Faculty Bibliography

Mediastinal masses can present with symptoms, signs, and syndromes or incidentally. Selecting the appropriate diagnostic imaging study for mediastinal mass evaluation requires awareness of the strengths and weaknesses of the various imaging modalities with regard to tissue characterization, soft tissue contrast, and surveillance. This publication expounds on the differences between chest radiography, CT, PET/CT, ultrasound, and MRI in terms of their ability to decipher and surveil mediastinal masses. Making the optimal imaging choice can yield diagnostic specificity, avert unnecessary biopsy and surgery, guide the interventionist when necessary, and serve as a means of surveillance for probably benign, but indeterminate mediastinal masses. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.

Subsolid nodules are common on chest CT and may be either benign or malignant. Their varied features, and broad differential diagnoses present management challenges. While subsolid nodules often represent lung adenocarcinomas, other possibilities are common, and influence management. Practice guidelines exist for subsolid nodule management for both incidentally and screening-detected nodules, incorporating patient and nodule characteristics. This review will highlight similarities and differences amongst these algorithms, with the intent of providing a resource for comparison, and aid in choosing management options.

Chest CT angiography (CTA) is essential in the diagnosis of acute aortic syndromes. Chest CTA quality can be optimized with attention to technical parameters pertaining to noncontrast imaging, timing of contrast-enhanced imaging, contrast material volume, kilovolt potential, tube-current modulation, and decisions regarding electrocardiographic-gating and ultra-fast imaging, which may affect the accurate diagnosis of acute aortic syndromes. An understanding of methods to apply to address suboptimal image quality is useful, as the accurate identification of acute aortic syndromes is essential for appropriate patient management. Acute aortic syndromes have high morbidity and mortality, particularly when involving the ascending aorta, and include classic aortic dissection, penetrating atherosclerotic ulcer, and acute intramural hematoma. An understanding of the pathogenesis and distinguishing imaging features of acute aortic syndromes and aortic rupture and some less common manifestations is helpful when interpreting imaging examinations. Related entities, such as ulcerated plaque, ulcerlike projections, and intramural blood pools, and mimics, such as vasculitis and aortic thrombus, are important to recognize; knowledge of these is important to avoid interpretive pitfalls. In addition, an awareness of postsurgical aortic changes can be useful when interpreting CTA examinations when patient history is incomplete. The authors review technical considerations when performing CTA, discuss acute aortic syndromes, and highlight diagnostic challenges encountered when interpreting aortic CTA examinations. ^©RSNA, 2021.

PURPOSE/OBJECTIVE:For a successful bronchoscopic lung volume reduction coil treatment it is important to place the coils in the most emphysematous lobes. Therefore assessment of the lobe with greatest destruction is essential. Our aims were to investigate the level of agreement among expert reviewers of HRCT-scans in emphysema patients and the comparison with QCT (quantitative computed tomography) software. METHOD/METHODS:Five experienced CT-assessors, conducted a visual assessment of the baseline HRCT-scans of emphysema patients who participated in the RENEW bronchoscopic lung volume reduction coil study. On the same HRCT-scans, a QCT analysis was performed. RESULTS:In total 134 HRCT-scans were rated by all 5 experts. All 5 CT-assessors agreed on which was the most destructed lobe in 61 % of the left lungs (Æ™:0.459) and 60 % of the right lungs (Æ™:0.370). The consensus of the 5 assessors matched the QCT in the left lung for 77 % of the patients (Æ™:0.425) and in the right lung for 82 % (Æ™:0.524). CONCLUSIONS:Our results show that the interobserver agreement between five expert CT-assessors was only fair to moderate when evaluating the most destructed lobe. CT-assessor consensus improved matching with QCT determination of lobar destruction compared to individual assessor determinations. Because some CT-features are associated with treatment outcomes and important for optimal patient selection of bronchoscopic lung volume reduction treatment, we recommend including more than one CT-reviewer and supported by QCT measurements.

Pyrite FeS2 is an outstanding candidate for a low-cost, nontoxic, sustainable photovoltaic material, but efficient pyrite-based solar cells are yet to materialize. Recent studies of single crystals have shed much light on this by uncovering a p-type surface inversion layer on n-type (S-vacancy doped) crystals, and the resulting internal p-n junction. This leaky internal junction likely plays a key role in limiting efficiency in pyrite-based photovoltaic devices, also obscuring the true bulk semiconducting transport properties of pyrite crystals. Here, we demonstrate complete mitigation of the internal p-n junction in FeS2 crystals by fabricating metallic CoS2 contacts via a process that simultaneously diffuses Co (a shallow donor) into the crystal, the resulting heavy n doping yielding direct Ohmic contact to the interior. Low-temperature bulk transport studies of controllably Co- and S-vacancy doped semiconducting crystals then enable a host of previously inaccessible observations and measurements, including determination of donor activation energies (which are as low as 5 meV for Co), observation of an unexpected second activated transport regime, realization of electron mobility up to 2100cm2V-1s-1, elucidation of very different mobilities in Co- and S-vacancy-doped cases, and observation of an abrupt temperature-dependent crossover to bulk Efros-Shklovskii variable-range hopping, accompanied by an unusual form of nonlinear Hall effect. Aspects of the results are interpreted with the aid of first-principles electronic structure calculations on both Co- and S-vacancy-doped FeS2. This work thus demonstrates unequivocal mitigation of the internal p-n junction in pyrite single crystals, with important implications for both future fundamental studies and photovoltaic devices.

