PURPOSE:To quantify the degree of correlation between pediatric radiation exposure rates and commonly used body size surrogates: anteroposterior (AP) or lateral (LAT) body thickness, weight, height, body mass index (BMI), body surface area (BSA), and age, for general fluoroscopy (GF) and cardiac fluoroscopically guided interventional (cardiac FGI) examinations, as a way to identify the most predictive surrogate to optimize dose management and exposure control across a wide range of pediatric body sizes. METHODS:This retrospective study included 6447 GF examinations, from 4452 pediatric patients, and 2968 cardiac FGI examinations, from 1471 patients, aged from birth to 21 years. Reference air kerma (RAK) data, collected from a radiation dose index monitoring database, were corrected using calibration measurements with accuracy better than ± 5%. Exposure rate was calculated as RAK divided by total fluoroscopy time. Patient body size surrogates: measured AP or LAT thickness, age, weight, height, BMI, and BSA were extracted or computed from recorded data. Correlations between log-transformed exposure rates and body size surrogates were evaluated using Pearson's correlation coefficients. Steiger's Z-test was applied to assess statistically significant differences between dependent correlations. RESULTS:For GF examinations, AP thickness correlated most strongly with exposure rate (r = 0.691), followed by BSA (r = 0.574) and weight (r = 0.568). BMI showed the weakest correlation (r = 0.446). For cardiac FGI examinations, weight demonstrated the highest correlation with exposure rate in both frontal (r = 0.689) and LAT (r = 0.794) planes, with BSA and LAT thickness performing similarly. All other correlations were significantly lower (p < 0.001). The superior performance of AP thickness in GF reflects its direct relationship to X-ray attenuation, whereas variable geometry and positioning during cardiac FGI favor weight as a more stable predictor. CONCLUSIONS:For pediatric fluoroscopy, AP thickness best predicts exposure rate in GF, while weight is the most reliable surrogate for cardiac FGI procedures. Selecting modality-specific size metrics enhances pediatric dose optimization and supports consistent radiation exposure management across all patient sizes.

Increased positron emission tomography (PET) scanner z-axis coverage provides an opportunity in pediatrics to reduce dose, anesthesia, or repeat scans due to motion. Recently, our digital PET scanner was upgraded from a 25-cm to a 30-cm z-axis coverage. We compare the two systems through National Electrical Manufacturing Association (NEMA) testing and evaluation of paired images from patients scanned on both systems. NEMA testing and a retrospective review of pediatric patients who underwent clinically indicated 18F-fluorodeoxyglucose (FDG) PET computed tomography (PET/CT) on both systems with unchanged acquisition parameters were performed. Image quality was assessed with liver signal to noise ratio (SNR-liver) and contrast to noise ratio (CNR) in the thigh muscle and liver with results compared with an unpaired t-test. Three readers independently reviewed paired (25 cm and 30 cm) images from the same patient, blinded to scanner configuration. Expansion to 30 cm increased system sensitivity to 29.8

BACKGROUND Somatostatin type-2A receptors (SST2A) regulate cell growth through complex downstream modulation of proliferation and apoptosis signaling, thereby representing a potential therapeutic target. Lutetium (177Lu-DOTATATE), a radionuclide therapy which binds SST2A and delivers local radiation via beta particle emission, gained FDA approval for gastroenteropancreatic neuroendocrine tumors, a disease characterized by SST2A expression. Potential expansion of Lutathera into pediatric neuro-oncology is supported by evidence that medulloblastoma, other embryonal tumors, and meningiomas consistently express membranous SST2A, exhibit corresponding uptake on SST2A-radiolabeled nuclear imaging (DOTATATE PET), and have demonstrated radiographic response to SST2A-targeted therapy, suggesting sufficient CNS penetration to achieve therapeutic benefit. METHODS CONNECT2007 (NCT05278208) is an international, multicenter phase I/II study investigating safety and efficacy of Lutathera in children and adults with SST2A-expressing CNS tumors. Patients with recurrent/progressive high-grade CNS tumors and meningiomas can undergo screening, consisting of DOTATATE PET imaging for functional confirmation of SST2A expression, requiring adequate uptake (Krenning score ≥2) by central review. The phase I cohort will enroll patients aged 4-<12 years to determine the pediatric recommended phase II dose (RP2D) following a Rolling Six design, with three potential dose levels, starting at the adult RP2D (200mCi), scaled by body surface area. The phase II cohort will enroll patients aged ≥12 years to assess anti-tumor activity through evaluation of 6-month progression-free survival in medulloblastoma/embryonal tumors (descriptive in other histologies). Lutathera is administered intravenously, with concurrent amino acids for nephroprotection, dosed every 8 weeks for up to 4 cycles. Longitudinal correlative studies include 1) evaluating prevalence, heterogeneity, and clinical, histologic molecular, and radiographic predictors of SST2A expression, 2) characterizing radiation dosimetry of Lutathera in organs at risk of toxicity and within CNS to assess tumor penetration, and 3) identifying imaging and molecular biomarkers of response. Enrollment began in January 2023; both cohorts remain open to accrual.

