A wide variety of neoplastic and nonneoplastic conditions occur in the mediastinum. Imaging plays a central role in the evaluation of mediastinal pathologies and their mimics. Localization of a mediastinal lesion to a compartment and characterization of morphology, density/signal intensity, enhancement, and mass effect on neighboring structures can help narrow the differentials. The International Thymic Malignancy Interest Group (ITMIG) established a cross-sectional imaging-derived and anatomy-based classification system for mediastinal compartments, comprising the prevascular (anterior), visceral (middle), and paravertebral (posterior) compartments. Cross-sectional imaging is integral in the evaluation of mediastinal lesions. Computed tomography (CT) and magnetic resonance imaging (MRI) are useful to characterize mediastinal lesions detected on radiography. Advantages of CT include its widespread availability, fast acquisition time, relatively low cost, and ability to detect calcium. Advantages of MRI include the lack of radiation exposure, superior soft tissue contrast resolution to detect invasion of the mass across tissue planes, including the chest wall and diaphragm, involvement of neurovascular structures, and the potential for dynamic sequences during free-breathing or cinematic cardiac gating to assess motion of the mass relative to adjacent structures. MRI is superior to CT in the differentiation of cystic from solid lesions and in the detection of fat to differentiate thymic hyperplasia from thymic malignancy.

OBJECTIVES:Cancer patients have worse outcomes from the COVID-19 infection and greater need for ventilator support and elevated mortality rates than the general population. However, previous artificial intelligence (AI) studies focused on patients without cancer to develop diagnosis and severity prediction models. Little is known about how the AI models perform in cancer patients. In this study, we aim to develop a computational framework for COVID-19 diagnosis and severity prediction particularly in a cancer population and further compare it head-to-head to a general population. METHODS:We have enrolled multi-center international cohorts with 531 CT scans from 502 general patients and 420 CT scans from 414 cancer patients. In particular, the habitat imaging pipeline was developed to quantify the complex infection patterns by partitioning the whole lung regions into phenotypically different subregions. Subsequently, various machine learning models nested with feature selection were built for COVID-19 detection and severity prediction. RESULTS:These models showed almost perfect performance in COVID-19 infection diagnosis and predicting its severity during cross validation. Our analysis revealed that models built separately on the cancer population performed significantly better than those built on the general population and locked to test on the cancer population. This may be because of the significant difference among the habitat features across the two different cohorts. CONCLUSIONS:Taken together, our habitat imaging analysis as a proof-of-concept study has highlighted the unique radiologic features of cancer patients and demonstrated effectiveness of CT-based machine learning model in informing COVID-19 management in the cancer population.

Metastatic disease is the most common chest malignancy, and the chest acquires more metastases than any system. Typical and atypical computed tomography imaging manifestations of pulmonary, pleural, and cardiac metastases are addressed.

In imaging of the mediastinum, advances in computed tomography (CT), and magnetic resonance imaging (MRI) technology enable improved characterization of mediastinal masses. Knowledge of the boundaries of the mediastinal compartments is key to accurate localization. Awareness of distinguishing imaging characteristics allows radiologists to suggest a specific diagnosis or narrow the differential. In certain situations, MRI adds value to further characterize mediastinal lesions.