In lung cancer, enrichment of the lower airway microbiota with oral commensals commonly occurs and ex vivo models support that some of these bacteria can trigger host transcriptomic signatures associated with carcinogenesis. Here, we show that this lower airway dysbiotic signature was more prevalent in group IIIB-IV TNM stage lung cancer and is associated with poor prognosis, as shown by decreased survival among subjects with early stage disease (I-IIIA) and worse tumor progression as measured by RECIST scores among subjects with IIIB-IV stage disease. In addition, this lower airway microbiota signature was associated with upregulation of IL-17, PI3K, MAPK and ERK pathways in airway transcriptome, and we identified Veillonella parvula as the most abundant taxon driving this association. In a KP lung cancer model, lower airway dysbiosis with V. parvula led to decreased survival, increased tumor burden, IL-17 inflammatory phenotype and activation of checkpoint inhibitor markers.

The rapid spread of COVID-19 cases in recent months has strained hospital resources, making rapid and accurate triage of patients presenting to emergency departments a necessity. Machine learning techniques using clinical data such as chest X-rays have been used to predict which patients are most at risk of deterioration. We consider the task of predicting two types of patient deterioration based on chest X-rays: adverse event deterioration (i.e., transfer to the intensive care unit, intubation, or mortality) and increased oxygen requirements beyond 6 L per day. Due to the relative scarcity of COVID-19 patient data, existing solutions leverage supervised pretraining on related non-COVID images, but this is limited by the differences between the pretraining data and the target COVID-19 patient data. In this paper, we use self-supervised learning based on the momentum contrast (MoCo) method in the pretraining phase to learn more general image representations to use for downstream tasks. We present three results. The first is deterioration prediction from a single image, where our model achieves an area under receiver operating characteristic curve (AUC) of 0.742 for predicting an adverse event within 96 hours (compared to 0.703 with supervised pretraining) and an AUC of 0.765 for predicting oxygen requirements greater than 6 L a day at 24 hours (compared to 0.749 with supervised pretraining). We then propose a new transformer-based architecture that can process sequences of multiple images for prediction and show that this model can achieve an improved AUC of 0.786 for predicting an adverse event at 96 hours and an AUC of 0.848 for predicting mortalities at 96 hours. A small pilot clinical study suggested that the prediction accuracy of our model is comparable to that of experienced radiologists analyzing the same information.

INTRODUCTION: Remdesivir (RDV) is an antiviral agent with in-vitro activity against SARS-CoV-2 that has been used during the COVID-19 pandemic. Dosing strategies for pediatric and adolescent patients have primarily been extrapolated from adult dosing recommendations and, to date, there is a lack of pharmacokinetic (PK) data of RDV in this patient population.
METHOD(S): Electronic medical record review of patients receiving RDV with concurrent therapeutic drug monitoring (TDM). RDV and GS-441524 concentrations were determined by LC-MS/MS methodology.
RESULT(S): 3 patients (2 female:1 male) met inclusion criteria and contributed 74 samples for determination of RDV and the active GS-441524 metabolite. The median age was 16 yrs (IQR 15.5-16 yrs) with a median weight of 76.4 kg (IQR 74.9-94.3kg). Patient #1 received ECMO support for the duration of RDV therapy. Patients #1 and 2 received RDV for 10 days with levels obtained daily. Patient #3 received RDV for 5 days with levels obtained daily. For all patients, mean RDV exposures, range 272-893 ng/mL were below the mean exposures reported in the RDV investigators brochure, 2900-7800 ng/mL. Patient #1 received ECMO and RDV exposures did not appear impacted by ECMO when compared with patients #2 and #3 that did not receive ECMO. For all patients, mean GS-441524 exposures, range 109-258 ng/mL, were similar to the mean exposures reported in the RDV investigators brochure, range 69-184 ng/mL. Similarly, the GS-441524 exposure did not appear to be affected by ECMO. Patients #1 and #2 did not appear to have any observable adverse events as a result of receiving RDV. Patient #3 experienced and increase in ALT >5x ULN which resulted in having RDV discontinued. All 3 patients experienced clinical resolution.
CONCLUSION(S): These are the first PK data of RDV in critically ill adolescent patients. These preliminary data suggest using adult dosing recommendations in adolescent patients result in RDV exposures below mean values demonstrated in adults with similar exposures of GS-441524 which could be a result of rapid conversion of RDV to GS- 441524 with delayed elimination in the setting of critical illness. Additional PK data of RDV in the critically ill pediatric population is warranted

During the COVID-19 pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-driven approach for automatic prediction of deterioration risk using a deep neural network that learns from chest X-ray images, and a gradient boosting model that learns from routine clinical variables. Our AI prognosis system, trained using data from 3,661 patients, achieves an AUC of 0.786 (95% CI: 0.742-0.827) when predicting deterioration within 96 hours. The deep neural network extracts informative areas of chest X-ray images to assist clinicians in interpreting the predictions, and performs comparably to two radiologists in a reader study. In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at NYU Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time. In summary, our findings demonstrate the potential of the proposed system for assisting front-line physicians in the triage of COVID-19 patients.