ACR Lung-RADS v2022: Assessment Categories and Management Recommendations.

The American College of Radiology created the Lung CT Screening Reporting and Data System (Lung-RADS) in 2014 to standardize the reporting and management of screen-detected pulmonary nodules. Lung-RADS was updated to version 1.1 in 2019 and revised size thresholds for nonsolid nodules, added classification criteria for perifissural nodules, and allowed for short-interval follow-up of rapidly enlarging nodules that may be infectious in etiology. Lung-RADS v2022, released in November 2022, provides several updates including guidance on the classification and management of atypical pulmonary cysts, juxtapleural nodules, airway-centered nodules, and potentially infectious findings. This new release also provides clarification for determining nodule growth and introduces stepped management for nodules that are stable or decreasing in size. This article summarizes the current evidence and expert consensus supporting Lung-RADS v2022.

ACR Lung-RADS v2022: Assessment Categories and Management Recommendations.

The ACR created the Lung CT Screening Reporting and Data System (Lung-RADS) in 2014 to standardize the reporting and management of screen-detected pulmonary nodules. Lung-RADS was updated to version 1.1 in 2019 and revised size thresholds for nonsolid nodules, added classification criteria for perifissural nodules, and allowed for short-interval follow-up of rapidly enlarging nodules that may be infectious in etiology. Lung-RADS v2022, released in November 2022, provides several updates including guidance on the classification and management of atypical pulmonary cysts, juxtapleural nodules, airway-centered nodules, and potentially infectious findings. This new release also provides clarification for determining nodule growth and introduces stepped management for nodules that are stable or decreasing in size. This article summarizes the current evidence and expert consensus supporting Lung-RADS v2022.

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.

Impact of Baseline Interstitial Lung Abnormalities on Pneumonitis Risk in Patients Receiving Immune Checkpoint Inhibitors for Non-Small-Cell Lung Cancer.

INTRODUCTION/BACKGROUND: Immune-related pneumonitis is a potentially fatal complication of treatment with immune checkpoint inhibitors (ICIs). Interstitial lung disease (ILD) is associated with increased risk for pneumonitis, but the impact of interstitial abnormalities (ILA) in the absence of ILD has not been extensively assessed. We examined the relationship between ILA on pretreatment chest computed tomography (CT) scans and risk of pneumonitis in patients with non-small-cell lung cancer (NSCLC). METHODS: This retrospective cohort study included consecutive adult patients who received ICI for NSCLC between January 2013 and January 2020 at our institution. Two thoracic radiologists blinded to clinical outcomes independently reviewed pre-ICI chest CTs to identify and categorize ILA using previously published definitions. We used uni- and multivariable analysis adjusted for age, radiation, and smoking status to assess for associations between ILA, clinicopathologic characteristics, and symptomatic (CTCAE grade ≥2) pneumonitis. RESULTS: Of 475 patients who received ICI treatment and met inclusion criteria, baseline ILA were present in 78 (16.4%) patients, most commonly as a subpleural nonfibrotic pattern. In total, 43 (9.1%) of 475 patients developed symptomatic pneumonitis. Pneumonitis occurred in 16.7% of patients with ILA compared to 7.6% patients without ILA (P < .05). Presence of ground glass and extent of lung parenchymal involvement were associated with an increased risk of pneumonitis. On multivariable analysis, baseline ILA remained associated with increased risk of symptomatic pneumonitis (OR 2.2, 95% CI, 1.0-4.5). CONCLUSIONS: Baseline ILAs are associated with the development of symptomatic pneumonitis in patients with NSCLC treated with ICI. Additional studies are needed to validate these observations.

Predicting benefit from immune checkpoint inhibitors in patients with non-small-cell lung cancer by CT-based ensemble deep learning: a retrospective study.

