CT Scan-Guided Fine Needle Aspiration Cytology for Lung Cancer Diagnosis through the COVID-19 Pandemic: What We Have Learned.

BACKGROUND AND RATIONALE: Novel coronavirus-related disease (COVID-19) has profoundly influenced hospital organization and structures worldwide. In Italy, the Lombardy Region, with almost 17% of the Italian population, rapidly became the most severely affected area since the pandemic beginning. The first and the following COVID-19 surges significantly affected lung cancer diagnosis and subsequent management. Much data have been already published regarding the therapeutic repercussions whereas very few reports have focused on the consequences of the pandemic on diagnostic procedures. METHODS: We, here, would like to analyze data of novel lung cancer diagnosis performed in our Institution in Norther Italy where we faced the earliest and largest outbreaks of COVID-19 in Italy. RESULTS: We discuss, in detail, the strategies developed to perform biopsies and the safe pathways created in emergency settings to protect lung cancer patients in subsequent therapeutic phases. Quite unexpectedly, no significant differences emerged between cases enrolled during the pandemic and those before, and the two populations were homogeneous considering the composition and diagnostic and complication rates. CONCLUSIONS: By pointing out the role of multidisciplinarity in emergency contexts, these data will be of help in the future for designing tailored strategies to manage lung cancer in a real-life setting.

Bend but don't break: Experience of a diverse New York City lung cancer screening program during the first year of the COVID-19 pandemic.

RATIONALE AND OBJECTIVES: The COVID-19 pandemic led to the national shutdown and subsequent reopening of cancer screening programs. Our diverse inner-city lung cancer screening program serves patients in the Bronx NY, which was severely affected by COVID-19, with the highest mortality in New York State in the spring of 2020. Staffing redeployment, quarantine protocols, increased safety measures, and changes in follow up resulted. The purpose of this study is to analyze the effect of the pandemic on lung cancer screening volumes during the first year of the pandemic. METHODS AND MATERIALS: Retrospective cohort comprised of all patients enrolled in our Bronx, NY lung cancer screening program from March 2019 to March 2021 who underwent LDCT or appropriate follow-up imaging. The pre-pandemic and pandemic period were defined as 3/28/2019 to 3/21/2020 and 3/22/2020 to 3/17/2021, respectively, dichotomized by the New York State lockdown. RESULTS: 1218 exams were performed in the pre-pandemic period and 857 in the pandemic period, a 29.6% decrease. The percentage of exams performed on newly enrolled patients decreased from 32.7% to 13.8% (p < 0.001). Patients in the pre-pandemic period and pandemic period respectively had the following demographic breakdown: mean age 66.9 ± 5.9 vs 66.5 ± 6.0, women 51.9% vs 51.6%, White 20.7% vs 20.3%, Hispanic/Latino 42.0% vs 36.3%. There was no significant difference in Lung-RADS scores for pre-pandemic and pandemic exams (p > 0.05). In the pandemic period, exam volume followed an inverted parabolic pattern, reflecting Covid surges for the cohort and all demographic subgroups. CONCLUSION: The COVID-19 pandemic significantly decreased lung cancer screening volume and new enrollment in our urban inner-city program. Screening volumes demonstrated a parabolic curve reflecting pandemic surges following the initial wave, unlike other reports. The combination of the impact of COVID on our population and lack of staffing redundancy in the screening program, in the face of typical COVID isolation and quarantine absences, impeded early pandemic rebound of our lung cancer screening program. This highlights the necessity of fostering resilience by developing robust programmatic resources.

Influences on Lung Cancer Screening Initiation and Retention in Rural Alabama.

INTRODUCTION/OBJECTIVES: Despite the introduction of lung cancer screening using low dose computed tomography (LDCT), overall screening rates in the U.S. remain low, with certain populations including Black and rural communities experiencing additional disparities. The primary objective of this study was to understand the facilitators of lung cancer screening initiation and retention in Alabama reported by people at risk from mostly rural, mostly Black populations in Jefferson County-including the urban center of Birmingham-and 6 rural counties: Choctaw, Dallas, Greene, Hale, Marengo, and Sumter. METHODS: We conducted semi-structured telephone interviews with 58 people who underwent lung cancer screening between December 2019 and January 2022. Participant responses were recorded by the interviewer for analysis. Open-ended responses were coded to identify emergent themes. RESULTS: The most reported influences to initiate screening were information or suggestion from a Community Health Advisor (CHAs) or the supervising county coordinator, suggestion from a friend, or consideration of a personal history of smoking. Most participants reported multiple influences. Physicians were not very influential in decisions to initiate screening, but they were extremely influential in participants' intent to continue screening, both positively and negatively. Knowing the recommended timeline for their annual scans was also a predictor of intention to continue screening. Participants screened during the COVID-19 state of emergency expressed less certainty about dates of next scans and more ambivalence about intention to continue screening. CONCLUSIONS: This study shows the benefit of using multiple methods to support increased awareness of and interest in lung cancer screening, particularly when educational messaging through CHAs is used. Clear guideline-based messages from healthcare providers about recommended screening is important for increasing retention. COVID-19 related implementation challenges impacted screening recruitment and retention. Future research is warranted to further explore use of CHAs in lung cancer screening.