BackgroundGlobal shortages of iodinated contrast media (ICM) during COVID-19 pandemic forced the imaging community to use ICM more strategically in CT exams.PurposeThe purpose of this work is to provide a quantitative framework for preserving iodine CNR while reducing ICM dosage by either lowering kV in single-energy CT (SECT) or using lower energy virtual monochromatic images (VMI) from dual-energy CT (DECT) in a phantom study.Materials and MethodsIn SECT study, phantoms with effective diameters of 9.7, 15.9, 21.1, and 28.5 cm were scanned on SECT scanners of two different manufacturers at a range of tube voltages. Statistical based iterative reconstruction and deep learning reconstruction were used. In DECT study, phantoms with effective diameters of 20, 29.5, 34.6, and 39.7 cm were scanned on DECT scanners from three different manufacturers. VMIs were created from 40 to 140 keV. ICM reduction by lowering kV levels for SECT or switching from SECT to DECT was calculated based on the linear relationship between iodine CNR and its concentration under different scanning conditions.ResultsOn SECT scanner A, while matching CNR at 120 kV, ICM reductions of 21%, 58%, and 72% were achieved at 100, 80, and 70 kV, respectively. On SECT scanner B, 27% and 80% ICM reduction was obtained at 80 and 100 kV. On the Fast-kV switch DECT, with CNR matched at 120 kV, ICM reductions were 35%, 30%, 23%, and 15% with VMIs at 40, 50, 60, and 68 keV, respectively. On the dual-source DECT, ICM reductions were 52%, 48%, 42%, 33%, and 22% with VMIs at 40, 50, 60, 70, and 80 keV. On the dual-layer DECT, ICM reductions were 74%, 62%, 45%, and 22% with VMIs at 40, 50, 60, and 70 keV.ConclusionsOur work provided a quantitative baseline for other institutions to further optimize their scanning protocols to reduce the use of ICM.