Respiratory motion during the CT and PET parts of a PET/CT scan leads to imperfect alignment of anatomic features seen by the 2 modalities. In this work, we concentrate on the effects of motion during CT. We propose a novel approach for improving the alignment. Methods: Respiratory waveform data were gathered during the CT and PET parts of 28 PET/CT scans of cancer patients with 40 lesions up to 3 cm in size in the lung or upper abdomen. PET list-mode data were reconstructed by 3 reconstruction methods: PET/static (the standard method with no motion correction); PET/ex (a method that calculates a range of expiratory amplitudes from the lowest one to the highest one); and PET/matched (a novel method that uses both waveforms). The 3 methods were compared. The distance between tumor positions in PET and CT were characterized in visual interpretation by physicians as well as quantitatively. Tumor SUVs (SUVmax and SUVpeak) were determined relative to SUV based on the static method. Image noise was evaluated in the liver and compared with PET/static. Results: In visual interpretation, the rate of good alignment was 13 of 21, 13 of 23, and 18 of 21 for the PET/static, PET/ex, and PET/matched methods, respectively, and the mean PET/CT distances were 3.5, 5.1, and 2.8 mm. In visual comparison with PET/ex, the rate of good alignment was increased in 1 of 10 and 7 of 10 cases for PET/static and PET/matched, respectively. SUVmax was on average 21% higher than PET/static when either PET/ex or PET/matched was used. SUVpeak was 12% higher. Image noise in the liver was 15% higher than PET/static for the PET/ex method, and 40% higher for PET/matched; that is, noise was much lower than in gated PET. Conclusion: Acquiring respiratory waveforms both in PET (as in the current state of the art) and in CT (an unusual key step in this approach) has the potential to improve the alignment of PET and CT images. A proposed method for using this information was tested. Improved alignment was demonstrated.

Applications of positron emission tomography/computed tomography (PET/CT) in the thorax include the evaluation of solitary pulmonary nodules, staging and restaging of oncologic patients, assessment of therapeutic response, and detection of residual or recurrent disease. Accurate interpretation of PET/CT requires knowledge of the physiological distribution of [18F]-fluoro-2-deoxy-D-glucose, as well as artifacts and quantitative errors due to the use of CT for attenuation correction of the PET scan. Potential pitfalls include malignancies that are PET negative and benign conditions that are PET positive. Awareness of these artifacts and potential pitfalls is important in preventing misinterpretation that can alter patient management.

Purpose: Precision radiation therapy such as stereotactic body radiation therapy and limited resection are being used more frequently to treat intrathoracic malignancies. Effective local control requires precise radiation target delineation or complete resection. Lung biopsy tracts (LBT) on computed tomography (CT) scans after the use of tract sealants can mimic malignant tract seeding (MTS) and it is unclear whether these LBTs should be included in the calculated tumor volume or resected. This study evaluates the incidence, appearance, evolution, and malignant seeding of LBTs. Methods and materials: A total of 406 lung biopsies were performed in oncology patients using a tract sealant over 19 months. Of these patients, 326 had follow-up CT scans and were included in the study group. Four thoracic radiologists retrospectively analyzed the imaging, and a pathologist examined 10 resected LBTs. Results: A total of 234 of 326 biopsies (72%, including primary lung cancer [n = 98]; metastases [n = 81]; benign [n = 50]; and nondiagnostic [n = 5]) showed an LBT on CT. LBTs were identified on imaging 0 to 3 months after biopsy. LBTs were typically straight or serpiginous with a thickness of 2 to 5 mm. Most LBTs were unchanged (92%) or decreased (6.3%) over time. An increase in LBT thickness/nodularity that was suspicious for MTS occurred in 4 of 234 biopsies (1.7%). MTS only occurred after biopsy of metastases from extrathoracic malignancies, and none occurred in patients with lung cancer. Conclusions: LBTs are common on CT after lung biopsy using a tract sealant. MTS is uncommon and only occurred in patients with extrathoracic malignancies. No MTS was found in patients with primary lung cancer. Accordingly, potential alteration in planned therapy should be considered only in patients with LBTs and extrathoracic malignancies being considered for stereotactic body radiation therapy or wedge resection. (C) 2017 The Author(s). Published by Elsevier Inc. on behalf of the American Society for Radiation Oncology.

The updated eighth edition of the tumor, node, metastasis (TNM) classification for lung cancer includes revisions to T and M descriptors. In terms of the M descriptor, the classification of intrathoracic metastatic disease as M1a is unchanged from TNM-7. Extrathoracic metastatic disease, which was classified as M1b in TNM-7, is now subdivided into M1b (single metastasis, single organ) and M1c (multiple metastases in one or multiple organs) descriptors. In this article, the rationale for changes in the M descriptors, the utility of preoperative staging with PET/computed tomography, and the treatment options available for patients with oligometastatic disease are discussed.

