Surgical Resection of Benign Nodules in Lung Cancer Screening: Incidence and Features.

Introduction: Interventions and surgical procedures are common for nonmalignant lung lesions detected on lung cancer screening (LCS). Inadvertent surgical resection of benign nodules with a clinical suspicion of lung cancer can occur, can be associated with complications, and adds to the cost of screening. The objective of this study is to assess the characteristics of surgically resected benign nodules detected on LCS computed tomography which were presumed to be lung cancers. Methods: This retrospective study included 4798 patients who underwent LCS between June 2014 and January 2021. The benign lung nodules, surgically resected with a presumed cancer diagnosis, were identified from the LCS registry. Patient demographics, imaging characteristics, and pathologic diagnoses of benign nodules were analyzed. Results: Of the 4798 patients who underwent LCS, 148 (3.1%) underwent surgical resection of a lung nodule, and of those who had a resection, 19 of 148 (12.8%) had a benign diagnosis (median age = 64 y, range: 56-77 y; F = 12 of 19, 63.2%; M = seven of 19, 36.8%). The median nodule size was 10 mm (range: 6-31 mm). Most nodules were solid (15 of 19, 78.9%), located in the upper lobes (11 of 19; 57.9%), and were peripheral (17 of 19, 89.5%). Most nodules (13 of 17; 76.5%) had interval growth, and four of 17 (23.5%) had increased fluorodeoxyglucose uptake. Of the 19 patients, 17 (89.5%) underwent sublobar resection (16 wedge resection and one segmentectomy), whereas two central nodules (10.5%) had lobectomies. Pathologies identified included focal areas of fibrosis or scarring (n = 8), necrotizing granulomatous inflammation (n = 3), other nonspecific inflammatory focus (n = 3), benign tumors (n = 3), reactive lymphoid hyperplasia (n = 1), and organizing pneumonia (n = 1). Conclusions: Surgical resections of benign nodules that were presumed malignant are infrequent and may be unavoidable given overlapping imaging features of benign and malignant nodules. Knowledge of benign pathologies that can mimic malignancy may help reduce the incidence of unnecessary surgeries.

Handheld Lung Ultrasound to Detect COVID-19 Pneumonia in Inpatients: A Prospective Cohort Study.

Background: Chest imaging, including chest X-ray (CXR) and computed tomography (CT), can be a helpful adjunct to nucleic acid test (NAT) in the diagnosis and management of Coronavirus Disease 2019 (COVID-19). Lung point of care ultrasound (POCUS), particularly with handheld devices, is an imaging alternative that is rapid, highly portable, and more accessible in low-resource settings. A standardized POCUS scanning protocol has been proposed to assess the severity of COVID-19 pneumonia, but it has not been sufficiently validated to assess diagnostic accuracy for COVID-19 pneumonia. Purpose: To assess the diagnostic performance of a standardized lung POCUS protocol using a handheld POCUS device to detect patients with either a positive NAT or a COVID-19-typical pattern on CT scan. Methods: Adult inpatients with confirmed or suspected COVID-19 and a recent CT were recruited from April to July 2020. Twelve lung zones were scanned with a handheld POCUS machine. Images were reviewed independently by blinded experts and scored according to the proposed protocol. Patients were divided into low, intermediate, and high suspicion based on their POCUS score. Results: Of 79 subjects, 26.6% had a positive NAT and 31.6% had a typical CT pattern. The receiver operator curve for POCUS had an area under the curve (AUC) of 0.787 for positive NAT and 0.820 for a typical CT. Using a two-point cutoff system, POCUS had a sensitivity of 0.90 and 1.00 compared to NAT and typical CT pattern, respectively, at the lower cutoff; it had a specificity of 0.90 and 0.89 compared to NAT and typical CT pattern at the higher cutoff, respectively. Conclusions: The proposed lung POCUS protocol with a handheld device showed reasonable diagnostic performance to detect inpatients with a positive NAT or typical CT pattern for COVID-19. Particularly in low-resource settings, POCUS with handheld devices may serve as a helpful adjunct for persons under investigation for COVID-19 pneumonia.