BACKGROUND. Deep learning abdominal organ segmentation algorithms have shown excellent results in adults; validation in children is sparse. OBJECTIVE. The purpose of this article is to develop and validate deep learning models for liver, spleen, and pancreas segmentation on pediatric CT examinations. METHODS. This retrospective study developed and validated deep learning models for liver, spleen, and pancreas segmentation using 1731 CT examinations (1504 training, 221 testing), derived from three internal institutional pediatric (age <= 18 years) datasets (n = 483) and three public datasets comprising pediatric and adult examinations with various pathologies (n = 1248). Three deep learning model architectures (SegResNet, DynUNet, and SwinUNETR) from the Medical Open Network for Artificial Intelligence (MONAI) framework underwent training using native training (NT), relying solely on institutional datasets, and transfer learning (TL), incorporating pretraining on public datasets. For comparison, TotalSegmentator, a publicly available segmentation model, was applied to test data without further training. Segmentation performance was evaluated using mean Dice similarity coefficient (DSC), with manual segmentations as reference. RESULTS. For internal pediatric data, the DSC for TotalSegmentator, NT models, and TL models for normal liver was 0.953, 0.964-0.965, and 0.965-0.966, respectively; for normal spleen, 0.914, 0.942-0.945, and 0.937-0.945; for normal pancreas, 0.733, 0.774-0.785, and 0.775-0.786; and for pancreas with pancreatitis, 0.703, 0.590-0.640, and 0.667-0.711. For public pediatric data, the DSC for TotalSegmentator, NT models, and TL models for liver was 0.952, 0.871-0.908, and 0.941-0.946, respectively; for spleen, 0.905, 0.771-0.827, and 0.897-0.926; and for pancreas, 0.700, 0.577-0.648, and 0.693-0.736. For public primarily adult data, the DSC for TotalSegmentator, NT models, and TL models for liver was 0.991, 0.633-0.750, and 0.926-0.952, respectively; for spleen, 0.983, 0.569-0.604, and 0.923-0.947; and for pancreas, 0.909, 0.148-0.241, and 0.699-0.775. The DynUNet TL model was selected as the best-performing NT or TL model considering DSC values across organs and test datasets and was made available as an open-source MONAI bundle (https://github.com/cchmc-dll/pediatric_abdominal_segmentation_bundle.git). CONCLUSION. TL models trained on heterogeneous public datasets and fine-tuned using institutional pediatric data outperformed internal NT models and Total-Segmentator across internal and external pediatric test data. Segmentation performance was better in liver and spleen than in pancreas. CLINICAL IMPACT. The selected model may be used for various volumetry applications in pediatric imaging.

CT reconstruction has undergone a substantial change over the last decade with the introduction of iterative reconstruction (IR) and now with deep learning reconstruction (DLR). In this review, DLR will be compared to IR and filtered back-projection (FBP) reconstructions. Comparisons will be made using image quality metrics such as noise power spectrum, contrast-dependent task-based transfer function, and non-prewhitening filter detectability index (dNPW'). Discussion on how DLR has impacted CT image quality, low-contrast detectability, and diagnostic confidence will be provided. DLR has shown the ability to improve in areas that IR is lacking, namely: noise magnitude reduction does not alter noise texture to the degree that IR did, and the noise texture found in DLR is more aligned with noise texture of an FBP reconstruction. Additionally, the dose reduction potential for DLR is shown to be greater than IR. For IR, the consensus was dose reduction should be limited to no more than 15-30% to preserve low-contrast detectability. For DLR, initial phantom and patient observer studies have shown acceptable dose reduction between 44 and 83% for both low- and high-contrast object detectability tasks. Ultimately, DLR is able to be used for CT reconstruction in place of IR, making it an easy "turnkey" upgrade for CT reconstruction. DLR for CT is actively being improved as more vendor options are being developed and current DLR options are being enhanced with second generation algorithms being released. DLR is still in its developmental early stages, but is shown to be a promising future for CT reconstruction.

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: 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% to 95% age-based percentiles using CDC and WHO growth charts), known medical condition, medication use, support devices, surgery, or missing axial images at L3 level. A previously validated automated DL pipeline (https://github.com/cchmc-dll/pediatric_sarcopenia_pipeline.git) 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, 11.5±45 years; 1125 females, 1043 males) met inclusion criteria. Mean BMI-for-age percentile based on CDC and WHO growth charts was 64.8±25.3% for females and 61.4±25.8% for males. SMA showed strong correlation with weight, height, age, and BMI for males (0.79-0.94) and females (0.75-0.90); SMI showed weak-to-moderate correlation with weight, height, age and BMI for males (0.25-0.57) and females (0.00-0.43). Normal SMA and SMI ranges for age and sex were expressed as curves and as a lookup table, identifying 54 males and 59 females with muscle measurements below the 5-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.

