Recent advances and future perspectives of machine learning techniques offer promising applications in medical imaging. Machine learning has the potential to improve different steps of the radiology workflow including order scheduling and triage, clinical decision support systems, detection and interpretation of findings, postprocessing and dose estimation, examination quality control, and radiology reporting. In this article, the authors review examples of current applications of machine learning and artificial intelligence techniques in diagnostic radiology. In addition, the future impact and natural extension of these techniques in radiology practice are discussed.

This copy is for personal use only. To order printed copies, contact reprints@rsna.org I n P r e s s Abbreviations: ICU = intensive care unit; ACE2 = angiotensin converting enzyme 2; COVID-19 = Coronavirus disease 2019; RUQ = right upper quadrant; SARS-CoV-2 = Severe acute respiratory syndrome coronavirus 2.Key Results: -33% of inpatients with COVID-19 had abdominal imaging and 17% had cross-sectional imaging. Imaging was associated with age (OR 1.03 per year increase) and intensive care unit (ICU) admission (OR 17.3). -54% of right upper quadrant ultrasounds demonstrated findings of cholestasis. -31% of CTs showed bowel wall abnormalities. Signs of late ischemia were seen on 20% of CTs in ICU patients (2.7% of ICU patients), with pathologic correlation suggesting small vessel thrombosis. Summary Statement: Bowel abnormalities, including ischemia, and cholestasis were common findings on abdominal imaging of inpatients with COVID-19. I n P r e s s Abstract:Background: Angiotensin converting enzyme 2 (ACE2), a target of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), demonstrates its highest surface expression in the lung, small bowel, and vasculature, suggesting abdominal viscera may be susceptible to injury.Purpose: To report abdominal imaging findings in patients with coronavirus disease 2019 . Materials and Methods:In this retrospective cross-sectional study, patients consecutively admitted to a single quaternary care center from 3/27/2020 to 4/10/2020 who tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were included. Abdominal imaging studies performed in these patients were reviewed and salient findings recorded.Medical records were reviewed for clinical data. Univariable analysis and logistic regression were performed. Results: 412 patients (average age 57 years; range 18->90 years; 241 men, 171 women) were evaluated. 224 abdominal imaging studies were performed (radiographs, n=137; ultrasound, n=44; CT, n=42; MRI, n=1) in 134 patients (33%). Abdominal imaging was associated with age (odds ratio [OR] 1.03 per year increase, p=0.001) and ICU admission (OR 17.3, p<0.001). Bowel wall abnormalities were seen on 31% of CT scans (13 of 42) and were associated with ICU admission (OR 15.5, p=0.01). Bowel findings included pneumatosis or portal venous gas, seen on 20% of CT scans in ICU patients (4 of 20). Surgical correlation (n=4) revealed unusual yellow discoloration of bowel (n=3) and bowel infarction (n=2). Pathology demonstrated ischemic enteritis with patchy necrosis and fibrin thrombi in arterioles (n=2). Of right upper quadrant ultrasounds, 87% (32 of 37) were performed for liver laboratory findings, and 54% (20 of 37) demonstrated a dilated sludge-filled gallbladder suggestive of cholestasis. Patients with a cholecystostomy tube placed (n=4) had negative bacterial cultures. Conclusion: Bowel abnormalities and cholestasis were common findings on abdominal imaging of inpatients with COVID-19. Patients who went to laparotomy often had ischemia, possibly due to sma...

Key PointsQuestionWhat is the projected effect of lowering incident nonmedical prescription opioid use on the future trajectory of the opioid overdose crisis in the United States?FindingsIn this system dynamics model study, under current conditions, the opioid overdose crisis is expected to worsen—with the annual number of opioid overdose deaths projected to reach nearly 82 000 by 2025, resulting in approximately 700 000 deaths from 2016 to 2025. Interventions focused on lowering the incidence of prescription opioid misuse were projected to result in a 3.0% to 5.3% decrease in opioid overdose deaths over this period.MeaningPrevention of prescription opioid misuse alone is projected to have a modest effect on lowering opioid overdose deaths in the near future, and multipronged approach is needed to dramatically change the course of the epidemic.

No abstract

Improved therapeutic options for hepatocellular carcinoma and metastatic disease place greater demands on diagnostic and surveillance tests for liver disease. Existing diagnostic imaging techniques provide limited evaluation of tissue characteristics beyond morphology; perfusion imaging of the liver has potential to improve this shortcoming. The ability to resolve hepatic arterial and portal venous components of blood flow on a global and regional basis constitutes the primary goal of liver perfusion imaging. Earlier detection of primary and metastatic hepatic malignancies and cirrhosis may be possible on the basis of relative increases in hepatic arterial blood flow associated with these diseases. To date, liver flow scintigraphy and flow quantification at Doppler ultrasonography have focused on characterization of global abnormalities. Computed tomography (CT) and magnetic resonance (MR) imaging can provide regional and global parameters, a critical goal for tumor surveillance. Several challenges remain: reduced radiation doses associated with CT perfusion imaging, improved spatial and temporal resolution at MR imaging, accurate quantification of tissue contrast material at MR imaging, and validation of parameters obtained from fitting enhancement curves to biokinetic models, applicable to all perfusion methods. Continued progress in this new field of liver imaging may have profound implications for large patient groups at risk for liver disease.

Age, sex, and racial/ethnic disparities exist, but are understudied in pancreatic adenocarcinoma (PDAC). We used the Surveillance, Epidemiology, and End Results (SEER)–Medicare linked database to determine whether survival and treatment disparities persist after adjusting for demographic and clinical characteristics. Our study included PDAC patients diagnosed between 1992 and 2011. We used Cox regression to compare survival across age, sex, and race/ethnicity within early‐stage and late‐stage cancer subgroups, adjusting for marital status, urban location, socioeconomics, SEER region, comorbidities, stage, lymph node status, tumor location, tumor grade, diagnosis year, and treatment received. We used logistic regression to compare differences in treatment received across age, sex, and race/ethnicity. Among 20,896 patients, 84% were White, 9% Black, 5% Asian, and 2% Hispanic. Median age was 75; 56% were female and 53% had late‐stage cancer. Among early‐stage patients in the adjusted Cox model, older patient subgroups had worse survival compared with ages 66–69 (HR > 1.1, P < 0.01 for groups >69); no survival differences existed between sexes. Black (HR = 1.1, P = 0.01) and Hispanic (HR = 1.2, P < 0.01) patients had worse survival compared with White. Among late‐stage cancer patients, patients over age 84 had worse survival than those aged 66–69 (HR = 1.1, P < 0.01), and males (HR = 1.08, P < 0.01) had worse survival than females; there were no racial/ethnic differences. Older age and minority race/ethnicity were associated with lower likelihood of receiving chemotherapy, radiation, and/or surgery. Age and racial/ethnic disparities in survival outcomes and treatment received exist for PDAC patients; these disparities persist after adjusting for differences in demographic and clinical characteristics.

No abstract

No abstract