Imaging Pearls ❯ Chest ❯ Chest Wall
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- Background: Visual assessment remains the standard for evaluating emphysema at CT; however, it is time consuming, is subjective,requires training, and is affected by variability that may limit sensitivity to longitudinal change.
Purpose: To evaluate the clinical and imaging significance of increasing emphysema severity as graded by a deep learning algorithmon sequential CT scans in cigarette smokers.
Materials and Methods: A secondary analysis of the prospective Genetic Epidemiology of Chronic Obstructive Pulmonary Disease COPDGene) study participants was performed and included baseline and 5-year follow-up CT scans from 2007 to 2017. Emphysema was classified automatically according to the Fleischner emphysema grading system at baseline and 5-year follow-up using a deep learning model. Baseline and change in clinical and imaging parameters at 5-year follow-up were compared in participants whose emphysema progressed versus those who did not. Kaplan-Meier analysis and multivariable Cox regression were used to assess the relationship between emphysema score progression and mortality.
Results: A total of 5056 participants (mean age, 60 years 6 9 [SD]; 2566 men) were evaluated. At 5-year follow-up, 1293 of the 5056 participants (26%) had emphysema progression according to the Fleischner grading system. This group demonstrated progressive airflow obstruction (forced expiratory volume in 1 second [percent predicted]: –3.4 vs –1.8), a greater decline in 6-minute walk distance (–177 m vs –124 m), and greater progression in quantitative emphysema extent (adjusted lung density: –1.4 g/L vs 0.5 g/L; percentage of lung voxels with CT attenuation less than 2950 HU: 0.6 vs 0.2) than those with nonprogressive emphysema(P , .001 for each). Multivariable Cox regression analysis showed a higher mortality rate in the group with emphysema progression,with an estimated hazard ratio of 1.5 (95% CI: 1.2, 1.8; P , .001).
Conclusion: An increase in Fleischner emphysema grade on sequential CT scans using an automated deep learning algorithm was associated with increased functional impairment and increased risk of mortality - Summary
Emphysema progression on CT scans scored using a deep learning algorithm was associated with increased functional impairment and mortality at 5-year follow-up.
Key Results
• A deep learning algorithm was used to classify emphysema at baseline and 5-year follow-up in 5056 participants.
• Of the 5056 participants, 1293 (26%) had an increase in emphysema grade at 5 years; these participants had progressive airflow obstruction, greater decline in 6-minute walk distance, and greater progression in emphysema extent than those with nonprogressive emphysema (P , .001 for each).
• Emphysema progression was associated with an increased mortality(hazard ratio: 1.5, P , .001).
Emphysema Progression at CT by Deep Learning Predicts Functional Impairment and Mortality: Results from the COPDGene Study
Andrea S. Oh et al.
Radiology 2022; 000:1–8 (in press) - “In conclusion, we applied a previously validated deep learning algorithm that automatically classifies emphysema pattern at CT according to the Fleischner classification system and demonstrated that an increase in emphysema severity score at 5 years was an independent predictor of diseaseprogression and mortality. These results suggest the clinical value of automatic, structured grading of emphysema severity at CT for identification of patients at greater risk. Possible applications include lung health assessments at lung cancer screening or entry criteria for clinical trials.”
Emphysema Progression at CT by Deep Learning Predicts Functional Impairment and Mortality: Results from the COPDGene Study
Andrea S. Oh et al.
Radiology 2022; 000:1–8 (in press)
- Since 1986, The Carter Center has led the international campaign to eradicate Guinea worm disease, working closely with ministries of health and local communities, the U.S. Centers for Disease Control and Prevention, the World Health Organization, UNICEF, and many others. Guinea worm disease could become the second human disease in history, after smallpox, to be eradicated. It would be the first parasitic disease to be eradicated and the first disease to be eradicated without the use of a vaccine or medicine.
- Guinea worm disease (dracunculiasis) is a parasitic infection caused by the nematode roundworm parasite Dracunculus medinensis. It is contracted when people consume water from stagnant sources contaminated with Guinea worm larvae. Inside a human's abdomen, Guinea worm larvae mate and female worms mature and grow. After about a year of incubation, the female Guinea worm, one meter long, creates an agonizingly painful lesion on the skin and slowly emerges from the body. Guinea worm sufferers may try to seek relief from the burning sensation caused by the emerging worm and immerse their limbs in water sources, but this contact with water stimulates the emerging worm to release its larvae into the water and begin the cycle of infection all over again.
- How Widespread is the Disease?
- In 1986, the disease afflicted an estimated 3.5 million people a year in 21 countries in Africa and Asia. Today, thanks to the work of The Carter Center and its partners — including the countries themselves — the incidence of Guinea worm has been reduced by more than 99.99 percent to 27 provisional* cases in 2020.
- The Carter Center works to eradicate Guinea worm in five countries affected by the disease: South Sudan, Mali, Chad, Ethiopia, and Angola.