Purpose: To assess the diagnostic confidence of intraoral radiographic image quality while reducing the pediatric patient's radiation exposure using a longer position indicating device (PID), additional X-ray beam filtration and rectangular collimation while using modern, lower-power intraoral dental X-ray units.Methods: A randomized prospective study scored bitewing intraoral dental images based on relevant clinical features. Observer studies with pediatric dentists and dental residents were conducted to verify whether diagnostic confidence remained unchanged after dose reduction modifications. The study involved a two-phase investigation to determine: (1) the best thickness of aluminum (Al) 2024-T3 alloy filter and (2) required increased exposure time to maintain intraoral radiographic image quality. A 30 cm PID with a rectangular collimator was used to further manage patient dose. For each phase, images from 125 patients were collected from February 2017 to September 2018 and analyzed.Results: The results from the observer study using a 30 cm PID, 1.02 mm thick Al alloy filter, and a rectangular collimator resulted in a patient dose reduction between 64 percent (exposure time of 400 msec) to 77 percent (250 msec), without any statis- tically significant effect to the diagnostic confidence of the observers in evaluating the reduced radiation images.Conclusion: Long recognized dose reduction methods, when implemented on a modern, low-power intraoral dental X-ray unit, do not impact confidence in bite- wing diagnostic images, but substantially reduce patient dose and should be adopted to increase patient safety, especially for children.

The purpose of this study was to provide an empirical model to develop reference air kerma (RAK) alert levels as a function of patient thickness or age for pediatric fluoroscopy for any institution to use in a Quality Assurance program. RAK and patient thickness were collected for 10&663 general fluoroscopic examinations and 1500 fluoroscopically guided interventions (FGIs). RAK and patient age were collected for 6137 fluoroscopic examinations with mobile-C-arms (MC). Coefficients of linear regression fits of logarithmic RAK as a function of patient thickness or age were generated for each fluoroscopy group. Regression fits of RAK for 50%, 90%, and 98% upper prediction levels were used as inputs to derive an empirical formula to estimate alert levels as a function of patient thickness. A methodology is presented to scale results from this study for any patient thickness or age for any institution, for example, the patient thickness dependent RAK alert level at the top 1% of expected RAK can be set using the 98% upper prediction interval boundary given by: RAK 98 % = e m . x avg + s 98 . c ̂ ${\rm{RAK}}_{98\% } = {e}^{m.{x}_{{\rm{avg}}} + {s}_{98}.\hat{c}}\ $ , where xavg is the institute's average patient thickness or age, and c ̂ $\hat{c}$ is the intercept based on the average RAK of the patient population calculated as c ̂ = ln ( RAK avg ) - m . x avg . RA K avg $\hat{c} = \ln ( {{\rm{RAK}}_{{\rm{avg}}}} )\ - m.{x}_{{\rm{avg}}}{\rm{.RA}}{{\rm{K}}}_{{\rm{avg}}}$ is the institution's average RAK (mGy). m and s98 are constants presented for each type of fluoroscope and RAK group and represent slope of the fit and scale factor, respectively. An empirical equation, which estimates alert levels expressed as air Kerma without backscatter at the interventional reference point as a function of patient thickness or age is provided for each fluoroscopic examination type. The empirical equations allow any facility with limited data to scale the results of this study's single facility data to model their practice's unique RAK alert levels and patient population demographics to establish pediatric alert levels for fluoroscopic procedures.

Quantification of organ size has utility in clinical care and research for diagnostics, prognostics and surgical planning. Volumetry is regarded as the best measure of organ size and change in size over time. Scarce reference values exist for liver and spleen volumes in healthy children. To report liver and spleen volumes for a sample of children defined by manual segmentation of contrast-enhanced CT images with the goal of defining normal values and thresholds that might indicate disease. This retrospective study included clinically acquired contrast-enhanced CTs of the abdomen/pelvis for children and adolescents imaged between January 2018 and July 2021. Liver and spleen volumes were derived through manual segmentation of CTs reconstructed at 2.5-, 3- or 5-mm slice thickness. A subset of images (5

Artificial intelligence (AI) uses computers to mimic cognitive functions of the human brain, allowing inferences to be made from generally large datasets. Traditional machine learning (e.g., decision tree analysis, support vector machines) and deep learning (e.g., convolutional neural networks) are two commonly employed AI approaches both outside and within the field of medicine. Such techniques can be used to evaluate medical images for the purposes of automated detection and segmentation, classification tasks (including diagnosis, lesion or tissue characterization, and prediction), and image reconstruction. In this review article we highlight recent literature describing current and emerging AI methods applied to abdominal imaging (e.g., CT, MRI and US) and suggest potential future applications of AI in the pediatric population.

