Imaging of Castleman Disease.

Castleman disease (CD) is a group of rare and complex lymphoproliferative disorders that can manifest in two general forms: unicentric CD (UCD) and multicentric CD (MCD). These two forms differ in clinical manifestation, imaging appearances, treatment options, and prognosis. UCD typically manifests as a solitary enlarging mass that is discovered incidentally or after development of compression-type symptoms. MCD usually manifests acutely with systemic symptoms including fever and weight loss. As a whole, CD involves lymph nodes throughout the chest, neck, abdomen, pelvis, and axilla and can have a wide variety of imaging appearances. Most commonly, lymph nodes or masses in UCD occur in the chest, classically with well-defined borders, hyperenhancement, and possible characteristic patterns of calcification and/or feeding vessels. Lymph nodes affected by MCD, while also hyperenhancing, tend to involve multiple nodal chains and manifest alongside anasarca or hepatosplenomegaly. The polyneuropathy, organomegaly, endocrinopathy, monoclonal plasma cell disorder, and skin changes (POEMS) subtype of MCD may demonstrate lytic or sclerotic osseous lesions in addition to features typical of MCD. Since a diagnosis of CD based solely on imaging findings is often not possible, pathologic confirmation with core needle biopsy and/or surgical excision is necessary. Nevertheless, imaging plays a crucial role in supporting the diagnosis of CD, guiding appropriate regions for biopsy, and excluding other potential causes or mimics of disease. CT is frequently the initial imaging technique used in evaluating potential CD. MRI and PET play important roles in thoroughly evaluating the disease and determining its extent, especially the MCD form. Complete surgical excision is typically curative for UCD. MCD usually requires systemic therapy. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Imaging Considerations and Thoracic Diseases in Females.

Females have unique physiological considerations that must be considered during image acquisition and interpretation. Familiarity with imaging of pregnant and lactating patients requires modifications from standard protocols to mitigate radiation-induced risk to both the mother and the fetus. Manifestations in the chest related to conception, pregnancy, and gynecologic pathologies are female-specific. Furthermore, there are a number of conditions that exclusively or disproportionately affect females, including diffuse lung diseases. Here, we review the expected normal findings and variant appearances of diseases in female patients on chest imaging to help the radiologist arrive at the correct diagnosis and avoid diagnostic pitfalls. In addition, the general principles of thoracic imaging in women of reproductive age are discussed.

Lung Cancer Screening Using Clinical Photon-Counting Detector Computed Tomography and Energy-Integrating-Detector Computed Tomography: A Prospective Patient Study.

OBJECTIVE: To evaluate the diagnostic quality of photon-counting detector (PCD) computed tomography (CT) in patients undergoing lung cancer screening compared with conventional energy-integrating detector (EID) CT in a prospective multireader study. MATERIALS: Patients undergoing lung cancer screening with conventional EID-CT were prospectively enrolled and scanned on a PCD-CT system using similar automatic exposure control settings and reconstruction kernels. Three thoracic radiologists blinded to CT system compared PCD-CT and EID-CT images and scored examinations using a 5-point Likert comparison score (-2 [left image is worse] to +2 [left image is better]) for artifacts, sharpness, image noise, diagnostic image quality, emphysema visualization, and lung nodule evaluation focusing on the border. Post hoc correction of Likert scores was performed such that they reflected PCD-CT performance in comparison to EID-CT. A nonreader radiologist measured objective image noise. RESULTS: Thirty-three patients (mean, 66.9 ± 5.6 years; 11 female; body mass index; 30.1 ± 5.1 kg/m 2 ) were enrolled. Mean volume CT dose index for PCD-CT was lower (0.61 ± 0.21 vs 0.73 ± 0.22; P < 0.001). Pooled reader results showed significant differences between imaging modalities for all comparative rankings ( P < 0.001), with PCD-CT favored for sharpness, image noise, image quality, and emphysema visualization and lung nodule border, but not artifacts. Photon-counting detector CT had significantly lower image noise (74.4 ± 10.5 HU vs 80.1 ± 8.6 HU; P = 0.048). CONCLUSIONS: Photon-counting detector CT with similar acquisition and reconstruction settings demonstrated improved image quality and less noise despite lower radiation dose, with improved ability to depict pulmonary emphysema and lung nodule borders compared with EID-CT at low-dose lung cancer CT screening.

Imaging Patterns in Occupational Lung Disease-When Should I Consider?