Pulmonary and pleural metastases are routinely identified on thoracic computed tomography. Pulmonary metastases are the most common pulmonary neoplasms and commonly originate from primary malignancies of the lung, breast, colon, pancreas, stomach, skin (ie, melanoma), head and neck, and kidney. Metastatic disease to the lungs may occur via 3 routes of spread: hematogenous, lymphatic, and endobronchial. Pleural metastases most commonly originate from primary malignancies of the lung and breast. Mechanisms of pleural metastatic involvement include hematogenous spread, direct invasion from a neighboring tumor, and retrograde lymphatic spread from the mediastinum. Awareness of the spectrum of appearances of metatastic disease in the chest is important in avoiding misinterpretation.

INTRODUCTION:Treatment of early esophageal cancer depends on the extent of the primary tumor and presence of regional lymph node metastasis.(RNM). Short axis diameter>10mm is typically used to detect RNM. However, clinical determination of RNM is inaccurate and can result in inappropriate treatment. Purpose of this study is to evaluate the accuracy of a single linear measurement (short axis>10mm) of regional nodes on CT in predicting nodal metastasis, in patients with early esophageal cancer and whether using a mean diameter value (short axis+long axis/2) as well as nodal shape improves cN designation.METHODS:CTs of 49 patients with cT1 adenocarcinoma treated with surgical resection alone were reviewed retrospectively. Regional nodes were considered positive for malignancy when round or ovoid and mean size>5mm adjacent to the primary tumor and>7mm when not adjacent. Results were compared with pN status after esophagectomy.RESULTS:18/49 patients had pN+ at resection. Using a single short axis diameter>10mm on CT, nodal metastasis (cN) was positive in 7/49. Only 1 of these patients was pN+ at resection (sensitivity 5%, specificity 80%, accuracy 53%). Using mean size and morphologic criteria, cN was positive in 28/49. 11 of these patients were pN+ at resection (sensitivity 61%, specificity 45%, accuracy 51%). EUS with limited FNA of regional nodes resulted in 16/49 patients with pN+ being inappropriately designated as cN0.CONCLUSIONS:Evaluation of size, shape and location of regional lymph nodes on CT improves the sensitivity of cN determination compared with a short axis measurement alone in patients with cT1 esophageal cancer, although clinical utility is limited.

Tract sealants are being used more frequently to reduce pneumothoraces and chest tube placement in patients undergoing lung biopsy. Use of a sealant plug can produce visible biopsy tracts on follow-up imaging and can mimic the appearance of malignant tract seeding. The purpose of our study was to characterize these tracts and determine the likelihood of malignant seeding to inform further management including localized radiation therapy and/or surgical planning. Over a 15 month period 407 lung biopsies were performed in patients with known or suspected thoracic and extrathoracic malignancies using a BioSentry Tract Sealant System; 321 cases had follow up CT studies. 4 chest radiologists retrospectively analyzed subsequent imaging to determine the incidence, appearance, temporal relationship and evolution of biopsy tracts. Tracts that decreased or did not change on follow-up were considered benign. 10 surgically resected cases were retrospectively examined by a pathologist for malignant tract seeding. 321 cases were analyzed. 237 (74%) had a visible biopsy tract on CT (95%CI 0.69, 0.78) (primary lung cancer n=90, metastases n=81, benign nodule n=66). All tracts were identified on 1st follow-up imaging at 1-3 months post-biopsy. Tracts were typically serpiginous and smooth or lobulated with a thickness of 2-5 mm. 218/237 (92%) tracts were unchanged over time (mean follow up, 12 months). 15/237 (6.3%) decreased in thickness. Unchanged or decreasing tracts were considered negative for malignant seeding. Increase in tract thickness or nodularity occurred in 4/237 (1.8%), suspicious for malignant tract seeding. 0/90 (0%) biopsy tracts in primary lung cancer showed progressive increase. 4/81 (4.9%) tracts in patients with metastases showed increase (mean, 99 days post-biopsy). 10 resected nodules (5 primary NSCLCs, 5 metastases) had no malignant tract seeding at histology. An observable biopsy tract on CT is common after lung biopsy using the BioSentryTM device. Tracts from biopsy of primary lung cancers using the BioSentry device had no malignant seeding and they should have no impact on surgical resection or localized radiation therapy. In the study population, patients who underwent lung biopsy for metastasis had a higher than expected rate of malignant seeding manifested by increased track thickness over time, requiring further investigation.