BACKGROUND. CT is the imaging modality of choice to identify lung metastasis. OBJECTIVE. The purpose of this study was to evaluate the performance of reduced-dose CT for the detection of lung nodules in children and young adults with cancer. METHODS. This prospective study enrolled patients 4-21 years old with known or suspected malignancy who were undergoing clinically indicated chest CT. Study participants underwent an additional investigational reduced-dose chest CT examination in the same imaging encounter. Separated deidentified CT examinations were reviewed in blinded fashion by three independent radiologists. One reviewer performed a subsequent secondary review to match nodules between the standard- and reduced-dose examinations. Diagnostic performance was computed for the reduced-dose examinations using the clinical examinations as the reference standard. lntraobserver agreement and interobserver agreement were calculated using Cohen kappa. RESULTS. A total of 78 patients (44 male patients and 34 female patients; mean age, 15.2 +/- 3.8 [SD] years) were enrolled. The mean estimated effective dose was 1.8 +/- 1.1 mSv for clinical CT and 0.3 +/- 0.1 mSv for reduced-dose CT, which is an 83% dose reduction. Forty-five of the 78 (58%) patients had 162 total lung nodules (mean size, 3.4 +/- 3.3 mm) detected on the clinical CT examinations. A total of 92% of nodules were visible on reduced-dose CT. The sensitivity and specificity of reduced-dose CT for nodules ranged from 63% to 77% and from 80% to 90%, respectively, across the three reviewers. Intraobserver agreement between clinical CT and reduced-dose CT was moderate to substantial for the presence of nodules (K = 0.45-0.67) and was good to excellent for the number of nodules (K = 0.68-0.84) and nodule size (K = 0.69-0.86). Interobserver agreement for the presence of nodules was moderate for both reduced-dose (K = 0.53) and clinical (K = 0.54) CT. A median of one nodule was present on clinical CT in patients with a falsely negative reduced-dose CT examination. CONCLUSION. Reduced-dose CT depicts more than 90% of lung nodules in children and young adults with cancer. Reviewers identified the presence of nodules with moderate sensitivity and high specificity. CLINICAL IMPACT. CT performed at a 0.3-mSv mean effective dose has acceptable diagnostic performance for lung nodule detection in children and young adults and has the potential to reduce patient dose or expand CT utilization (e.g., to replace radiography in screening or monitoring protocols).

Purpose Recently, medical professionals have reconsidered the practice of routine gonadal shielding for radiographic examinations. The objective of this study was to evaluate the gonadal dose reduction achievable with gonadal shields in the primary beam during abdominal/pelvic radiographic examinations under ideal and non‐ideal shielding placement. Methods CT scans of CIRS anthropomorphic phantoms were used to perform voxelized Monte Carlo simulations of the photon transport during abdominal/pelvic radiographic examinations with standard filtration and 0.1 mm Cu + 1 mm Al added filtration to estimate gonadal doses for an adult, 5 yr old, and newborn phantom with and without gonadal shields. The reduction in dose when the shields were not placed at the ideal locations was also evaluated. The ratio of the number of scattered‐to‐primary photons (SPR) across the anteroposterior (AP) dimension of the phantoms was also reported. Results The simulated dose reduction with ideal shielding placement for the testes and ovaries ranged from 80% to 90% and 55% to 70% respectively. For children, a misalignment of the shield to the gonad of 4 cm reduced the measured dose reduction to the gonads to <10%. For adults, this effect did not occur until the misalignment increased to ~6 cm. Effects of dose reduction with and without the gonadal shields properly placed were similar for standard filtration and added filtration. SPR at the level of the testes was consistently <1 for all phantoms. SPR for ovaries was ~1.5 for the adult and 5‐yr old, and ~1 for the newborn phantom. Conclusion Dose reduction with ideal alignment of the simulated gonadal shield to the gonads in this study was greater for the testes than the ovaries; both reductions were substantial. However, the dose reductions were greatly reduced (to <10%) for both sexes with misalignment of the gonads to the shields by 4 cm for children and 6 cm for adults.