Occupational lung diseases (OLDs) encompass a broad group of entities related to the inhalation of a variety of agents in the workplace. OLDs may affect the lung parenchyma, pleura, and/or airways. OLDs can pose a diagnostic challenge for radiologists due to a lack of exposure history and overlap in imaging findings with nonoccupational-related entities. For this reason, it is important for the radiologist to be familiar with the high-resolution computed tomography patterns associated with OLDs and consider OLDs when formulating a differential.

Predicting Usual Interstitial Pneumonia Histopathology From Chest CT Imaging With Deep Learning.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a progressive, often fatal form of interstitial lung disease (ILD) characterized by the absence of a known cause and usual interstitial pneumonitis (UIP) pattern on chest CT imaging and/or histopathology. Distinguishing UIP/IPF from other ILD subtypes is essential given different treatments and prognosis. Lung biopsy is necessary when noninvasive data are insufficient to render a confident diagnosis. RESEARCH QUESTION: Can we improve noninvasive diagnosis of UIP be improved by predicting ILD histopathology from CT scans by using deep learning? STUDY DESIGN AND METHODS: This study retrospectively identified a cohort of 1,239 patients in a multicenter database with pathologically proven ILD who had chest CT imaging. Each case was assigned a label based on histopathologic diagnosis (UIP or non-UIP). A custom deep learning model was trained to predict class labels from CT images (training set, n = 894) and was evaluated on a 198-patient test set. Separately, two subspecialty-trained radiologists manually labeled each CT scan in the test set according to the 2018 American Thoracic Society IPF guidelines. The performance of the model in predicting histopathologic class was compared against radiologists' performance by using area under the receiver-operating characteristic curve as the primary metric. Deep learning model reproducibility was compared against intra-rater and inter-rater radiologist reproducibility. RESULTS: For the entire cohort, mean patient age was 62 ± 12 years, and 605 patients were female (49%). Deep learning performance was superior to visual analysis in predicting histopathologic diagnosis (area under the receiver-operating characteristic curve, 0.87 vs 0.80, respectively; P < .05). Deep learning model reproducibility was significantly greater than radiologist inter-rater and intra-rater reproducibility (95% CI for difference in Krippendorff's alpha did not include zero). INTERPRETATION: Deep learning may be superior to visual assessment in predicting UIP/IPF histopathology from CT imaging and may serve as an alternative to invasive lung biopsy.

Clinical, radiologic, and pathologic features and outcomes of pulmonary transthyretin amyloidosis.

INTRODUCTION: Amyloid transthyretin amyloidosis (ATTR) is characterized by deposition of a misfolded conformation of the transport protein TTR, most commonly in cardiac and nerve tissue, causing clinical disease. Pulmonary amyloidosis, or deposition of ATTR in lung tissue, is a poorly characterized manifestation of this disease. We present the clinical course, imaging characteristics, pathology results, and outcomes of a patient cohort diagnosed with pulmonary ATTR. METHODS: We retrospectively reviewed records of 28 patients with pulmonary ATTR seen at Mayo Clinic from September 30, 2005, through December 31, 2020. Data collected included information on demographics, subjective symptoms, tissue biopsy results, pulmonary function testing, imaging findings, and treatment. RESULTS: Of the patients, 89% were men; the median age was 74.5 years (range, 50-99 years). Patients were typically diagnosed after persistent dyspnea and abnormal chest imaging resulted in lung biopsy, which yielded the ATTR diagnosis. Most patients had a preexisting diagnosis of cardiac ATTR. The disease was wild-type in 62% and hereditary in 38%. Normal pulmonary function tests followed by a restrictive pattern were the most common presentation. Of the patients, 93% had chest computed tomography, with common findings of diffuse nodularity, calcified granulomas, interlobular septal thickening, and pleural effusions. Almost all patients had pulmonary vascular involvement, and half had interstitial involvement on tissue biopsy. One-third received either anti-amyloid pharmacotherapy or a heart transplant. Half of patients had died before the time of study inclusion. CONCLUSION: Pulmonary disease is a less common but clinically important manifestation of ATTR.

Deep Learning Improves the Temporal Reproducibility of Aortic Measurement.