BACKGROUND: Only around 20-30% of patients with non-small-cell lung cancer (NCSLC) have durable benefit from immune-checkpoint inhibitors. Although tissue-based biomarkers (eg, PD-L1) are limited by suboptimal performance, tissue availability, and tumour heterogeneity, radiographic images might holistically capture the underlying cancer biology. We aimed to investigate the application of deep learning on chest CT scans to derive an imaging signature of response to immune checkpoint inhibitors and evaluate its added value in the clinical context. METHODS: In this retrospective modelling study, 976 patients with metastatic, EGFR/ALK negative NSCLC treated with immune checkpoint inhibitors at MD Anderson and Stanford were enrolled from Jan 1, 2014, to Feb 29, 2020. We built and tested an ensemble deep learning model on pretreatment CTs (Deep-CT) to predict overall survival and progression-free survival after treatment with immune checkpoint inhibitors. We also evaluated the added predictive value of the Deep-CT model in the context of existing clinicopathological and radiological metrics. FINDINGS: Our Deep-CT model demonstrated robust stratification of patient survival of the MD Anderson testing set, which was validated in the external Stanford set. The performance of the Deep-CT model remained significant on subgroup analyses stratified by PD-L1, histology, age, sex, and race. In univariate analysis, Deep-CT outperformed the conventional risk factors, including histology, smoking status, and PD-L1 expression, and remained an independent predictor after multivariate adjustment. Integrating the Deep-CT model with conventional risk factors demonstrated significantly improved prediction performance, with overall survival C-index increases from 0·70 (clinical model) to 0·75 (composite model) during testing. On the other hand, the deep learning risk scores correlated with some radiomics features, but radiomics alone could not reach the performance level of deep learning, indicating that the deep learning model effectively captured additional imaging patterns beyond known radiomics features. INTERPRETATION: This proof-of-concept study shows that automated profiling of radiographic scans through deep learning can provide orthogonal information independent of existing clinicopathological biomarkers, bringing the goal of precision immunotherapy for patients with NSCLC closer. FUNDING: National Institutes of Health, Mark Foundation Damon Runyon Foundation Physician Scientist Award, MD Anderson Strategic Initiative Development Program, MD Anderson Lung Moon Shot Program, Andrea Mugnaini, and Edward L C Smith.

U.S. Newspaper Coverage of Lung Cancer Screening From 2010 to 2022.

BACKGROUND. Newspapers are an important source of information for the public about low-dose CT (LDCT) lung cancer screening (LCS) and may influence public perception and knowledge of this important cancer screening service. OBJECTIVE. The purpose of this article was to evaluate the volume, content, and other characteristics of articles pertaining to LCS that have been published in U.S. newspapers. METHODS. The ProQuest U.S. Newsstream database was searched for U.S. newspaper articles referring to LCS published between January 1, 2010 (the year of publication of the National Lung Screening Trial results), and March 28, 2022. Search terms included "lung cancer screening(s)," "lung screening(s)," "low dose screening(s)," and "LDCT." Search results were reviewed to identify those articles mentioning LCS. Characteristics of included articles and originating newspapers were extracted. Articles were divided among nine readers, who independently assessed article sentiment regarding LCS and additional article content using a standardized form. RESULTS. The final analysis included 859 articles, comprising 816 nonsyndicated articles published in a single newspaper and 43 syndicated articles published in multiple newspapers. Sentiment regarding LCS was positive in 76% (651/859) of articles, neutral in 21% (184/859), and negative in 3% (24/859). Frequency of positive sentiment was lowest (61%) for articles published from 2010 to 2012; frequency of negative sentiment was highest (8%) for articles published in newspapers in the highest quartile for weekly circulation. LCS enrollment criteria were mentioned in 52% of articles, smoking cessation programs in 28%, need for annual CT in 27%, and shared decision-making in 4%. Cost or insurance coverage for LCS was mentioned in 33% in articles. A total of 64% of articles mentioned at least one benefit of LCS (most commonly early detection or possible cure of lung cancer), and 23% mentioned at least one harm (most commonly false-positives). A total of 9% of articles interviewed or mentioned a radiologist. CONCLUSION. The sentiment of U.S. newspaper articles covering LCS from 2010 to 2022 was overall positive. However, certain key elements of LCS were infrequently mentioned. CLINICAL IMPACT. The findings highlight areas for potential improvement of LCS media coverage; radiologists have an opportunity to take a more active role in this coverage.

Efficacy and clinicogenomic correlates of response to immune checkpoint inhibitors alone or with chemotherapy in non-small cell lung cancer.

The role of combination chemotherapy with immune checkpoint inhibitors (ICI) (ICI-chemo) over ICI monotherapy (ICI-mono) in non-small cell lung cancer (NSCLC) remains underexplored. In this retrospective study of 1133 NSCLC patients, treatment with ICI-mono vs ICI-chemo associate with higher rates of early progression, but similar long-term progression-free and overall survival. Sequential vs concurrent ICI and chemotherapy have similar long-term survival, suggesting no synergism from combination therapy. Integrative modeling identified PD-L1, disease burden (Stage IVb; liver metastases), and STK11 and JAK2 alterations as features associate with a higher likelihood of early progression on ICI-mono. CDKN2A alterations associate with worse long-term outcomes in ICI-chemo patients. These results are validated in independent external (n = 89) and internal (n = 393) cohorts. This real-world study suggests that ICI-chemo may protect against early progression but does not influence overall survival, and nominates features that identify those patients at risk for early progression who may maximally benefit from ICI-chemo.