Conventional proton beam range verification using positron emission tomography (PET) relies on tissue activation alone and therefore requires particle therapy PET whose installation can represent a large financial burden for many centers. Previously, we showed the feasibility of developing patient implantable markers using high proton cross-section materials ((18)O, Cu, and (68)Zn) for in vivo proton range verification using conventional PET scanners. In this technical note, we characterize those materials to test their usability in more clinically relevant conditions. Two phantoms made of low-density balsa wood (~0.1 g cm(-3)) and beef (~1.0 g cm(-3)) were embedded with Cu or (68)Zn foils of several volumes (10-50 mm(3)). The metal foils were positioned at several depths in the dose fall-off region, which had been determined from our previous study. The phantoms were then irradiated with different proton doses (1-5 Gy). After irradiation, the phantoms with the embedded foils were moved to a diagnostic PET scanner and imaged. The acquired data were reconstructed with 20-40 min of scan time using various delay times (30-150 min) to determine the maximum contrast-to-noise ratio. The resultant PET/computed tomography (CT) fusion images of the activated foils were then examined and the foils' PET signal strength/visibility was scored on a 5 point scale by 13 radiologists experienced in nuclear medicine. For both phantoms, the visibility of activated foils increased in proportion to the foil volume, dose, and PET scan time. A linear model was constructed with visibility scores as the response variable and all other factors (marker material, phantom material, dose, and PET scan time) as covariates. Using the linear model, volumes of foils that provided adequate visibility (score 3) were determined for each dose and PET scan time. The foil volumes that were determined will be used as a guideline in developing practical implantable markers.

Eosinophilic lung diseases encompass a broad range of conditions wherein patients present with pulmonary opacities and eosinophilia of the serum, pulmonary tissue, or bronchoalveolar lavage fluid. Many of these entities can be idiopathic or are secondary to parasitic infection, exposure to drugs, toxins, or radiation. These diseases exhibit a wide range of imaging findings, including consolidation, ground-glass opacities, nodules, and masses. Diagnoses often require bronchoalveolar lavage and/or biopsy to confirm respiratory eosinophilia and to exclude other entities, such as infection or malignancy. Treatment entails administration of corticosteroids, removal of inciting agents, and treatment of underlying infection.

Background Contrast enhanced computed tomography (CT) is currently the imaging modality of choice for distinguishing thymic epithelial tumors (TETs) from other anterior mediastinal masses, characterizing the primary tumor and staging disease. Currently the role of magnetic resonance imaging (MRI) in imaging TETs is limited to patients who cannot receive iodinated contrast material. The study’s main objective is to assess if chest MRI is as accurate as CT in staging TETs. The second objective is to see if newer shorter MRI sequences can replace conventional MRI sequences without loss of accuracy, leading to a short MRI study.

Treating heart masses demands a well-planned diagnostic strategy. As with any disease in medicine, the confirmation of a diagnosis leads to a tailored, more efficacious treatment. We present the case of a 72-year-old man presenting with a right atrial mass on echocardiography and diagnosis of plasmacytoma after transvenous endomyocardial biopsy.