The incidence of brain trauma caused by road injuries: Results from the Global Burden of Disease Study 2019.

BACKGROUND: Road collisions are a significant source of traumatic brain injury (TBI). We aimed to determine the pattern of road injury related TBI (RI-TBI) incidence, as well as its temporal trends. METHODS: We collected detailed information on RI-TBI between 1990 and 2019, derived from the Global Burden of Disease Study 2019. Estimated annual percentage changes (EAPCs) of RI-TBI age standardized incidence rate (ASIR), by sex, region, and cause of road injuries, were assessed to quantify the temporal trends of RI-TBI burden. RESULTS: Globally, incident cases of RI-TBI increased 68.1% from 6,900,000 in 1990 to 11,600,000 in 2019. The overall ASIR increased by an average of 0.43% (95% CI 0.30%-0.56%) per year during this period. The ASIR of RI-TBI due to cyclist, motorcyclist and other road injuries increased between 1990 and 2019; the corresponding EAPCs were 0.56 (95% CI 0.37-0.75), 1.60 (95% CI 1.35-1.86), and 0.75 (95% CI 0.59-0.91), respectively. In contrast, the ASIR of RI-TBI due to motor vehicle and pedestrian decreased with an EAPC of -0.12 and -0.14 respectively. The changing pattern for RI-TBI was heterogeneous across countries and regions. The most pronounced increases were observed in Mexico (EAPC = 3.74), followed by China (EAPC = 2.45) and Lesotho (EAPC = 1.91). CONCLUSIONS: RI-TBI remains a major public health concern worldwide, although road safety legislations have contributed to the decreasing incidence in some countries. We found an unfavorable trend in several countries with a relatively low socio-demographic index, suggesting that much more targeted and specific approaches should be adopted in these areas to forestall the increase in RI-TBI.

Radiomics model based on shear-wave elastography in the assessment of axillary lymph node status in early-stage breast cancer.

OBJECTIVES: To develop and validate an ultrasound elastography radiomics nomogram for preoperative evaluation of the axillary lymph node (ALN) burden in early-stage breast cancer. METHODS: Data of 303 patients from hospital #1 (training cohort) and 130 cases from hospital #2 (external validation cohort) between Jun 2016 and May 2019 were enrolled. Radiomics features were extracted from shear-wave elastography (SWE) and corresponding B-mode ultrasound (BMUS) images. The minimum redundancy maximum relevance and least absolute shrinkage and selection operator algorithms were used to select ALN status-related features. Proportional odds ordinal logistic regression was performed using the radiomics signature together with clinical data, and an ordinal nomogram was subsequently developed. We evaluated its performance using C-index and calibration. RESULTS: SWE signature, US-reported LN status, and molecular subtype were independent risk factors associated with ALN status. The nomogram based on these variables showed good discrimination in the training (overall C-index: 0.842; 95%CI, 0.773-0.879) and the validation set (overall C-index: 0.822; 95%CI, 0.765-0.838). For discriminating between disease-free axilla (N0) and any axillary metastasis (N + (≥ 1)), it achieved a C-index of 0.845 (95%CI, 0.777-0.914) for the training cohort and 0.817 (95%CI, 0.769-0.865) for the validation cohort. The tool could also discriminate between low (N + (1-2)) and heavy metastatic ALN burden (N + (≥ 3)), with a C-index of 0.827 (95%CI, 0.742-0.913) in the training cohort and 0.810 (95%CI, 0.755-0.864) in the validation cohort. CONCLUSION: The radiomics model shows favourable predictive ability for ALN staging in patients with early-stage breast cancer, which could provide incremental information for decision-making. KEY POINTS: • Radiomics analysis helps radiologists to evaluate the axillary lymph node status of breast cancer with accuracy. • This multicentre retrospective study showed that radiomics nomogram based on shear-wave elastography provides incremental information for risk stratification. • Treatment can be given with more precision based on the model.

The global, regional, and national burden of acute pancreatitis in 204 countries and territories, 1990-2019.

