status.

Defining Normal Ranges of Skeletal Muscle Area and Skeletal Muscle Index in Children on CT Using an Automated Deep Learning Pipeline: Implications for Sarcopenia Diagnosis.

BACKGROUND. Skeletal muscle area (SMA), representing skeletal muscle cross-sectional area at the L3 vertebral level, and skeletal muscle index (SMI), representing height-normalized SMA, can serve as markers of sarcopenia. Normal SMA and SMI values have been reported primarily in adults. OBJECTIVE. The purpose of this study was to use an automated deep learning (DL) pipeline for muscle segmentation on abdominal CT to define normative age- and sex-based values for pediatric muscle cross-sectional area as a guide for diagnosis of sarcopenia in children. METHODS. This retrospective study reviewed records of patients who underwent abdominal CT at Cincinnati Children's Hospital Medical Center from January 1, 2009, to January 3, 2019. Patients were excluded on the basis of age outside of the eligible range (2.00-18.99 years), body mass index (BMI) outside of 5-95% age-based percentiles using CDC and WHO growth charts, known medical condition, medication use, support devices, surgery, or missing axial images at the L3 level. A previously validated automated DL pipeline was used to identify an axial slice at L3 and segment skeletal muscle to generate SMA and SMI. Pearson correlation coefficients were computed. Quantile regression analysis was used to plot SMA and SMI as functions of age and sex and to determine age- and sex-based percentile values. RESULTS. Of 8817 patients who underwent abdominal CT during the study period, 2168 (mean age, 12.3 ± 4.3 [SD] years; 1125 female patients, 1043 male patients) met inclusion criteria. Mean BMI-for-age percentile based on CDC and WHO growth charts was 64.8% ± 25.3% for female patients and 61.4% ± 25.8% for male patients. SMA showed strong correlation with weight, height, age, and BMI for male (0.79-0.94) and female (0.75-0.90) patients; SMI showed weak-to-moderate correlation with weight, height, age, and BMI for male (0.25-0.57) and female (0-0.43) patients. Normal SMA and SMI ranges for age and sex were expressed as curves and as a lookup table, identifying 54 male and 59 female patients with muscle measurements below the 5th percentile regression curve. CONCLUSION. By using an automated DL pipeline in a large sample of carefully selected children, normal ranges for SMA and SMI were calculated as functions of age and sex. CLINICAL IMPACT. The normative values should aid the diagnosis of sarcopenia in children.