Faculty Bibliography

Background: Anterior mediastinal lesions may be radiographically challenging to diagnose. Helpful imaging findings for the radiologist and pathologist, and relevant reporting information for the clinician will be reviewed. * Educational Goals/Teaching Points: Review multimodality imaging of thymic lesions with histopathologic correlation * Thymic hyperplasia * Thymic cyst * Thymolipoma * Staging of thymic carcinoma * Highlight distinguishing features between common anterior mediastinal lesions * Lymphoma * Germ Cell tumor * Thyroid * Present atypical anterior mediastinal lesions * Ectopic Parathyroid * Lymphangioma * Lipoma and Lipomatosis * Sarcoma * Nodal mesothelioma
Conclusion(s): Pathologists and radiologists often work in tandem to arrive at a unifying diagnosis, and understanding the radiopathologic correlation of anterior mediastinal lesions will serve to improve diagnosis and reporting

Objectives: To differentiate pulmonary tumor type by volumetric iodine quantification and texture analysis on dual-energy CT images.
Material(s) and Method(s): Radiology information system search for all contrast-enhanced DECT chest examinations from 1/1/2015-4/30/ 2018 was performed, filtering for those with pathology within 120 days, yielding 80 cases of pathologically-proven pulmonary lesions. 73 lesions meeting inclusion criteria were manually volumetrically segmented via open-source software using the low-kV DECT dataset. 3D-iodine quantification was achieved by mapping between high/low energy HU on a representative 2D-image, and applying to surrounding slices, with absolute iodine normalized to mid-descending aorta. Full Width Tenth Maximum was applied to each normalized iodine histogram, providing a single comprehensive measure of relative iodine concentration. Volumetric iodine values and first order texture features were assessed using Hoteling's T-squared multivariate analysis.
Result(s): 72 individuals (37 women, 35 men) with mean age 64 years were included. 44 primary, 25 metastatic, and 3 benign lesions were assessed; 22 with history of chemotherapy. Mean time between histopathologic sampling and imaging was 26 days. Mean, median and minimum volumetric iodine concentration were significant (P< 0.05) in distinguishing primary versus metastatic lesions, with P<0.01 for these measures between de novo primary and metastatic lesions. Metastatic lesions demonstrated higher mean iodine (1.2 mg/mL) in comparison to primary lesions (0.83 mg/mL). Mean and median 3D-iodine values significantly (P<0.05) differed between primary lung adenocarcinoma and squamous lesions (mean 0.95 mg/mL and 0.49 mg/mL, respectively). Skewness significantly differed between de novo versus treated primary (P=0.01), and metastatic versus primary lesions (P=0.03), and entropy between de novo primary and metastatic lesions (P<0.001), and treated versus non-treated metastases (P= 0.03).
Conclusion(s): Volumetric iodine quantification significantly differs between de novo primary versus metastatic, and primary lung adenocarcinoma versus squamous lesions. Texture features may also have a role in distinguishing tumor type and treatment response. Clinical Relevance Application: Potential role of DECT in distinguishing tumor type and treatment response

Background: Subsolid nodules account for 20% of lesions detected on CT lung cancer screening, and many are incidentally detected on routine CT exams. Persistent subsolid nodules may correlate with adenocarcinoma spectrum, though commonly are due to other etiologies, emphasizing the importance of accurate nodule characterization and management recommendations. This review will used a case-based approach to showcase how lesion description and management may differ from that of solid nodules, including application of Fleischner guidelines and discussion of management options. Educational Goals/Teaching Points: * Highlight technical pitfalls in evaluation of subsolid nodules * Highlight interpretive pitfalls in evaluation of subsolid nodules * Review differential considerations including adenocarcinoma spectrum: AAH, AIS, MIA, Invasive adenocarcinoma * Describe differential considerations beyond the adenocarcinoma spectrum, such as: * Neoplastic: lymphoma, metastatic disease, treatment response * Infectious/Inflammatory: organizing pneumonia, eosinophilic pneumonia, alveolar sarcoid, various infection, drug reaction * Adapt management guidelines to diverse clinical scenarios: age, multiplicity, baseline versus follow-up, incidental versus screening * Explain treatment options for subsolid adenocarcinoma lesions including emerging technology such as cryoablation
Conclusion(s): Subsolid nodules are commonly encountered clinically, with accurate interpretation based on avoiding technical pitfalls and providing relevant descriptors to differentiate actionable findings. Management guidelines and further steps must take into account nodule features, clinical risk factors, and patient specific considerations

Lung cancer remains the most common cause of cancer death in the United States of America and worldwide despite continued advances in lung cancer screening as well as surgical, medical, and radiation oncological treatments. Adenocarcinoma is the most common histological subtype of primary lung cancer and has recently been reorganized into a spectrum ranging from preinvasive lesions to invasive adenocarcinoma. An understanding of the pathology, diagnosis, and management of the spectrum of lung adenocarcinoma is more important than ever, considering the central role of the radiologist. The aim of this review is to describe the subtypes of the lung adenocarcinoma spectrum in terms of histological and imaging features, their pattern of growth on imaging, management, staging, and evolving knowledge of tumor genetics.

Diseases that are predominantly peribronchovascular in distribution on computed tomography by definition involve the bronchi, adjacent vasculature, and associated lymphatics involving the central or axial lung interstitium. An understanding of diseases that can present with focal peribronchovascular findings is useful for establishing diagnoses and guiding patient management. This review will cover clinical and imaging features that may assist in differentiating amongst the various causes of primarily peribronchovascular disease.

