Faculty Bibliography

Subsolid nodules (SSNs) are increasingly encountered in chest computed tomography (CT) imaging and clinical practice, as awareness of their significance and CT utilization grows. Either part-solid or solely ground-glass in attenuation, SSNs are shown to correlate with lung adenocarcinomas and their precursors, although a differential diagnosis is to be considered that includes additional neoplastic and inflammatory etiologies. This review discusses the differential diagnosis for SSNs, imaging and clinical features, and pathology that are helpful when making management decisions that may include PET/CT, biopsy, or surgery. Potential pitfalls in nodule characterization and management will be highlighted, to aid in managing SSNs appropriately.

There is a body-wide network of interstitial spaces that includes three components: a large-scale fascial network made up of fluid-filled spaces containing collagens and other extracellular matrix components like hyaluronic acid (HA), the peri-vascular/capillary interstitium, and intercellular interstitial spaces. Staining for HA within the colon, skin, and liver has demonstrated spatial continuity of the fascial interstitium across tissue layers and between organs, while continuity of HA staining between perineurial and adventitial sheathes beyond organ boundaries confirmed that they also participate in this body-wide network. We asked whether the pulmonary interstitium comprises a continuous organ-wide network that also connects to the body-wide interstitium via routes along nerves and the vasculature. We studied archival lung lobectomy specimens containing normal tissues inclusive of all lung anatomical units from six females and three males (mean age 53+/- 16.5 years). For comparison, we also studied normal mouse lung. Multiplex immunohistochemical cocktails were used to identify: (1) HA, CD34, and vimentin - highlighting interstitium; (2) HA, CD34, and podoplanin (D2-40) - highlighting relationships between the interstitium, vasculature, and lymphatics. Sizes of extracellular APP were measured. Tissues from nine patients (six females, three males, mean age 53+/- 16.5 years) were studied. HA staining was continuous throughout the five major anatomic compartments of the lung: alveolar walls, subpleural connective tissue, centrilobular peribronchovascular compartment, interlobular septal compartment, and axial peribronchovascular of the hilum, with similar findings in murine lung tissue. Continuity with interstitial spaces of the perineurium and adventitia was confirmed. The distribution of APP corresponded to known routes of lymphatic drainage, superficial and deep. APP within perineurium and perivascular adventitia further demonstrated continuity between intra- and extrapulmonary interstitium. To conclude, all segments of the lung interstitium are connected and are linked along nerves and the vascular tree to a body-wide communication network. These findings have significant implications for understanding lung physiology and pathobiology, suggesting routes of passage for inflammatory cells and mediators, malignant cells, and infectious agents. Interstitial spaces may be important in microbiome signaling within and beyond the lung and may be a component of the lung-brain axis.

OBJECTIVE:Distinguishing post-COVID-19 residual abnormalities from interstitial lung abnormalities (ILA) on CT can be challenging if clinical information is limited. This study aimed to evaluate the diagnostic performance of radiologists in distinguishing post-COVID-19 residual abnormalities from ILA. METHODS:This multi-reader, multi-case study included 60 age- and sex-matched subjects with chest CT scans. There were 40 cases of ILA (20 fibrotic and 20 non-fibrotic) and 20 cases of post-COVID-19 residual abnormalities. Fifteen radiologists from multiple nations with varying levels of experience independently rated suspicion scores on a 5-point scale to distinguish post-COVID-19 residual abnormalities from fibrotic ILA or non-fibrotic ILA. Interobserver agreement was assessed using the weighted κ value, and the scores of individual readers were compared with the consensus of all readers. Receiver operating characteristic curve analysis was conducted to evaluate the diagnostic performance of suspicion scores for distinguishing post-COVID-19 residual abnormalities from ILA and for differentiating post-COVID-19 residual abnormalities from both fibrotic and non-fibrotic ILA. RESULTS:Radiologists' diagnostic performance for distinguishing post-COVID-19 residual abnormalities from ILA was good (area under the receiver operating characteristic curve (AUC) range, 0.67-0.92; median AUC, 0.85) with moderate agreement (κ = 0.56). The diagnostic performance for distinguishing post-COVID-19 residual abnormalities from non-fibrotic ILA was lower than that from fibrotic ILA (median AUC = 0.89 vs. AUC = 0.80, p = 0.003). CONCLUSION/CONCLUSIONS:Radiologists demonstrated good diagnostic performance and moderate agreement in distinguishing post-COVID-19 residual abnormalities from ILA, but careful attention is needed to avoid misdiagnosing them as non-fibrotic ILA. KEY POINTS/CONCLUSIONS:Question How good are radiologists at differentiating interstitial lung abnormalities (ILA) from changes related to COVID-19 infection? Findings Radiologists had a median AUC of 0.85 in distinguishing post-COVID-19 abnormalities from ILA with moderate agreement (κ = 0.56). Clinical relevance Radiologists showed good diagnostic performance and moderate agreement in distinguishing post-COVID-19 residual abnormalities from ILA; nonetheless, caution is needed in distinguishing residual abnormalities from non-fibrotic ILA.