Conquering Educational Mountains: Maintaining a Radiology Clinical Education Track.

Establishing a clinical education track as part of a radiology residency is essential in shaping future radiology educators. Many obstacles will be encountered while starting, maintaining, and improving these educational pathways. Hurdles may include recruiting suitable residents for the track, recruiting and supporting faculty advisors, sustaining long-term resident engagement, counteracting educational exclusivity, and providing adequate time and financial support. Although every program and institution may face individualized "mountains" to overcome, they are not insurmountable. The goal of this review is to address different conflicts we have encountered while maintaining the clinical education tract at our institution and to provide tips for overcoming them.

Abdominal Imaging Findings on Computed Tomography as a Tool for COVID-19 Mortality Risk Assessment: Comparison With Chest Radiograph Severity Scores.

OBJECTIVE: To quantify the association between computed tomography abdomen and pelvis with contrast (CTAP) findings and chest radiograph (CXR) severity score, and the incremental effect of incorporating CTAP findings into predictive models of COVID-19 mortality. METHODS: This retrospective study was performed at a large quaternary care medical center. All adult patients who presented to our institution between March and June 2020 with the diagnosis of COVID-19 and had a CXR up to 48 hours before a CTAP were included. Primary outcomes were the severity of lung disease before CTAP and mortality within 14 and 30 days. Logistic regression models were constructed to quantify the association between CXR score and CTAP findings. Penalized logistic regression models and random forests were constructed to identify key predictors (demographics, CTAP findings, and CXR score) of mortality. The discriminatory performance of these models, with and without CTAP findings, was summarized using area under the characteristic (AUC) curves. RESULTS: One hundred ninety-five patients (median age, 63 years; 119 men) were included. The odds of having CTAP findings was 3.89 times greater when a CXR score was classified as severe compared with mild (P = 0.002). When CTAP findings were included in the feature set, the AUCs for 14-day mortality were 0.67 (penalized logistic regression) and 0.71 (random forests). Similar values for 30-day mortality were 0.76 and 0.75. When CTAP findings were omitted, all AUC values were attenuated. CONCLUSIONS: The CTAP findings were associated with more severe CXR score and may serve as predictors of COVID-19 mortality.

Multi-population generalizability of a deep learning-based chest radiograph severity score for COVID-19.

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.

United States lung cancer screening program websites: radiology representation, multimedia and multilingual content.

PURPOSE: To assess radiology representation, multimedia content, and multilingual content of United States lung cancer screening (LCS) program websites. MATERIALS AND 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.

Axillary Anatomy and Pathology: Pearls and "Pitfalls" for Thoracic Imagers.

The axilla contains several important structures which exist in a relatively confined anatomic space between the neck, chest wall, and upper extremity. While neoplastic lymphadenopathy may be among the most common axillary conditions, many other processes may be encountered. For example, expanded use of axillary vessels for access routes for endovascular procedures will increase the need for radiologists to access vessel anatomy, patency, and complications that may arise. Knowledge of axillary anatomy and pathology will allow the imager to systematically evaluate the axillae using various imaging modalities.

Lung-RADS Category 3 and 4 Nodules on Lung Cancer Screening in Clinical Practice.