BACKGROUND: Acute pancreatitis is a common and potentially lethal gastrointestinal disease, but literatures for the disease burden are scarce for many countries. Understanding the current burden of acute pancreatitis and the different trends across various countries is essential for formulating effective preventive intervenes. We aimed to report the incidence, mortality, and disability-adjusted life-years (DALYs) caused by acute pancreatitis in 204 countries and territories between 1990 and 2019. METHODS: Estimates from the Global Burden of Disease Study 2019 (GBD 2019) were used to analyze the epidemiology of acute pancreatitis at the global, regional, and national levels. We also reported the correlation between development status and acute pancreatitis' age-standardized DALY rates, and calculated DALYs attributable to alcohol etiology that had evidence of causation with acute pancreatitis. All of the estimates were shown as counts and age-standardized rates per 100,000 person-years. RESULTS: There were 2,814,972.3 (95% UI 2,414,361.3-3,293,591.8) incident cases of acute pancreatitis occurred in 2019 globally; 1,273,955.2 (1,098,304.6-1,478,594.1) in women and 1,541,017.1 (1,307,264.4-1,814,454.3) in men. The global age-standardized incidence rate declined from 37.9/100,000 to 34.8/100,000 during 1990-2019, an annual decrease of 8.4% (5.9-10.4%). In 2019, there were 115,053.2 (104,304.4-128,173.4) deaths and 3,641,105.7 (3,282,952.5-4,026,948.1) DALYs due to acute pancreatitis. The global age-standardized mortality rate decreased by 17.2% (6.6-27.1%) annually from 1.7/100,000 in 1990 to 1.4/100,000 in 2019; over the same period, the age-standardized DALY rate declined by 17.6% (7.8-27.0%) annually. There were substantial differences in the incidence, mortality and DALYs across regions. Alcohol etiology attributed to a sizable fraction of acute pancreatitis-related deaths, especially in the high and high-middle SDI regions. CONCLUSION: Substantial variation existed in the burden of acute pancreatitis worldwide, and the overall burden remains high with aging population. Geographically targeted considerations are needed to tailor future intervenes to relieve the burden of acute pancreatitis in specific countries, especially for Eastern Europe.

Ultrasound-based deep learning radiomics in the assessment of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer.

PURPOSE: The aim of the study was to develop and validate a deep learning radiomic nomogram (DLRN) for preoperatively assessing breast cancer pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) based on the pre- and post-treatment ultrasound. METHODS: Patients with locally advanced breast cancer (LABC) proved by biopsy who proceeded to undergo preoperative NAC were enrolled from hospital #1 (training cohort, 356 cases) and hospital #2 (independent external validation cohort, 236 cases). Deep learning and handcrafted radiomic features reflecting the phenotypes of the pre-treatment (radiomic signature [RS] 1) and post-treatment tumour (RS2) were extracted. The minimum redundancy maximum relevance algorithm and the least absolute shrinkage and selection operator regression were used for feature selection and RS construction. A DLRN was then developed based on the RSs and independent clinicopathological risk factors. The performance of the model was assessed with regard to calibration, discrimination and clinical usefulness. RESULTS: The DLRN predicted the pCR status with accuracy, yielded an area under the receiver operator characteristic curve of 0.94 (95% confidence interval, 0.91-0.97) in the validation cohort, with good calibration. The DLRN outperformed the clinical model and single RS within both cohorts (P < 0.05, as per the DeLong test) and performed better than two experts' prediction of pCR (both P < 0.01 for comparison of total accuracy). Besides, prediction within the hormone receptor-positive/human epidermal growth factor receptor 2 (HER2)-negative, HER2+ and triple-negative subgroups also achieved good discrimination performance, with an AUC of 0.90, 0.95 and 0.93, respectively, in the external validation cohort. Decision curve analysis confirmed that the model was clinically useful. CONCLUSION: A deep learning-based radiomic nomogram had good predictive value for pCR in LABC, which could provide valuable information for individual treatment.