To evaluate the effectiveness of propranolol at mitigating FDG uptake in brown adipose tissue (BAT) of pediatric patients with known or suspected malignancies. PET/CT scans of 3 cohorts of patients treated from 2005 to 2017 were scored for the presence of FDG uptake by BAT at 7 sites: right or left neck/supraclavicular area, right or left axilla, mediastinum, posterior thorax, and abdomen/pelvis. Uptake was scored as follows: 0, none; 1, mild uptake < liver; 2, moderate uptake = liver; and 3, intense uptake > liver. Group 1 consisted of 323 patients (630 scans) who had no specific preparation to mitigate FDG uptake by BAT. Group 2 consisted of 345 patients (705 scans) who underwent only warming in an uptake room with a fixed temperature at 24 °C. Group 3 consisted of 622 patients (1457 scans) who underwent warming. In group 3, patients 8 years and older, 471 patients (1114 scans), were also pre-medicated with oral propranolol 60 min before injection of FDG. Generalized estimation equation, using the logit link method, was used to model the relationship between the incidence of BAT score > 0, in any site, as a function of age, sex, seasonal effect, and body surface area (BSA). In patients aged 8 years or older, the incidence of BAT uptake was 35–44 % and declined to 15 % with propranolol. BAT was most frequent in the neck (26 %), axilla (18 %), posterior thorax (18 %), mediastinum (14 %), and abdomen/pelvis (8 %); BAT was less common in warm months (p = 0.001). No substantial benefit was shown with pre-injection warming alone. No significant effect was found for age, sex, or BSA separately. When BAT uptake was present, it was usually intense. Propranolol preparation minimizes FDG uptake by BAT and should be considered routine for pediatric FDG PET/CT cancer-related protocols in children, adolescents, and young adults.

Background CT deep learning reconstruction (DLR) algorithms have been developed to remove image noise. How the DLR affects image quality and radiation dose reduction has yet to be fully investigated. Purpose To investigate a DLR algorithm's dose reduction and image quality improvement for pediatric CT. Materials and Methods DLR was compared with filtered back projection (FBP), statistical-based iterative reconstruction (SBIR), and model-based iterative reconstruction (MBIR) in a retrospective study by using data from CT examinations of pediatric patients (February to December 2018). A comparison of object detectability for 15 objects (diameter, 0.5-10 mm) at four contrast difference levels (50, 150, 250, and 350 HU) was performed by using a non-prewhitening-matched mathematical observer model with eye filter (d'NPWE), task transfer function, and noise power spectrum analysis. Object detectability was assessed by using area under the curve analysis. Three pediatric radiologists performed an observer study to assess anatomic structures with low object-to-background signal and contrast to noise in the azygos vein, right hepatic vein, common bile duct, and superior mesenteric artery. Observers rated from 1 to 10 (worst to best) for edge definition, quantum noise level, and object conspicuity. Analysis of variance and Tukey honest significant difference post hoc tests were used to analyze differences between reconstruction algorithms. Results Images from 19 patients (mean age, 11 years ± 5 [standard deviation]; 10 female patients) were evaluated. Compared with FBP, SBIR, and MBIR, DLR demonstrated improved object detectability by 51% (16.5 of 10.9), 18% (16.5 of 13.9), and 11% (16.5 of 14.8), respectively. DLR reduced image noise without noise texture effects seen with MBIR. Radiologist ratings were 7 ± 1 (DLR), 6.2 ± 1 (MBIR), 6.2 ± 1 (SBIR), and 4.6 ± 1 (FBP); two-way analysis of variance showed a difference on the basis of reconstruction type (P < .001). Radiologists consistently preferred DLR images (intraclass correlation coefficient, 0.89; 95% CI: 0.83, 0.93). DLR demonstrated 52% (1 of 2.1) greater dose reduction than SBIR. Conclusion The DLR algorithm improved image quality and dose reduction without sacrificing noise texture and spatial resolution. © RSNA, 2020 Online supplemental material is available for this article.