BACKGROUND. Lung-RADS category 3 and 4 nodules account for most screening-detected lung cancers and are considered actionable nodules with management implications. The cancer frequency among such nodules is estimated in the Lung-RADS recommendations and has been investigated primarily by means of retrospectively assigned Lung-RADS classifications. OBJECTIVE. The purpose of this study was to assess the frequency of cancer among lung nodules assigned Lung-RADS category 3 or 4 at lung cancer screening (LCS) in clinical practice and to evaluate factors that affect the cancer frequency within each category. METHODS. This retrospective study was based on review of clinical radiology reports of 9148 consecutive low-dose CT LCS examinations performed for 4798 patients between June 2014 and January 2021 as part of an established LCS program. Unique nodules assigned Lung-RADS category 3 or 4 (4A, 4B, or 4X) that were clinically categorized as benign or malignant in a multidisciplinary conference that considered histologic analysis and follow-up imaging were selected for further analysis. Benign diagnoses based on stability required at least 12 months of follow-up imaging. Indeterminate nodules were excluded. Cancer frequencies were evaluated. RESULTS. Of the 9148 LCS examinations, 857 (9.4%) were assigned Lung-RADS category 3, and 721 (7.9%) were assigned category 4. The final analysis included 1297 unique nodules in 1139 patients (598 men, 541 women; mean age, 66.0 ± 6.3 years). A total of 1108 of 1297 (85.4%) nodules were deemed benign, and 189 of 1297 (14.6%) were deemed malignant. The frequencies of malignancy of category 3, 4A, 4B, and 4X nodules were 3.9%, 15.5%, 36.3%, and 76.8%. A total of 45 of 46 (97.8%) endobronchial nodules (all category 4A) were deemed benign on the basis of resolution. Cancer frequency was 13.1% for solid, 24.4% for part-solid, and 13.5% for ground-glass nodules. CONCLUSION. In the application of Lung-RADS to LCS clinical practice, the frequency of Lung-RADS category 3 and 4 nodules and the cancer frequency in these categories were higher than the prevalence and cancer risk estimated for category 3 and 4 nodules in the Lung-RADS recommendations and those reported in earlier studies in which category assignments were retrospective. Nearly all endobronchial category 4A nodules were benign. CLINICAL IMPACT. Future Lung-RADS iterations should consider the findings of this study from real-world practice to improve the clinical utility of the system.

Longitudinal CT and MRI Characteristics of Unilocular Thymic Cysts.

Background Isoattenuating and hyperattenuating thymic cysts at CT are often misinterpreted as lymphadenopathy or thymic epithelial neoplasms. Purpose To evaluate the longitudinal change in thymic cyst appearance at CT and MRI. Materials and Methods All chest MRI studies showing thymic cysts between July 2008 and December 2019, identified from a retrospective search of a quality assurance database, were included in this study if initial CT depicted a thymic lesion, the patient was referred for follow-up MRI for characterization, and the baseline (ie, index) MRI indicated a cystic lesion. Follow-up CT scans and/or MRI scans were identified through July 2020. Thymic cyst characteristics, such as size, location, and morphologic features, as well as CT and MRI characteristics, were recorded. Change in size, attenuation, and T1-weighted MRI signal was assessed longitudinally. Descriptive statistics of longitudinal change were tabulated. Results A total of 244 chest MRI studies in 140 patients with 142 unique cysts and 392 CT examinations (636 total examinations and 645 thymic cysts-nine examinations with two cysts each) were evaluated. The median follow-up duration was 2.2 years. Thirty-three patients with 34 unique cysts (34 of 142 cysts [24%]) underwent imaging follow-up for more than 5 years. Thymic cysts followed up for more than 5 years were most commonly saccular (189 of 274 cysts [69% axially]) and retrosternal (14 of 34 cysts [41%]). Craniocaudal dimension was larger than transverse and anteroposterior dimensions in 223 of 274 cysts (81%). Mean thymic cyst attenuation was 25 HU (range, 15-100 HU). Five of 31 cysts (16%) exhibited wall calcification. The median cyst wall thickness was 2.0 mm (range, 0.9-3.0 mm). Most thymic cysts changed in volume (31 of 34 cysts [91%]), CT attenuation (15 of 35 cysts [43%]), and T1-weighted MRI signal (12 of 18 cysts [67%]) over time. None developed mural irregularity, nodularity, or septations. Conclusion Unilocular thymic cysts, defined at index MRI, never developed irregular wall thickening, mural nodularity, or septations that would raise concern for malignant transformation. However, these cysts showed mural calcification and change in size, CT attenuation, and MRI signal over more than 5 years of follow-up. © RSNA, 2021 Online supplemental material is available for this article.