Imaging-based measurements form the basis of surgical decision making in patients with aortic aneurysm. Unfortunately, manual measurement suffer from suboptimal temporal reproducibility, which can lead to delayed or unnecessary intervention. We tested the hypothesis that deep learning could improve upon the temporal reproducibility of CT angiography-derived thoracic aortic measurements in the setting of imperfect ground-truth training data. To this end, we trained a standard deep learning segmentation model from which measurements of aortic volume and diameter could be extracted. First, three blinded cardiothoracic radiologists visually confirmed non-inferiority of deep learning segmentation maps with respect to manual segmentation on a 50-patient hold-out test cohort, demonstrating a slight preference for the deep learning method (p < 1e-5). Next, reproducibility was assessed by evaluating measured change (coefficient of reproducibility and standard deviation) in volume and diameter values extracted from segmentation maps in patients for whom multiple scans were available and whose aortas had been deemed stable over time by visual assessment (n = 57 patients, 206 scans). Deep learning temporal reproducibility was superior for measures of both volume (p < 0.008) and diameter (p < 1e-5) and reproducibility metrics compared favorably with previously reported values of manual inter-rater variability. Our work motivates future efforts to apply deep learning to aortic evaluation.

Imaging primer for CT angiography in peripheral vascular trauma.

The use of computed tomography angiography (CTA) for the evaluation of peripheral vascular trauma has become increasingly prevalent in the past decade with the development of multidetector CT (MDCT) and multiple studies subsequently demonstrating high sensitivity, specificity, and diagnostic accuracy when compared with conventional angiography. Additional benefits of MDCT include the ability to rapidly acquire the images, perform multiplanar and 3D reconstructions, and assess the adjacent soft tissues and bones. Rapid intravenous injection of iodinated contrast material is required for optimal arterial enhancement. CTA manifestations of an arterial injury may be direct, and include active contrast extravasation, pseudoaneurysm, arteriovenous fistula (AVF), intimal injury, dissection, or occlusion. There are also indirect signs which have a high association with vascular injury, and should raise suspicion, when present. Pitfalls related to image acquisition or patient factors can be mitigated with appropriate planning and post-processing techniques.

Chest Imaging in the Diagnosis of Occupational Lung Diseases.

Imaging plays a crucial role in the diagnosis and monitoring of occupational lung diseases (OLDs); however, the sensitivity and specificity of detection and diagnosis vary greatly depending on the imaging modality used. There is substantial overlap in appearance with non-occupation-related entities. OLDs should be considered in the differential even in the absence of a provided exposure history. Because many findings are not specific, a multidisciplinary approach is important in arriving at the diagnosis and will continue to be important as workplace-related pulmonary diseases evolve with changing industrial practices and workplace regulations.

Diffuse Pulmonary Ossification on High-Resolution Computed Tomography in Idiopathic Pulmonary Fibrosis, Systemic Sclerosis-Related Interstitial Lung Disease, and Chronic Hypersensitivity Pneumonitis: A Comparative Study.

OBJECTIVE: The aim of the study was to compare the prevalence of diffuse pulmonary ossification (DPO) in idiopathic pulmonary fibrosis (IPF), systemic sclerosis (SSc)-related interstitial lung disease (ILD), and chronic hypersensitivity pneumonitis (HP). METHODS: High-resolution computed tomography (HRCT) from 71 IPF, 67 SSc-ILD, and 75 HP cases were independently evaluated by 2 thoracic radiologists blinded to patient data. Studies were assessed for the presence of DPO, HRCT scanning pattern, stigmata of granulomatous infection, and honeycombing. RESULTS: The prevalence of DPO was significantly higher in cases of IPF and SSc compared with HP, although there was no significant difference in prevalence between the IPF and SSc groups, even when accounting for the presence of prior granulomatous infection. Interobserver agreement for the presence of DPO was substantial. CONCLUSIONS: Although prevalence DPO on HRCT varies between some forms of ILD, the use of DPO to influence characterization of ILD should be considered with caution.

The utility of chest computed tomography (CT) and RT-PCR screening of asymptomatic patients for SARS-CoV-2 prior to semiurgent or urgent hospital procedures.

OBJECTIVE: Presently, evidence guiding clinicians on the optimal approach to safely screen patients for coronavirus disease 2019 (COVID-19) to a nonemergent hospital procedure is scarce. In this report, we describe our experience in screening for SARS-CoV-2 prior to semiurgent and urgent hospital procedures. DESIGN: Retrospective case series. SETTING: A single tertiary-care medical center. PARTICIPANTS: Our study cohort included patients ≥18 years of age who had semiurgent or urgent hospital procedures or surgeries. METHODS: Overall, 625 patients were screened for SARS-CoV-2 using a combination of phone questionnaire (7 days prior to the anticipated procedure), RT-PCR and chest computed tomography (CT) between March 1, 2020, and April 30, 2020. RESULTS: Of the 625 patients, 520 scans (83.2%) were interpreted as normal; 1 (0.16%) had typical features of COVID-19; 18 scans (2.88%) had indeterminate features of COVID-19; and 86 (13.76%) had atypical features of COVID-19. In total, 640 RT-PCRs were performed, with 1 positive result (0.15%) in a patient with a CT scan that yielded an atypical finding. Of the 18 patients with chest CTs categorized as indeterminate, 5 underwent repeat negative RT-PCR nasopharyngeal swab 1 week after their initial swab. Also, 1 patient with a chest CT categorized as typical had a follow-up repeat negative RT-PCR, indicating that the chest CT was likely a false positive. After surgery, none of the patients developed signs or symptoms suspicious of COVID-19 that would indicate the need for a repeated RT-PCR or CT scan. CONCLUSION: In our experience, chest CT scanning did not prove provide valuable information in detecting asymptomatic cases of SARS-CoV-2 (COVID-19) in our low-prevalence population.