The immunocompromised host is a patient who is at risk for life threatening complications. This article offers a structured approach to interpreting abnormal chest computed tomography (CT) scans in these patients. Immune defects are categorized as innate or adaptive and each is linked to specific infectious risks. CT scan findings are grouped into 5 categories: nodules and/or masses, consolidation or ground glass opacity, large airway abnormalities, pleural effusions, and lymphadenopathy. This algorithmic approach can guide clinicians in establishing a differential diagnosis for immunocompromised patients with abnormal chest CT scans and help them reach a faster and more accurate diagnosis.

PURPOSE/OBJECTIVE:Apical pleuroparenchymal scarring (APPS) is commonly seen on chest computed tomography (CT), though the imaging and clinical features, to the best of our knowledge, have never been studied. The purpose was to understand APPS's typical morphologic appearance and associated clinical features. PATIENTS AND METHODS/METHODS:A random generator selected 1000 adult patients from all 21516 chest CTs performed at urban outpatient centers from January 1, 2016 to December 31, 2016. Patients with obscuring apical diseases were excluded to eliminate confounding factors. After exclusions, 780 patients (median age: 64 y; interquartile range: 56 to 72 y; 55% males) were included for analysis. Two radiologists evaluated the lung apices of each CT for the extent of abnormality in the axial plane (mild: <5 mm, moderate: 5 to 10 mm, severe: >10 mm), craniocaudal plane (extension halfway to the aortic arch, more than halfway, vs below the arch), the predominant pattern (nodular vs reticular and symmetry), and progression. Cohen kappa coefficient was used to assess radiologists' agreement in scoring. Ordinal logistic regression was used to determine associations of clinical and imaging variables with APPS. RESULTS:APPS was present on 65% (507/780) of chest CTs (54% mild axial; 80% mild craniocaudal). The predominant pattern was nodular and symmetric. Greater age, female sex, lower body mass index, greater height, and white race were associated with more extensive APPS. APPS was not found to be associated with lung cancer in this cohort. CONCLUSION/CONCLUSIONS:Classifying APPS by the extent of disease in the axial or craniocaudal planes, in addition to the predominant pattern, enabled statistically significant associations to be determined, which may aid in understanding the pathophysiology of apical scarring and potential associated risks.

OBJECTIVES/OBJECTIVE:To evaluate the clinical significance of low-field MRI lung opacity severity. METHODS:Retrospective cross-sectional analysis of post-acute Covid-19 patients imaged with low-field MRI from 9/2020 through 9/2022, and within 1 month of pulmonary function tests (PFTs), 6-min walk test (6mWT), and symptom inventory (SI), and/or within 3 months of St. George Respiratory Questionnaire (SGRQ) was performed. Univariate and correlative analyses were performed with Wilcoxon, Chi-square, and Spearman tests. The association between disease and demographic factors and MR opacity severity, PFTs, 6mWT, SI, and SGRQ, and association between MR opacity severity with functional and patient-reported outcomes (PROs), was evaluated with mixed model analysis of variance, covariance and generalized estimating equations. Two-sided 5 % significance level was used, with Bonferroni multiple comparison correction. RESULTS:81 MRI exams in 62 post-acute Covid-19 patients (median age 57, IQR 41-64; 25 women) were included. Exams were a median of 8 months from initial illness. Univariate analysis showed lung opacity severity was associated with decreased %DLCO (ρ = -0.55, P = .0125), and lung opacity severity quartile was associated with decreased %DLCO, predicted TLC, FVC, and increased FEV1/FVC. Multivariable analysis adjusting for sex, initial disease severity, and interval from Covid-19 diagnosis showed MR lung opacity severity was associated with decreased %DLCO (P < .001). Lung opacity severity was not associated with PROs. CONCLUSION/CONCLUSIONS:Low-field MRI lung opacity severity correlated with decreased %DLCO in post-acute Covid-19 patients, but was not associated with PROs.

