Retroperitoneal masses not arising from major solid organs are uncommon. Although there is no simple method of classifying retroperitoneal masses, a reasonable approach is to consider the masses as predominantly solid or cystic and to subdivide these into neoplastic and nonneoplastic masses. Because the treatment options vary, it is useful to be able to differentiate these masses by using imaging criteria. Although the differential diagnosis of retroperitoneal masses can be narrowed down to a certain extent on the basis of imaging characteristics, patterns of involvement, and demographics, there is still a considerable overlap of imaging findings for these masses, and histologic examination is often required for definitive diagnosis. Computed tomography (CT) and magnetic resonance (MR) imaging play an important role in characterization and in the assessment of the extent of the disease and involvement of adjacent and distant structures. Familiarity with the CT and MR imaging features of various retroperitoneal masses will facilitate accurate diagnosis and staging for aggressive lesions.

Purpose: To evaluate ability of radiomic (computer-extracted imaging) features to distinguish non-small cell lung cancer adenocarcinomas from granulomas at noncontrast CT. Materials and Methods: For this retrospective study, screening or standard diagnostic noncontrast CT images were collected for 290 patients (mean age, 68 years; range, 18–92 years; 125 men [mean age, 67 years; range, 18–90 years] and 165 women [mean age, 68 years; range, 33–92 years]) from two institutions between 2007 and 2013. Histopathologic analysis was available for one nodule per patient. Corresponding nodule of interest was identified on CT axial images by a radiologist with manually annotation. Nodule shape, wavelet (Gabor), and texture-based (Haralick and Laws energy) features were extracted from intra- and perinodular regions. Features were pruned to train machine learning classifiers with 145 patients. In a test set of 145 patients, classifier results were compared against a convolutional neural network (CNN) and diagnostic readings of two radiologists. Results: Support vector machine classifier with intranodular radiomic features achieved an area under the receiver operating characteristic curve (AUC) of 0.75 on the test set. Combining radiomics of intranodular with perinodular regions improved the AUC to 0.80. On the same test set, CNN resulted in an AUC of 0.76. Radiologist readers achieved AUCs of 0.61 and 0.60, respectively. Conclusion: Radiomic features from intranodular and perinodular regions of nodules can distinguish non-small cell lung cancer adenocarcinomas from benign granulomas at noncontrast CT. Summary Perinodular and intranodular radiomic features corresponding to texture and shape (radiomics) were evaluated to distinguish nonsmall cell lung cancer adenocarcinomas from benign granulomas at noncontrast CT.

The first clinical photon-counting detector CT system demonstrated superior spatial resolution relative to current CT systems and improved noise properties and multi-energy temporal resolution relative to similarly configured, energy-integrating detector CT. Key Results1. The high-resolution mode of the photon-counting detector (PCD) CT demonstrated 125-micron in-plane spatial resolution and 0.3 mm longitudinal resolution, the smallest reported to date for a clinical CT system.2. The PCD CT system provided 66-msec temporal resolution multi-energy imaging in dual-source mode.3. Noise reduction (up to 47%) or dose reduction (up to 30%) were achieved in study participants using the PCD CT system relative to a similar CT system equipped with conventional detectors.

Imaging plays an important role in the evaluation and management of acute pulmonary embolism (PE). Computed tomography (CT) pulmonary angiography (CTPA) is the current standard of care and provides accurate diagnosis with rapid turnaround time. CT also provides information on other potential causes of acute chest pain. With dual-energy CT, lung perfusion abnormalities can also be detected and quantified. Chest radiograph has limited utility, occasionally showing findings of PE or infarction, but is useful in evaluating other potential causes of chest pain. Ventilation-perfusion (VQ) scan demonstrates ventilation-perfusion mismatches in these patients, with several classification schemes, typically ranging from normal to high. Magnetic resonance imaging (MRI) also provides accurate diagnosis, but is available in only specialized centers and requires higher levels of expertise. Catheter pulmonary angiography is no longer used for diagnosis and is used only for interventional management. Echocardiography is used for risk stratification of these patients. In this article, we review the role of imaging in the evaluation of acute PE.