Prediction of oxygen requirement in patients with COVID-19 using a pre-trained chest radiograph xAI model: efficient development of auditable risk prediction models via a fine-tuning approach.

Risk prediction requires comprehensive integration of clinical information and concurrent radiological findings. We present an upgraded chest radiograph (CXR) explainable artificial intelligence (xAI) model, which was trained on 241,723 well-annotated CXRs obtained prior to the onset of the COVID-19 pandemic. Mean area under the receiver operating characteristic curve (AUROC) for detection of 20 radiographic features was 0.955 (95% CI 0.938-0.955) on PA view and 0.909 (95% CI 0.890-0.925) on AP view. Coexistent and correlated radiographic findings are displayed in an interpretation table, and calibrated classifier confidence is displayed on an AI scoreboard. Retrieval of similar feature patches and comparable CXRs from a Model-Derived Atlas provides justification for model predictions. To demonstrate the feasibility of a fine-tuning approach for efficient and scalable development of xAI risk prediction models, we applied our CXR xAI model, in combination with clinical information, to predict oxygen requirement in COVID-19 patients. Prediction accuracy for high flow oxygen (HFO) and mechanical ventilation (MV) was 0.953 and 0.934 at 24 h and 0.932 and 0.836 at 72 h from the time of emergency department (ED) admission, respectively. Our CXR xAI model is auditable and captures key pathophysiological manifestations of cardiorespiratory diseases and cardiothoracic comorbidities. This model can be efficiently and broadly applied via a fine-tuning approach to provide fully automated risk and outcome predictions in various clinical scenarios in real-world practice.

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.

Accurate auto-labeling of chest X-ray images based on quantitative similarity to an explainable AI model.

The inability to accurately, efficiently label large, open-access medical imaging datasets limits the widespread implementation of artificial intelligence models in healthcare. There have been few attempts, however, to automate the annotation of such public databases; one approach, for example, focused on labor-intensive, manual labeling of subsets of these datasets to be used to train new models. In this study, we describe a method for standardized, automated labeling based on similarity to a previously validated, explainable AI (xAI) model-derived-atlas, for which the user can specify a quantitative threshold for a desired level of accuracy (the probability-of-similarity, pSim metric). We show that our xAI model, by calculating the pSim values for each clinical output label based on comparison to its training-set derived reference atlas, can automatically label the external datasets to a user-selected, high level of accuracy, equaling or exceeding that of human experts. We additionally show that, by fine-tuning the original model using the automatically labelled exams for retraining, performance can be preserved or improved, resulting in a highly accurate, more generalized model.

Thoracic Imaging Manifestations of Treated Lymphomas: Response Evaluation, Posttherapeutic Sequelae, and Complications.

Lymphoma is the most common hematologic malignancy comprising a diverse group of neoplasms arising from multiple blood cell lineages. Any structure of the thorax may be involved at any stage of disease. Imaging has a central role in the initial staging, response assessment, and surveillance of lymphoma, and updated standardized assessment criteria are available to assist with imaging interpretation and reporting. Radiologists should be aware of the modern approaches to lymphoma treatment, the role of imaging in posttherapeutic surveillance, and manifestations of therapy-related complications.

Liquid Biopsy, Diagnostic Imaging, and Future Synergies.

Imaging plays an integral role in the initial diagnosis and longitudinal care of patients with cancer. Liquid biopsies, which most commonly involve genetic analysis of circulating free DNA, have emerged as important complementary tools in cancer care with the potential to interface with imaging at each step of the cancer care continuum. Here, the authors use non-small-cell lung cancer as a paradigm to elucidate factors driving the need for liquid biopsy in the spectrum of lung cancer care, demonstrate ways in which liquid biopsy has already changed standard clinical practice, and discuss anticipated synergies of liquid biopsy and imaging in screening and early detection and in monitoring of disease.

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.

Factors affecting patient adherence to lung cancer screening: A multisite analysis.