The Four Corners Sign: A Specific Imaging Feature in Differentiating Systemic Sclerosis-related Interstitial Lung Disease From Idiopathic Pulmonary Fibrosis.

PURPOSE: Differentiating between systemic sclerosis-related interstitial lung disease (SSc-ILD) and idiopathic pulmonary fibrosis (IPF) is important because of the differences in workup, prognosis, and treatment. However, there is much overlap in the appearance of these 2 entities on high-resolution computed tomography. We propose that inflammation and/or fibrosis focally or disproportionately involving the bilateral anterolateral upper lobes and posterosuperior lower lobes ["Four Corners" Sign (FCS)] is specific for SSc-ILD. MATERIALS AND METHODS: Randomized high-resolution computed tomography studies from 74 IPF and 73 SSc-ILD cases were evaluated by 2 thoracic radiologists blinded to all patient data. For each case the reviewers noted whether the FCS was present and assigned a confidence level on the basis of a 7-point Likert scale. The same process was then performed on a randomized external validation group of 42 SSc-ILD and 42 IPF cases. RESULTS: For Likert scores of 6 or 7 ("mostly agree" or "entirely agree" that the FCS is present, respectively) the sensitivity in SSc was 16.4% (95% confidence interval, 9.7%, 26.6%), specificity 100.0% (95% confidence interval, 95.1%, 100.0%). There was a significant association between a confidently present FCS and SSc compared with a confidently present FCS and IPF (P=0.0003). Analysis on an external validation group of 42 SSc and 42 IPF cases conferred similarly high specificity for SSc in cases characterized as FCS with high confidence. CONCLUSION: The FCS, a pattern of focal or disproportionate inflammation and/or fibrosis involving the bilateral anterolateral upper lobes and posterosuperior lower lobes, is specific for SSc-ILD when readers are confident of its presence.

Comparing magnetic resonance angiography (MRA) and computed tomography angiography (CTA) with conventional angiography in the detection of distal territory cerebral mycotic and oncotic aneurysms.

BACKGROUND: Mycotic and oncotic aneurysms may result in devastating neurologic sequelae if undetected. The objectives of this study were to examine interobserver variability and accuracy of cross-sectional imaging for the detection of distal territory mycotic and oncotic aneurysms. METHODS: We searched our institutional database for all radiology reports from 2005 to 2015 with an indication or diagnosis of mycotic or oncotic aneurysm. Patients who underwent DSA and either CTA or MRA within 12 weeks of each other were identified. The cross-sectional images from each study were blinded and reviewed by two radiologists. If positive for aneurysm, location and number of aneurysms were reported. Sensitivity, specificity, positive predictive value, negative predictive value, and interobserver variability were determined for MRA and MRA/CTA. RESULTS: Twenty-five patients were included in this study. Ten (40%) harbored distal aneurysms. Cross-sectional imaging had a sensitivity of 45.5%, specificity of 90.0%, and kappa value of 0.29 (0.00-0.69) for the detection of cerebral mycotic and oncotic aneurysms. CONCLUSIONS: Because of the low sensitivity and high interobserver variability of cross-sectional imaging, DSA should remain the gold standard for evaluation of suspected oncotic and mycotic aneurysms. In cases in which cross sectional imaging is negative and there is a high clinical suspicion for mycotic aneurysm, DSA should be strongly considered.

Neuroimaging of a pilocytic astrocytoma with anaplastic features and diffusion tensor imaging characteristics.

We report the MRI findings of an adult patient with a (rare in adults) biopsy-proven pilocytic astrocytoma with anaplastic features. Diffusion tensor imaging may potentially provide information on cell proliferation, vascularity, and fiber destruction, which can have implications for treatment and prognosis. In this case, tractography and fractional anisotropy maps demonstrated displacement of adjacent parenchyma and relatively intact fractional anisotropy, suggesting a pilocytic rather than an anaplastic astrocytoma.