Faculty Bibliography

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.

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.

PURPOSE/OBJECTIVE:Tissue textures have been recognized as biomarkers for various clinical tasks. In computed tomography (CT) image reconstruction, it is important but challenging to preserve the texture when lowering x-ray exposure from full- toward low-/ultra-low dose level. Therefore, this paper aims to explore the texture-dose relationship within one tissue-specific pre-log Bayesian CT reconstruction algorithm. METHODS:To enhance the texture in ultra-low dose CT (ULdCT) reconstruction, this paper presents a Bayesian type algorithm. A shifted Poisson model is adapted to describe the statistical properties of pre-log data, and a tissue-specific Markov random field prior (MRFt) is used to incorporate tissue texture from previous full-dose CT, thus called SP-MRFt algorithm. Utilizing the SP-MRFt algorithm, we investigated tissue texture degradation as a function of x-ray dose levels from full dose (100 mAs/120 kVp) to ultralow dose (1 mAs/120 kVp) by using quantitative texture-based evaluation metrics. RESULTS:Experimental results show the SP-MRFt algorithm outperforms conventional filtered back projection (FBP) and post-log domain penalized weighted least square MRFt (PWLS-MRFt) in terms of noise suppression and texture preservation. Comparable results are also obtained with shifted Poisson model with 7 × 7 Huber MRF weights (SP-Huber7). The investigation on texture-dose relationship shows that the quantified texture measures drop monotonically as dose level decreases, and interestingly a turning point is observed on the texture-dose response curve. CONCLUSIONS:This important observation implies that there exists a minimum dose level, at which a given CT scanner (hardware configuration and image reconstruction software) can achieve without compromising clinical tasks. Moreover, the experiment results show that the variance of electronic noise has higher impact than the mean to the texture-dose relationship.

Background/UNASSIGNED:Computed tomographic (CT) features have demonstrated their value in classifying and assessing pulmonary nodules. Additionally, recent studies have shown the presence of keratin 17 (K17) in lung cancer is associated with increased mortality compared to patients with low/no K17 expression. The purpose of this study is to determine if there are CT imaging features that correlate with overexpression of K17 in patients with lung cancer. Methods/UNASSIGNED:This retrospective cohort study was approved by an Institutional Review Board. Lung cancer in 67 consecutive patients, who consented to have their lung cancer tissue stored in a tissue bank, were revaluated by immunohistochemical staining for the presence or absence of K17. Pre-operative imaging studies were collected on all patients. Two blinded independent radiologists evaluated multiple imaging features for each lung cancer. Results/UNASSIGNED:The overexpression of K17 was documented in 38.8% (26/67) of all lung cancers included in this cohort. Of the CT features recorded, the presence of the CT feature of lobulated borders was positively associated with over expression of K17 (P=0.02). No other imaging feature was associated with the presence or absence of K17. Conclusions/UNASSIGNED:The presence of a lobulated border, suggesting differential growth pattern of the lung cancer appears to be associated with the expression of K17.

Purpose/UNASSIGNED:To evaluate pulmonary embolism (PE) prevalence at CT pulmonary angiography in patients testing positive for coronavirus disease 2019 (COVID-19) and factors associated with PE severity. Materials and Methods/UNASSIGNED:value < .05 was considered significant. Results/UNASSIGNED:< .001). One additional patient with negative results at CT pulmonary angiography had deep venous thrombosis, thus resulting in 38.7% with PE or deep venous thrombosis, despite 40% receiving prophylactic anticoagulation. Other factors did not demonstrate significant PE association. Conclusion/UNASSIGNED:© RSNA, 2020.