Clinical and Imaging Features of Non-Small Cell Lung Cancer with G12C KRAS Mutation.

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.

Comparison of Chest CT Findings of COVID-19, Influenza, and Organizing Pneumonia: A Multireader Study.

BACKGROUND. Previous studies compared CT findings of COVID-19 pneumonia with those of other infections; however, to our knowledge, no studies to date have included noninfectious organizing pneumonia (OP) for comparison. OBJECTIVE. The objectives of this study were to compare chest CT features of COVID-19, influenza, and OP using a multireader design and to assess the performance of radiologists in distinguishing between these conditions. METHODS. This retrospective study included 150 chest CT examinations in 150 patients (mean [± SD] age, 58 ± 16 years) with a diagnosis of COVID-19, influenza, or non-infectious OP (50 randomly selected abnormal CT examinations per diagnosis). Six thoracic radiologists independently assessed CT examinations for 14 individual CT findings and for Radiological Society of North America (RSNA) COVID-19 category and recorded a favored diagnosis. The CT characteristics of the three diagnoses were compared using random-effects models; the diagnostic performance of the readers was assessed. RESULTS. COVID-19 pneumonia was significantly different (p < .05) from influenza pneumonia for seven of 14 chest CT findings, although it was different (p < .05) from OP for four of 14 findings (central or diffuse distribution was seen in 10% and 7% of COVID-19 cases, respectively, vs 20% and 21% of OP cases, respectively; unilateral distribution was seen in 1% of COVID-19 cases vs 7% of OP cases; non-tree-in-bud nodules was seen in 32% of COVID-19 cases vs 53% of OP cases; tree-in-bud nodules were seen in 6% of COVID-19 cases vs 14% of OP cases). A total of 70% of cases of COVID-19, 33% of influenza cases, and 47% of OP cases had typical findings according to RSNA COVID-19 category assessment (p < .001). The mean percentage of correct favored diagnoses compared with actual diagnoses was 44% for COVID-19, 29% for influenza, and 39% for OP. The mean diagnostic accuracy of favored diagnoses was 70% for COVID-19 pneumonia and 68% for both influenza and OP. CONCLUSION. CT findings of COVID-19 substantially overlap with those of influenza and, to a greater extent, those of OP. The diagnostic accuracy of the radiologists was low in a study sample that contained equal proportions of these three types of pneumonia. CLINICAL IMPACT. Recognized challenges in diagnosing COVID-19 by CT are furthered by the strong overlap observed between the appearances of COVID-19 and OP on CT. This challenge may be particularly evident in clinical settings in which there are substantial proportions of patients with potential causes of OP such as ongoing cancer therapy or autoimmune conditions.

Unilateral Lymphadenopathy After COVID-19 Vaccination: A Practical Management Plan for Radiologists Across Specialties.

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.

Multi-Radiologist User Study for Artificial Intelligence-Guided Grading of COVID-19 Lung Disease Severity on Chest Radiographs.

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.

Management and Outcomes of Suspected Infectious and Inflammatory Lung Abnormalities Identified on Lung Cancer Screening CT.