The ACR Incidental Findings Committee presents recommendations for managing incidentally detected mediastinal and cardiovascular findings found on CT. The Chest Subcommittee was composed of thoracic radiologists who developed the provided guidance. These recommendations represent a combination of current published evidence and expert opinion and were finalized by informal iterative consensus. The recommendations address the most commonly encountered mediastinal and cardiovascular incidental findings and are not intended to be a comprehensive review of all incidental findings associated with these compartments. Our goal is to improve the quality of care by providing guidance on how to manage incidentally detected thoracic findings.

RATIONALE AND OBJECTIVES/OBJECTIVE:This study aims to determine the optimal photon energy for image quality of the pulmonary arteries (PAs) on dual-energy computed tomography (CT) pulmonary angiography (CTPA) utilizing low volumes of iodinated contrast. MATERIALS AND METHODS/METHODS:The study received institutional review board exemption and was Health Insurance Portability and Accountability Act compliant. Adults (n = 56) who underwent dual-energy CTPA with 50-60 cc of iodinated contrast on a third-generation dual-source multidetector CT were retrospectively and consecutively identified. Twelve virtual monoenergetic kiloelectron volt (keV) image data sets (40-150 keV, 10-keV increments) were generated with a second-generation noise-reducing algorithm. Standard regions of interest were placed on main, right, left, and right interlobar pulmonary arteries; pectoralis muscle; and extrathoracic air. Attenuation [mean CT number (Hounsfield unit, HU)], noise [standard deviation (HU)], signal to noise (SNR), and contrast to noise ratio were evaluated. Three blinded chest radiologists rated (from 1 to 5, with 5 being the best) randomized monoenergetic and weighted-average images for attenuation and noise. P <.05 was considered significant. RESULTS:Region of interest mean CT number increased as keV decreased, with 40 keV having the highest value (P < .001). Mean SNR was highest for 40-60 keV (P <.05) (14.5-14.7) and was higher (P <.05) than all remaining energies (90-150 keV) for all vessel regions combined. Contrast to noise ratio was highest for 40 keV (P <.001) and decreased as keV increased. SNR was highest at 60 and 70 keV, only slightly higher than 40-50 keV (P <.05). Reader scores for 40-50 keV were greater than other energies and weighted-average images (P <.05). CONCLUSIONS:Kiloelectron volt images of 40-50 keV from the second-generation algorithm optimize attenuation on dual-energy CTPA and can potentially aid in interpretation and avoiding nondiagnostic examinations.

The incidental pulmonary nodule is commonly encountered when interpreting chest CTs. The management of pulmonary nodules requires a multidisciplinary approach entailing integration of nodule size and features, clinical risk factors, and patient preference and comorbidities. Guidelines have been issued for the management of both solid and subsolid nodules, with the Fleischner Society issuing revised guidelines in 2017. This article focuses on the CT imaging characteristics and clinical behavior of pulmonary nodules, with review of the current management guidelines that reflect this knowledge.

Incidentally detected lung nodules are increasingly common in routine diagnostic computed tomography (CT) imaging. Formal management recommendations for incidental nodules, such as those outlined by the Fleischner Society, must therefore reflect a balance of malignancy risk and the clinical context in which nodules are discovered. Nodule size, attenuation, morphology, and location all influence the likelihood of malignancy and, thus, the necessity and timing of follow-up according to current Fleischner recommendations. As technological advancements in CT imaging continue, there may be greater reliance on advanced computerized analysis of lung nodule features to help determine the risk of clinically significant disease.

RATIONALE AND OBJECTIVES: This study aimed to differentiate pathologically defined lepidic predominant lesions (LPL) from more invasive adenocarcinomas (INV) using three-dimensional (3D) volumetric density and first-order texture histogram analysis of surgically excised stage 1 lung adenocarcinomas. MATERIALS AND METHODS: This retrospective study was institutional review board approved and Health Insurance Portability and Accountability Act compliant. Sixty-four cases of pathologically proven stage 1 lung adenocarcinoma surgically resected between September 2006 and October 2015, including LPL (n = 43) and INV (n = 21), were evaluated using high-resolution computed tomography. Quantitative measurements included nodule volume, percent solid volume (% solid), and first-order texture histogram analysis including skewness, kurtosis, entropy, and mean nodule attenuation within each histogram quartile. Binomial logistic regression models were used to identify the best set of parameters distinguishing LPL from INV. RESULTS: Univariate analysis of 3D volumetric density and histogram features was statistically significant between LPL and INV groups (P < .05). Accuracy of a binomial logistic model to discriminate LPL from INV based on size and % solid was 85.9%. With optimized probability cutoff, the model achieves 81% sensitivity, 76.7% specificity, and area under the receiver operating characteristic curve of 0.897 (95% confidence interval, 0.821-0.973). An additional model based on size and mean nodule attenuation of the third quartile (Hu_Q3) of the histogram achieved similar accuracy of 81.3% and area under the receiver operating characteristic curve of 0.877 (95% confidence interval, 0.790-0.964). CONCLUSIONS: Both 3D volumetric density and first-order texture analysis of stage 1 lung adenocarcinoma allow differentiation of LPL from more invasive adenocarcinoma with overall accuracy of 85.9%-81.3%, based on multivariate analyses of either size and % solid or size and Hu_Q3, respectively.