Faculty Bibliography

PURPOSE/OBJECTIVE:To assess radiology representation, multimedia content, and multilingual content of United States lung cancer screening (LCS) program websites. MATERIALS AND METHODS/METHODS:We identified the websites of US LCS programs with the Google internet search engine using the search terms lung cancer screening, low-dose CT screening, and lung screening. We used a standardized checklist to assess and collect specific content, including information regarding LCS staff composition and references to radiologists and radiology. We also tabulated types and frequencies of included multimedia and multilingual content and patient narratives. RESULTS:We analyzed 257 unique websites. Of these, only 48% (124 of 257) referred to radiologists or radiology in text, images, or videos. Radiologists were featured in images or videos on only 14% (36 of 257) of websites. Radiologists were most frequently acknowledged for their roles in reading or interpreting imaging studies (35% [90 of 574]). Regarding multimedia content, only 36% (92 of 257) of websites had 1 image, 27% (70 of 257) included 2 or more images, and 26% (68 of 257) of websites included one or more videos. Only 3% (7 of 257) of websites included information in a language other than English. Patient narratives were found on only 15% (39 of 257) of websites. CONCLUSIONS:The field of Radiology is mentioned in text, images, or videos by less than half of LCS program websites. Most websites make only minimal use of multimedia content such as images, videos, and patient narratives. Few websites provide LCS information in languages other than English, potentially limiting accessibility to diverse populations.

To tune and test the generalizability of a deep learning-based model for assessment of COVID-19 lung disease severity on chest radiographs (CXRs) from different patient populations. A published convolutional Siamese neural network-based model previously trained on hospitalized patients with COVID-19 was tuned using 250 outpatient CXRs. This model produces a quantitative measure of COVID-19 lung disease severity (pulmonary x-ray severity (PXS) score). The model was evaluated on CXRs from 4 test sets, including 3 from the United States (patients hospitalized at an academic medical center (N = 154), patients hospitalized at a community hospital (N = 113), and outpatients (N = 108)) and 1 from Brazil (patients at an academic medical center emergency department (N = 303)). Radiologists from both countries independently assigned reference standard CXR severity scores, which were correlated with the PXS scores as a measure of model performance (Pearson R). The Uniform Manifold Approximation and Projection (UMAP) technique was used to visualize the neural network results. Tuning the deep learning model with outpatient data showed high model performance in 2 United States hospitalized patient datasets (R = 0.88 and R = 0.90, compared to baseline R = 0.86). Model performance was similar, though slightly lower, when tested on the United States outpatient and Brazil emergency department datasets (R = 0.86 and R = 0.85, respectively). UMAP showed that the model learned disease severity information that generalized across test sets. A deep learning model that extracts a COVID-19 severity score on CXRs showed generalizable performance across multiple populations from 2 continents, including outpatients and hospitalized patients.

Reports are rising of patients with unilateral axillary lymphadenopathy, visible on diverse imaging examinations, after recent coronavirus disease 2019 vaccination. With less than 10% of the US population fully vaccinated, we can prepare now for informed care of patients imaged after recent vaccination. The authors recommend documenting vaccination information (date[s] of vaccination[s], injection site [left or right, arm or thigh], type of vaccine) on intake forms and having this information available to the radiologist at the time of examination interpretation. These recommendations are based on three key factors: the timing and location of the vaccine injection, clinical context, and imaging findings. The authors report isolated unilateral axillary lymphadenopathy (i.e., no imaging findings outside of visible lymphadenopathy), which is ipsilateral to recent (prior 6 weeks) vaccination, as benign with no further imaging indicated. Clinical management is recommended, with ultrasound if clinical concern persists 6 weeks after the final vaccination dose. In the clinical setting to stage a recent cancer diagnosis or assess response to therapy, the authors encourage prompt recommended imaging and vaccination (possibly in the thigh or contralateral arm according to the location of the known cancer). Management in this clinical context of a current cancer diagnosis is tailored to the specific case, ideally with consultation between the oncology treatment team and the radiologist. The aim of these recommendations is to (1) reduce patient anxiety, provider burden, and costs of unnecessary evaluation of enlarged nodes in the setting of recent vaccination and (2) avoid further delays in vaccinations and recommended imaging for best patient care during the pandemic.