BACKGROUND. Incidental findings are frequently encountered during lung cancer screening (LCS). Limited data describe the prevalence of suspected acute infectious and inflammatory lung processes on LCS and how they should be managed. OBJECTIVE. The purpose of this study was to determine the prevalence, radiologic reporting and management, and outcome of suspected infectious and inflammatory lung processes identified incidentally during LCS and to propose a management algorithm. METHODS. This retrospective study included 6314 low-dose CT (LDCT) examinations performed between June 2014 and April 2019 in 3800 patients as part of an established LCS program. Radiology reports were reviewed, and patients with potentially infectious or inflammatory lung abnormalities were identified and analyzed for descriptors of imaging findings, Lung-RADS designation, recommendations, and clinical outcomes. Using the descriptors, outcomes, and a greater than 2% threshold risk of malignancy, a follow-up algorithm was developed to decrease additional imaging without affecting cancer detection. RESULTS. A total of 331/3800 (8.7%) patients (178 men, 153 women; mean age [range], 66 [53-87] years) undergoing LCS had lung findings that were attributed to infection or inflammation. These abnormalities were reported as potentially significant findings using the S modifier in 149/331 (45.0%) and as the dominant nodule used to determine the Lung-RADS category in 96/331 (29.0%). Abnormalities were multiple or multifocal in 260/331 (78.5%). Common descriptors were ground-glass (155/331; 46.8%), tree-in-bud (56/331; 16.9%), consolidation (41/331; 12.4%), and clustered (67/331; 20.2%) opacities. A follow-up chest CT outside of screening was performed within 12 months or less in 264/331 (79.8%) and within 6 months or less in 186/331 (56.2%). A total of 260/331 (78.5%) opacities resolved on follow-up imaging. Two malignancies (2/331; 0.6%) were associated with these abnormalities and both had consolidations. Theoretic adoption of a proposed management algorithm for suspected infectious and inflammatory findings reduced unnecessary follow-up imaging by 82.6% without missing a single malignancy. CONCLUSION. Presumed acute infectious or inflammatory lung abnormalities are frequently encountered in the setting of LCS. These opacities are commonly multifocal and resolve on follow-up. Less than 1% are associated with malignancy. CLINICAL IMPACT. Adoption of a conservative management algorithm can standardize recommendations and reduce unnecessary imaging without increasing the risk of missing a malignancy.

Clinical and Imaging Features of Non-Small-Cell Lung Cancer in Young Patients.

BACKGROUND: Non-small-cell lung cancer (NSCLC) in young adult patients is rare, with scarce data available in patients aged < 40 years and even less in those aged < 35 years. Our goal was to determine the presenting symptoms, clinicopathologic characteristics, and imaging features of young patients with NSCLC at time of diagnosis and compare them to those of older adults. PATIENTS AND METHODS: We retrospectively analyzed the medical records and imaging of young patients (≤ 40 years old) with NSCLC treated at our institution between 1998 and 2018. Patients < 35 years old were compared to those between 35 and 40 years old. Characteristics of patients ≤ 40 years old were compared to older patients (> 40 years) from publicly available data sets. RESULTS: We identified 166 young patients with NSCLC (median age, 36.6 years; range, 18-40 years). Most presented with nonspecific respiratory symptoms and were diagnosed with pneumonia (84/136, 62%). Compared to patients < 35 years old, patients 35-40 years old were more likely to have malignancy detected incidentally (15% vs. 5%, P = .04). Patients < 35 years old were more likely to have central tumors (55% vs. 33%, P = .02) and to have bone (38% vs. 19%, P = .007) and lung (39% vs. 24%, P = .03) metastases. Compared to older patients (> 40 years), young patients were more likely to be never smokers (65.0% vs. 14.7%, P < .001) and to have advanced disease (88% vs. 66%, P < .001). CONCLUSION: Young patients with NSCLC often present with nonspecific symptoms and have advanced disease at diagnosis, often mimicking other pathologies. Awareness of the clinical presentation and imaging features of NSCLC in young patients may help minimize delays in diagnoses.

Clinicopathologic and Longitudinal Imaging Features of Lung Cancer Associated With Cystic Airspaces: A Systematic Review and Meta-Analysis.

