
Can abdominopelvic CT scans in patients without known COVID-19 -- and with no respiratory symptoms -- predict future surges of the disease in the greater population? It's possible, according to a study published September 30 in Academic Radiology.
The research indicates that unexpected lung base findings on abdominopelvic CT that suggest COVID-19 infection could help clinicians prepare for disease surges, wrote a team led by Dr. Paul Smereka of NYU Langone Health in New York City.
"The rise and fall of unexpected lung base findings suggestive of COVID-19 infection on abdominopelvic CT in patients without COVID-19 symptoms correlated with the number of confirmed new cases throughout NYC from the same time period," the group wrote. "A model using abdominopelvic CT lung base findings could serve as a surrogate for future COVID-19 outbreaks."
The fact that people can carry COVID-19 but be asymptomatic makes curbing its spread challenging, the team noted. Smereka and colleagues explored if lung findings indicative for COVID-19 on abdominopelvic CT scans could serve as a proxy for the diagnosis of COVID-19 in the community. Their study included 151 patients who presented at NYU Langone between March and May 2020 without respiratory symptoms but who showed signs of COVID-19 disease on 189 abdominopelvic CT exams.
The investigators searched the CT reports for keywords pointing to COVID-19 infection by lung base findings and tracked patients' COVID-19 status, respiratory symptoms, laboratory results, and outcomes such as hospitalization, ICU admission/intubation, or death. They then compared these findings to confirmed COVID-19 cases in New York City during the same time frame.
Of the 151 patients, 53 (35.1%) were confirmed to be positive for COVID-19, 19 were found to be negative (12.6%), and 79 (52.4%) had no COVID-19 test. Of the 189 abdominopelvic CT exams, almost half were deemed "COVID-19 likely" by thoracic radiologist readers.
Study data showed correlations between the number of abdominopelvic CT exams positive for COVID-19 and growth in the overall number of COVID-19 cases in New York City between March and May 2020.
| Correlation between abdominopelvic CT exams positive for COVID-19 and cases in New York City, March to May 2020 | ||
| Date | Patients who underwent abdominopelvic CT with no respiratory symptoms of COVID-19, tested positive | COVID-19 cases in NYC |
| 3/1/20-3/7/20 | 0 | 25 |
| 3/8/20-3/14/20 | 2 | 1,926 |
| 3/15/20-3/21/20 | 2 | 18,969 |
| 3/22/20-3/28/20 | 9 | 29,183 |
| 3/29/20-4/4/20 | 12 | 35,944 |
| 4/5/20-4/11/20 | 14 | 35,148 |
| 4/12/20-4/18/20 | 7 | 23,507 |
| 4/19/20-4/25/20 | 5 | 19,644 |
| 4/26/20-5/2/20 | 3 | 13,346 |
The findings demonstrate that abnormal results on abdominopelvic CT show promise as an early warning of a coming upsurge in COVID-19 cases, according to the authors.
"Our results show that abnormal lung base findings on abdominopelvic CT during the COVID-19 outbreak in New York City ... follow a similar trend to total confirmed new cases throughout [the city] during the same time period," they wrote. "Consequently, the rise in suspicious lung base findings in 'respiratory asymptomatic' patients could stand as a surrogate for COVID-19 community infection rates."












![Images show the pectoralis muscles of a healthy male individual who never smoked (age, 66 years; height, 178 cm; body mass index [BMI, calculated as weight in kilograms divided by height in meters squared], 28.4; number of cigarette pack-years, 0; forced expiratory volume in 1 second [FEV1], 97.6% predicted; FEV1: forced vital capacity [FVC] ratio, 0.71; pectoralis muscle area [PMA], 59.4 cm2; pectoralis muscle volume [PMV], 764 cm3) and a male individual with a smoking history and chronic obstructive pulmonary disorder (COPD) (age, 66 years; height, 178 cm; BMI, 27.5; number of cigarette pack-years, 43.2, FEV1, 48% predicted; FEV1:FVC, 0.56; PMA, 35 cm2; PMV, 480.8 cm3) from the Canadian Cohort Obstructive Lung Disease (i.e., CanCOLD) study. The CT image is shown in the axial plane. The PMV is automatically extracted using the developed deep learning model and overlayed onto the lungs for visual clarity.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/03/genkin.25LqljVF0y.jpg?auto=format%2Ccompress&crop=focalpoint&fit=crop&h=112&q=70&w=112)








