Silent Airway Mucus Plugs in COPD and Clinical Implications.

BACKGROUND: Airway mucus plugs are frequently identified on CT scans of patients with COPD with a smoking history without mucus-related symptoms (ie, cough, phlegm [silent mucus plugs]). RESEARCH QUESTION: In patients with COPD, what are the risk and protective factors associated with silent airway mucus plugs? Are silent mucus plugs associated with functional, structural, and clinical measures of disease? STUDY DESIGN AND METHODS: We identified mucus plugs on chest CT scans of participants with COPD from the COPDGene study. The mucus plug score was defined as the number of pulmonary segments with mucus plugs, ranging from 0 to 18, and categorized into three groups (0, 1-2, and ≥ 3). We determined risk and protective factors for silent mucus plugs and the associations of silent mucus plugs with measures of disease severity using multivariable linear and logistic regression models. RESULTS: Of 4,363 participants with COPD, 1,739 had no cough or phlegm. Among the 1,739 participants, 627 (36%) had airway mucus plugs identified on CT scan. Risk factors of silent mucus plugs (compared with symptomatic mucus plugs) were older age (OR, 1.02), female sex (OR, 1.40), and Black race (OR, 1.93) (all P values < .01). Among those without cough or phlegm, silent mucus plugs (vs absence of mucus plugs) were associated with worse 6-min walk distance, worse resting arterial oxygen saturation, worse FEV1 % predicted, greater emphysema, thicker airway walls, and higher odds of severe exacerbation in the past year in adjusted models. INTERPRETATION: Mucus plugs are common in patients with COPD without mucus-related symptoms. Silent mucus plugs are associated with worse functional, structural, and clinical measures of disease. CT scan-identified mucus plugs can complement the evaluation of patients with COPD.

Reader Perceptions and Impact of AI on CT Assessment of Air Trapping.

Quantitative imaging measurements can be facilitated by artificial intelligence (AI) algorithms, but how they might impact decision-making and be perceived by radiologists remains uncertain. After creation of a dedicated inspiratory-expiratory CT examination and concurrent deployment of a quantitative AI algorithm for assessing air trapping, five cardiothoracic radiologists retrospectively evaluated severity of air trapping on 17 examination studies. Air trapping severity of each lobe was evaluated in three stages: qualitatively (visually); semiquantitatively, allowing manual region-of-interest measurements; and quantitatively, using results from an AI algorithm. Readers were surveyed on each case for their perceptions of the AI algorithm. The algorithm improved interreader agreement (intraclass correlation coefficients: visual, 0.28; semiquantitative, 0.40; quantitative, 0.84; P < .001) and improved correlation with pulmonary function testing (forced expiratory volume in 1 second-to-forced vital capacity ratio) (visual r = -0.26, semiquantitative r = -0.32, quantitative r = -0.44). Readers perceived moderate agreement with the AI algorithm (Likert scale average, 3.7 of 5), a mild impact on their final assessment (average, 2.6), and a neutral perception of overall utility (average, 3.5). Though the AI algorithm objectively improved interreader consistency and correlation with pulmonary function testing, individual readers did not immediately perceive this benefit, revealing a potential barrier to clinical adoption. Keywords: Technology Assessment, Quantification © RSNA, 2021.

CT patterns of fungal pulmonary infections of the lung: comparison of standard-dose and simulated low-dose CT.

PURPOSE: To assess the effect of radiation dose reduction on the appearance and visual quantification of specific CT patterns of fungal infection in immuno-compromised patients. MATERIALS AND METHODS: Raw data of thoracic CT scans (64 × 0.75 mm, 120 kVp, 300 reference mAs) from 41 consecutive patients with clinical suspicion of pulmonary fungal infection were collected. In 32 patients fungal infection could be proven (median age of 55.5 years, range 35-83). A total of 267 cuboids showing CT patterns of fungal infection and 27 cubes having no disease were reconstructed at the original and 6 simulated tube currents of 100, 40, 30, 20, 10, and 5 reference mAs. Eight specific fungal CT patterns were analyzed by three radiologists: 76 ground glass opacities, 42 ground glass nodules, 51 mixed, part solid, part ground glass nodules, 36 solid nodules, 5 lobulated nodules, 6 spiculated nodules, 14 cavitary nodules, and 37 foci of air-space disease. The standard of reference was a consensus subjective interpretation by experts whom were not readers in the study. RESULTS: The mean sensitivity and standard deviation for detecting pathological cuboids/disease using standard dose CT was 0.91 ± 0.07. Decreasing dose did not affect sensitivity significantly until the lowest dose level of 5 mAs (0.87 ± 0.10, p=0.012). Nodular pattern discrimination was impaired below the dose level of 30 reference mAs: specificity for fungal 'mixed nodules' decreased significantly at 20, 10 and 5 reference mAs (p<0.05). At lower dose levels, classification drifted from 'solid' to 'mixed nodule', although no lesion was missed. CONCLUSION: Our simulation data suggest that tube current levels can be reduced from 300 to 30 reference mAs without impairing the diagnostic information of specific CT patterns of pulmonary fungal infections.