Vaping-associated lung injury via the use of electronic nicotine delivery systems (ENDS) is currently being evaluated as a potential source of pulmonary injury with uncertain etiology as the use of tetrahydrocannabinol (THC) is increasing throughout the USA. ENDS are marketed to be unlike traditional cigarette smoking in that they are purported to contain only propylene glycol, vegetable glycerine, nicotine, and flavorants compared with the > 60 carcinogenic ingredients in cigarettes. The New England Journal of Medicine (NEJM) currently reports four imaging patterns correlated with vaping-attributed pathology including acute eosinophilic pneumonia, diffuse alveolar damage, organizing pneumonia, and lipoid pneumonia. The incidence and extent of lung disease in otherwise young healthy patients with a history of vaping has not however been definitively recognized within the field of radiology. We present a case of vaping-associated acute respiratory distress syndrome (ARDS) in a young patient with no additional past medical history. The immediate radiologic recognition of vaping as a risk factor for ARDS in the emergency setting is pivotal so that appropriate medical management and respiratory support can be initiated without delay.

In this study, we aimed to predict mechanical ventilation requirement and mortality using computational modeling of chest radiographs (CXRs) for coronavirus disease 2019 (COVID-19) patients. This two-center, retrospective study analyzed 530 deidentified CXRs from 515 COVID-19 patients treated at Stony Brook University Hospital and Newark Beth Israel Medical Center between March and August 2020. Linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and random forest (RF) machine learning classifiers to predict mechanical ventilation requirement and mortality were trained and evaluated using radiomic features extracted from patients’ CXRs. Deep learning (DL) approaches were also explored for the clinical outcome prediction task and a novel radiomic embedding framework was introduced. All results are compared against radiologist grading of CXRs (zone-wise expert severity scores). Radiomic classification models had mean area under the receiver operating characteristic curve (mAUCs) of 0.78 ± 0.05 (sensitivity = 0.72 ± 0.07, specificity = 0.72 ± 0.06) and 0.78 ± 0.06 (sensitivity = 0.70 ± 0.09, specificity = 0.73 ± 0.09), compared with expert scores mAUCs of 0.75 ± 0.02 (sensitivity = 0.67 ± 0.08, specificity = 0.69 ± 0.07) and 0.79 ± 0.05 (sensitivity = 0.69 ± 0.08, specificity = 0.76 ± 0.08) for mechanical ventilation requirement and mortality prediction, respectively. Classifiers using both expert severity scores and radiomic features for mechanical ventilation (mAUC = 0.79 ± 0.04, sensitivity = 0.71 ± 0.06, specificity = 0.71 ± 0.08) and mortality (mAUC = 0.83 ± 0.04, sensitivity = 0.79 ± 0.07, specificity = 0.74 ± 0.09) demonstrated improvement over either artificial intelligence or radiologist interpretation alone. Our results also suggest instances in which the inclusion of radiomic features in DL improves model predictions over DL alone. The models proposed in this study and the prognostic information they provide might aid physician decision making and efficient resource allocation during the COVID-19 pandemic.

Patients receiving mechanical ventilation for coronavirus disease 2019 (COVID-19) related, moderate-to-severe acute respiratory distress syndrome (CARDS) have mortality rates between 76–98%. The objective of this retrospective cohort study was to identify differences in prone ventilation effects on oxygenation, pulmonary infiltrates (as observed on chest X-ray (CXR)), and systemic inflammation in CARDS patients by survivorship and to identify baseline characteristics associated with survival after prone ventilation. The study cohort included 23 patients with moderate-to-severe CARDS who received prone ventilation for ≥16 h/day and was segmented by living status: living (n = 6) and deceased (n = 17). Immediately after prone ventilation, PaO2/FiO2 improved by 108% (p < 0.03) for the living and 150% (p < 3 × 10−4) for the deceased. However, the 48 h change in lung infiltrate severity in gravity-dependent lung zones was significantly better for the living than for the deceased (p < 0.02). In CXRs of the lower lungs before prone ventilation, we observed 5 patients with confluent infiltrates bilaterally, 12 patients with ground-glass opacities (GGOs) bilaterally, and 6 patients with mixed infiltrate patterns; 80% of patients with confluent infiltrates were alive vs. 8% of patients with GGOs. In conclusion, our small study indicates that CXRs may offer clinical utility in selecting patients with moderate-to-severe CARDS who will benefit from prone ventilation. Additionally, our study suggests that lung infiltrate severity may be a better indicator of patient disposition after prone ventilation than PaO2/FiO2.

Arachnoid cysts are benign masses that represent a relatively small percentage of intracranial lesions. Spontaneous rupture of an arachnoid cyst resulting in a subdural hygroma is a very rare event. We report a case of a pediatric patient with a history of an arachnoid cyst and chronic headaches presenting with bilateral papilledema, worsening headaches, and no history of head trauma. Magnetic resonance imaging of the brain revealed an extra-axial cystic lesion in the right middle cranial fossa, similar to an arachnoid cyst seen on previous imaging. A new right subdural collection similar to the cerebral spinal fluid signal causing mass effect on brain parenchyma was determined to represent a subdural hygroma. Craniotomy was performed to evacuate the subdural hygroma as well as cyst fenestration. We report this case to emphasize the importance of considering spontaneous rupture of an arachnoid cyst as a differential diagnosis despite absence of head trauma.

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