BACKGROUND AND PURPOSE:Severe respiratory distress in patients with COVID-19 has been associated with higher rate of neurologic manifestations. Our aim was to investigate whether the severity of chest imaging findings among patients with coronavirus disease 2019 (COVID-19) correlates with the risk of acute neuroimaging findings. MATERIALS AND METHODS:This retrospective study included all patients with COVID-19 who received care at our hospital between March 3, 2020, and May 6, 2020, and underwent chest imaging within 10 days of neuroimaging. Chest radiographs were assessed using a previously validated automated neural network algorithm for COVID-19 (Pulmonary X-ray Severity score). Chest CTs were graded using a Chest CT Severity scoring system based on involvement of each lobe. Associations between chest imaging severity scores and acute neuroimaging findings were assessed using multivariable logistic regression. RESULTS:= .041). The pulmonary x-ray severity score was a significant predictor of acute neuroimaging findings in patients with COVID-19. CONCLUSIONS:Patients with COVID-19 and acute neuroimaging findings had more severe findings on chest imaging on both radiographs and CT compared with patients with COVID-19 without acute neuroimaging findings. The severity of findings on chest radiography was a strong predictor of acute neuroimaging findings in patients with COVID-19.

KRAS G12C mutations are important oncogenic mutations that confer sensitivity to direct G12C inhibitors. We retrospectively identified patients with KRAS+ NSCLC from 2015 to 2019 and assessed the imaging features of the primary tumor and the distribution of metastases of G12C NSCLC compared to those of non-G12C KRAS NSCLC and NSCLC driven by oncogenic fusion events (RET, ALK, ROS1) and EGFR mutations at the time of initial diagnosis. Two hundred fifteen patients with KRAS+ NSCLC (G12C: 83; non-G12C: 132) were included. On single variate analysis, the G12C group was more likely than the non-G12C KRAS group to have cavitation (13% vs. 5%, p = 0.04) and lung metastasis (38% vs. 21%; p = 0.043). Compared to the fusion rearrangement group, the G12C group had a lower frequency of pleural metastasis (21% vs. 41%, p = 0.01) and lymphangitic carcinomatosis (4% vs. 39%, p = 0.0001) and a higher frequency of brain metastasis (42% vs. 22%, p = 0.005). Compared to the EGFR+ group, the G12C group had a lower frequency of lung metastasis (38% vs. 67%, p = 0.0008) and a higher frequency of distant nodal metastasis (10% vs. 2%, p = 0.02). KRAS G12C NSCLC may have distinct primary tumor imaging features and patterns of metastasis when compared to those of NSCLC driven by other genetic alterations.

RATIONALE AND OBJECTIVES:Radiographic findings of COVID-19 pneumonia can be used for patient risk stratification; however, radiologist reporting of disease severity is inconsistent on chest radiographs (CXRs). We aimed to see if an artificial intelligence (AI) system could help improve radiologist interrater agreement. MATERIALS AND METHODS:We performed a retrospective multi-radiologist user study to evaluate the impact of an AI system, the PXS score model, on the grading of categorical COVID-19 lung disease severity on 154 chest radiographs into four ordinal grades (normal/minimal, mild, moderate, and severe). Four radiologists (two thoracic and two emergency radiologists) independently interpreted 154 CXRs from 154 unique patients with COVID-19 hospitalized at a large academic center, before and after using the AI system (median washout time interval was 16 days). Three different thoracic radiologists assessed the same 154 CXRs using an updated version of the AI system trained on more imaging data. Radiologist interrater agreement was evaluated using Cohen and Fleiss kappa where appropriate. The lung disease severity categories were associated with clinical outcomes using a previously published outcomes dataset using Fisher's exact test and Chi-square test for trend. RESULTS:Use of the AI system improved radiologist interrater agreement (Fleiss κ = 0.40 to 0.66, before and after use of the system). The Fleiss κ for three radiologists using the updated AI system was 0.74. Severity categories were significantly associated with subsequent intubation or death within 3 days. CONCLUSION:An AI system used at the time of CXR study interpretation can improve the interrater agreement of radiologists.