BACKGROUND. Lung cancer (LC) associated with cystic airspaces is an uncommon presentation that is underrecognized on imaging. Additionally, understanding of its underlying pathology and risk factors is limited, which can contribute to delays in diagnosis. OBJECTIVE. The purpose of this analysis was to systematically review, analyze, and synthesize the medical literature to determine the imaging features of LC associated with cystic airspaces. EVIDENCE ACQUISITION. In accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we included published research reporting the clinical, pathologic, and imaging features of LC associated with cystic airspaces. We then performed a pooled analysis of continuous and categoric data with respect to patient clinical characteristics, tumor pathologic features, underlying driver mutation, CT features, and evolution of these features over time. EVIDENCE SYNTHESIS. The analysis included eight original observational studies with a combined total of 341 patients with LC associated with cystic airspaces (weighted mean age, 61.8 years; range, 30-87 years; 135 women and 206 men). Most patients were current or previous smokers (127/192 [66.1%]). The most common histologic finding was adenocarcinoma (289/328 [88.1%]) followed by squamous cell carcinoma (30/328 [9.1%]). The most common driver mutations were EGFR (46/122 [37.7%]) and KRAS (21/122 [17.2%]). The cysts in LC associated with cystic airspaces commonly had nonuniform (104/114 [91.2%]) and thick (83/222 [37.4%]) walls, irregular margins (53/142 [37.3%]), and were unilocular (173/272 [63.6%]). Most cysts had a nodular component (210/328 [64.0%]). Over time, most cysts showed development or enlargement of the nodular component (61/89 [68.5%]), approximately half showed wall thickening (43/89 [48.3%]), and a minority evolved into completely solid lesions (11/89 [12.4%]). The size of the cystic component increased in 36 of 89 patients (40.4%), decreased in 28 (31.5%), and remained stable in 24 (27.0%). CONCLUSION. LC associated with cystic airspaces occurs most commonly as adeno-carcinoma and is seen in both smokers and nonsmokers. The cysts associated with LC show wall thickening and mural nodularity, which may evolve over time. LC associated with cystic airspaces can be indolent, and long-term surveillance with imaging should be considered if cysts are not resected. CLINICAL IMPACT. Familiarity with the imaging features and temporal evolution of LC associated with cystic airspaces can minimize delays in LC diagnosis. Future management guidelines should include protocols for follow-up and management of cystic lung lesions identified during diagnostic and LC screening CT.

Detection of Extrapulmonary Malignancy During Lung Cancer Screening: 5-Year Analysis at a Tertiary Hospital.

PURPOSE: The aims of this study were to determine the prevalence and outcomes of extrapulmonary malignancies identified on lung cancer screening (LCS) and to determine the cost associated with the investigation of these lesions. METHODS: This retrospective study included 7,414 low-dose CT studies performed between June 2014 and December 2019 on 4,160 patients as part of an established LCS program. Patients with indeterminate extrapulmonary lesions were identified, and the diagnostic workup, management, and outcomes of the lesions were determined. Costs related to diagnostic evaluation were estimated using 2020 total facility relative value units and the 2020 Medicare conversion factor. Out-of-pocket costs were extracted from billing records. RESULTS: There were 20 extrapulmonary malignancies among 241 reported lesions in 225 patients (mean age, 66.1 ± 6.4 years; 109 men, 116 women). The prevalence of extrapulmonary malignancy was 20 of 4,160 (0.48%). Early-stage cancers were detected in 13 of 20 (65%). No cancer-specific mortality was observed. The predictive value for malignancy varied by organ (P = .03) and was highest in the chest wall and axilla (36.4%), followed by bone (25%). The average cost on the basis of Medicare reimbursement for diagnosis of an extrapulmonary malignancy on LCS was $1,316.03 ($6.33 per participant and $109.21 per indeterminate incidental lesion). Most patients (203 of 225 [90.2%]) did not have out-of-pocket costs related to diagnostic workup. In those who did, the median cost was $160.60 (range, $75-$606.76). CONCLUSIONS: Low-dose CT for LCS detects extrapulmonary malignancy with high predictive value for certain locations. There is cost associated in the workup related to these incidental lesions, but most malignancies are detected at early stages and have good outcomes.