OBJECTIVE: To identify factors associated with delayed adherence to follow-up in lung cancer screening. METHODS: Utilizing a data warehouse and lung cancer screening registry, variables were collected from a referred sample of 3110 unique participants with follow-up CT during the study period (1 January 2016 to 17 October 2018). Adherence was defined as undergoing chest CT within 90 days and 30 days of the recommended time for follow-up and was determined using proportions and multiple variable logistic regression models across the American College of Radiology Lung Imaging Reporting and Data System (Lung-RADS®) categories. RESULTS: Of 1954 lung cancer screening participants (51.9% (1014/1954) males, 48.1% (940/1954) female; mean age 65.7 (range 45-87), smoking history median 40 pack-years, 60.2% and 44.5% did not follow-up within 30 and 90 days, respectively. Participants receiving Lung-RADS® category 1 or 2 presented later than those with Lung-RADS® category 3 at 90 days (coefficient -27.24, 95% CI -51.31, -3.16, p = 0.027). Participants with Lung-RADS® category 1 presented later than those with Lung-RADS® category 2 at both 90- and 30-days past due (OR 0.76 95% CI [0.59-0.97], p = 0.029 and OR 0.63 95% CI [0.48-0.83], p = 0.001, respectively). CONCLUSIONS: Adherence to follow-up was higher among participants receiving more suspicious Lung-RADS® results at index screening CT and among those who had undergone more non-lung cancer screening imaging examinations prior to index lung cancer screening CT. These observations may inform strategies aimed at prospectively identifying participants at risk for delayed or nonadherence to prevent potential morbidity and mortality from incident lung cancers.

Evaluation of the Informational Content and Readability of US Lung Cancer Screening Program Websites.

Importance: The internet is an important source of medical information for many patients and may have a key role in the education of patients about lung cancer screening (LCS). Although most LCS programs in the United States have informational websites, the accuracy, completeness, and readability of these websites have not previously been studied. Objective: To evaluate the informational content and readability of US LCS program websites. Design, Setting, and Participants: This cross-sectional study assessed US LCS program websites identified on September 15, 2018. A standardized checklist was used to assess key informational content of each website, and text was analyzed for reading level, word count, and reading time. Links to US websites of national advocacy organizations with LCS program content were tabulated. All functional LCS program websites in Google internet search engine results using the search terms lung cancer screening, low-dose CT screening, and lung screening were included in the analysis. Main Outcomes and Measures: Radiologists used a standardized checklist to evaluate content, and readability was assessed with validated scales. Website word count, reading time, and number of links to outside LCS informational websites were assessed. Results: A total of 257 LCS websites were included in the analysis. The word count ranged from 73 to 4410 (median, 571; interquartile range, 328-909). The reading time ranged from 0.3 to 19.6 minutes (median, 2.5; interquartile range, 1.5-4.0). The median reading level of all websites was grade 10 (interquartile range, 9-11). Only 26% (n = 66) of websites had at least 1 web link to a national website with additional information on LCS. There was wide variability regarding reported eligibility age criteria, with ages 55 to 77 years most frequently cited (42% [n = 108]). Only 56% (n = 143) of websites mentioned smoking cessation. The subject of patient cost was mentioned on 75% (n = 192) of websites. Although major LCS benefits, such as detection of lung cancer, were discussed by most (93% [n = 239]) websites, less than half of the websites (45% [n = 115]) made any mention of possible risks associated with screening. Conclusions and Relevance: There appears to be marked variability in the informational content of US LCS program websites, and the reading level of most websites is above that recommended by the American Medical Association and the National Institutes of Health. Efforts to improve website content and readability may be warranted.

Feasibility and accuracy of CT-guided percutaneous needle biopsy of cavitary pulmonary lesions.

PURPOSE: We aimed to evaluate the feasibility, accuracy, and complications of computed tomography (CT)-guided percutaneous transthoracic needle biopsy (PTNB) of cavitary lesions. METHODS: Consecutive PTNB procedures in an academic institution over a 4-year period were reviewed, 53 of which were performed on patients with cavitary lesions. The demographic data of patients, lesion characteristics, biopsy technique and complications, initial pathologic results, and final diagnosis were reviewed. A final diagnosis was established through surgical correlation, microbiology or clinico-radiologic follow-up for at least 18 months after biopsy. RESULTS: The overall accuracy of PTNB was 81%. In 33 patients (62%) the cavitary lesion was found to be malignant (23 lung cancers and 10 metastases). The sensitivity and specificity for malignancy was 91% and 100%, respectively. In 20 patients (38%) a benign etiology was established (16 infections and 4 noninfectious etiologies), with PTNB demonstrating a sensitivity of 81% and specificity of 100% for infection. Wall thickness at the biopsy site, lesion in lower lobe, and malignancy were significant independent risk factors for diagnostic success. Minor complications occurred in 28% of cases: 13 pneumothoraces (5 requiring chest tube), 1 small hemothorax, and 1 mild hemoptysis. A nonsignificant higher chest tube insertion rate was seen in cavities with a thinner wall. CONCLUSION: PTNB of cavitary lesions provides high accuracy, sensitivity, and specificity for both malignancy and infection and has an acceptable complication rate. Wall thickness at the biopsy site, lesion in lower lobe, and malignancy were significant independent risk factors for diagnostic success. Samples for microbiology should be obtained in all patients, especially in the absence of on-site cytology, due to the high prevalence of infection in cavitary lesions.