TOPIC IMPORTANCE/UNASSIGNED:Chest CT imaging holds a major role in the diagnosis of lung diseases, many of which affect the peribronchovascular region. Identification and categorization of peribronchovascular abnormalities on CT imaging can assist in formulating a differential diagnosis and directing further diagnostic evaluation. REVIEW FINDINGS/RESULTS:The peribronchovascular region of the lung encompasses the pulmonary arteries, airways, and lung interstitium. Understanding disease processes associated with structures of the peribronchovascular region and their appearances on CT imaging aids in prompt diagnosis. This article reviews current knowledge in anatomic and pathologic features of the lung interstitium composed of intercommunicating prelymphatic spaces, lymphatics, collagen bundles, lymph nodes, and bronchial arteries; diffuse lung diseases that present in a peribronchovascular distribution; and an approach to classifying diseases according to patterns of imaging presentations. Lung peribronchovascular diseases can appear on CT imaging as diffuse thickening, fibrosis, masses or masslike consolidation, ground-glass or air space consolidation, and cysts, acknowledging that some diseases may have multiple presentations. SUMMARY/CONCLUSIONS:A category approach to peribronchovascular diseases on CT imaging can be integrated with clinical features as part of a multidisciplinary approach for disease diagnosis.

PURPOSE/OBJECTIVE:To determine the performance of volumetric dual energy low kV and iodine radiomic features for the differentiation of intrathoracic lymph node histopathology, and influence of contrast protocol. MATERIALS AND METHODS/METHODS:Intrathoracic lymph nodes with histopathologic correlation (neoplastic, granulomatous sarcoid, benign) within 90 days of DECT chest imaging were volumetrically segmented. 1691 volumetric radiomic features were extracted from iodine maps and low-kV images, totaling 3382 features. Univariate analysis was performed using 2-sample t-test and filtered for false discoveries. Multivariable analysis was used to compute AUCs for lymph node classification tasks. RESULTS:129 lymph nodes from 72 individuals (mean age 61 ± 15 years) were included, 52 neoplastic, 51 benign, and 26 granulomatous-sarcoid. Among all contrast enhanced DECT protocol exams (routine, PE and CTA), univariable analysis demonstrated no significant differences in iodine and low kV features between neoplastic and non-neoplastic lymph nodes; in the subset of neoplastic versus benign lymph nodes with routine DECT protocol, 199 features differed (p = .01- < 0.05). Multivariable analysis using both iodine and low kV features yielded AUCs >0.8 for differentiating neoplastic from non-neoplastic lymph nodes (AUC 0.86), including subsets of neoplastic from granulomatous (AUC 0.86) and neoplastic from benign (AUC 0.9) lymph nodes, among all contrast protocols. CONCLUSIONS:Volumetric DECT radiomic features demonstrate strong collective performance in differentiation of neoplastic from non-neoplastic intrathoracic lymph nodes, and are influenced by contrast protocol.

Imaging plays a major role in the care of the intensive care unit (ICU) patients. An understanding of the monitoring devices is essential for the interpretation of imaging studies. An awareness of their expected locations aids in identifying complications in a timely manner. This review describes the imaging of ICU monitoring and support catheters, tubes, and pulmonary and cardiac devices, some more commonly encountered and others that have been introduced into clinical patient care more recently. Special focus will be placed on chest radiography and potential pitfalls encountered.