Ultra-low-dose computed tomography (ULDCT) may have similar efficacy as low-dose CT (LDCT) for detecting a variety of pulmonary conditions in people with current or past smoking histories, but had poor detection of ground glass opacification lesions, according to a recent prospective study presented at the Radiological Society of North America (RSNA) conference.
In a recent poster presentation at the Radiological Society of North America (RSNA) conference, researchers shared preliminary findings from a prospective trial that compared the use of ultra-low-dose computed tomography (ULDCT) to low-dose CT (LDCT) in current smokers and those with a previous smoking history.
For the study, researchers examined the use of LDCT dosing (mean effective radiation dose of 1.40 mSv) versus ULDCT dosing (mean effective radiation dose of 0.39 mSv) in consecutive patients referred for lung cancer screening over a one-month period. The study cohort included current and previous smokers with a 20 pack-year history, according to the study. The researchers noted that 58 of the 265 enrolled patients had abnormal CT scans. The CT scans were reviewed by two senior thoracic radiologists.
The study authors found a 92 percent agreement between ULDCT and LDCT for emphysema and a 91 percent agreement for pulmonary nodules. Agreement for findings such as bronchial wall thickening, atelectasis and bronchiectasis ranged between 80 to 90 percent, according to the study.
However, the researchers noted that agreement between ULDCT and LDCT ranged between 60 to 80 percent for septal thickening, pleural effusion, and consolidation. They also noted poor agreement (37 percent) for ground glass opacification (GGO) lesions.
While the researchers noted the use of ULDCT could reduce effective radiation dosing to a third of that used for LDCT, they said the significant potential for missing other lung cancers such as lepidic adenocarcinoma outweigh the benefits of reduced radiation dosing.
“Despite the exciting agreement for some pulmonary findings, these results question whether using an ULDCT protocol for (lung cancer screening) would be adequate as many subsolid or GGO lesions could be missed,” wrote Matheus Zanon, MD, MSc, who is affiliated with the Department of Radiology at Pontificia Universidade Catolica do Rio Grande do Sul in Porto Alegre, Brazil, and colleagues.
Can Radiomics Bolster Low-Dose CT Prognostic Assessment for High-Risk Lung Adenocarcinoma?
December 16th 2024A CT-based radiomic model offered over 10 percent higher specificity and positive predictive value for high-risk lung adenocarcinoma in comparison to a radiographic model, according to external validation testing in a recent study.
Can AI Facilitate Single-Phase CT Acquisition for COPD Diagnosis and Staging?
December 12th 2024The authors of a new study found that deep learning assessment of single-phase CT scans provides comparable within-one stage accuracies to multiphase CT for detecting and staging chronic obstructive pulmonary disease (COPD).
The Reading Room: Artificial Intelligence: What RSNA 2020 Offered, and What 2021 Could Bring
December 5th 2020Nina Kottler, M.D., chief medical officer of AI at Radiology Partners, discusses, during RSNA 2020, what new developments the annual meeting provided about these technologies, sessions to access, and what to expect in the coming year.
Can MRI-Based AI Bolster Biopsy Decision-Making in PI-RADS 3 Cases?
December 9th 2024In patients with PI-RADS 3 lesion assessments, the combination of AI and prostate-specific antigen density (PSAD) level achieved a 78 percent sensitivity and 93 percent negative predictive value for clinically significant prostate cancer (csPCa), according to research presented at the Radiological Society of North American (RSNA) conference.