Validation of the North America expert consensus statement on reporting CT findings for COVID-19 in individuals with lung cancer.

The aim of our study was to validate the use of the standardized Radiological Society of North America (RSNA) reporting system in individuals with known lung cancer who presented to the emergency department with suspected COVID-19. We included patients aged 18 years or older from the Cancer Institute of the State of São Paulo (ICESP) with a confirmed diagnosis of lung cancer, admitted to the emergency department and undergoing chest computed tomography (CT) for suspicion of COVID-19. Comparison between SARS-CoV2 RT-PCR across RSNA categories was performed in all patients and further stratified by diagnosis of lung cancer progression. Among 58 individuals included in the analysis (65±9 years, 43% men), 20 had positive RT-PCR. Less than a half (43%) had no new lung findings in the CT. Positive RT-PCR was present in 75% of those with typical findings according to RSNA and in only 9% when these findings were classified as atypical or negative (P<0.001). Diagnostic accuracy was even higher when stratified by the presence or absence of progressive disease (PD). Extent of pulmonary inflammatory changes was strongly associated with higher mortality, reaching a lethality of 83% in patients with >25% of lung involvement and 100% when there was >50% of lung involvement. The lung involvement score was also highly predictive of prognosis in this population as was reported for non-lung cancer individuals. Collectively, our results demonstrated that diagnostic and prognostic values of chest CT findings in COVID-19 are robust to the presence of lung abnormalities related to lung cancer.

Breast and Lung Cancer Screening Among Medicare Enrollees During the COVID-19 Pandemic.

Importance: Several studies reported sharp decreases in screening mammography for breast cancer and low-dose computed tomographic screening for lung cancer in the early months of the COVID-19 pandemic, followed by a return to normal or near-normal levels in the summer of 2020. Objective: To determine the observed vs expected mammography and low-dose computed tomographic scan rates from the beginning of the pandemic through April 2022. Design, Setting, and Participants: In this retrospective cohort study assessing mammography and low-dose computed tomography rates from January 2017 through April 2022, data for January 2016 to February 2020 were used to generate expected rates for the period March 2020 to April 2022. The study included a 20% national sample of Medicare fee-for-service enrollees among women aged 50 to 74 years for mammography, and men and women aged 55 to 79 years for low-dose computed tomographic scan. Main Outcomes and Measures: Receipt of screening mammography or low-dose computed tomographic scan. Results: The yearly cohorts for the mammography rates included more than 1 600 000 women aged 50 to 74 years, and the cohorts for the low-dose computed tomographic scan rates included more than 3 700 000 men and women aged 55 to 79 years. From January 2017 through February 2020, monthly mammography rates were flat, whereas there was a monotonic increase in low-dose computed tomographic scan rates, from approximately 500 per million per month in early 2017 to 1100 per million per month by January 2020. Over the period from March 2020 to April 2022, there were episodic drops in both mammography and low-dose computed tomographic scan rates, coincident with increases in national COVID-19 infection rates. For the periods from March 2020 to February 2020 and March 2021 to February 2022, the observed low-dose computed tomographic scan rates were 24% (95% CI, 23%-24%) and 14% (95% CI, 13%-15%) below expected rates, whereas mammography rates were 17% (95% CI, 17%-18%) and 4% (95% CI, 4%-3%) below expected. Conclusions and Relevance: In this cohort study, the decreases in cancer screening during the early phases of the COVID-19 pandemic did not resolve after the initial pandemic surges. Successful interventions to improve screening rates should address pandemic-specific reasons for low screening participation.

Uptake of invitations to a lung health check offering low-dose CT lung cancer screening among an ethnically and socioeconomically diverse population at risk of lung cancer in the UK (SUMMIT): a prospective, longitudinal cohort study.

BACKGROUND: Lung cancer screening with low-dose CT reduces lung cancer mortality, but screening requires equitable uptake from candidates at high risk of lung cancer across ethnic and socioeconomic groups that are under-represented in clinical studies. We aimed to assess the uptake of invitations to a lung health check offering low-dose CT lung cancer screening in an ethnically and socioeconomically diverse cohort at high risk of lung cancer. METHODS: In this multicentre, prospective, longitudinal cohort study (SUMMIT), individuals aged 55-77 years with a history of smoking in the past 20 years were identified via National Health Service England primary care records at practices in northeast and north-central London, UK, using electronic searches. Eligible individuals were invited by letter to a lung health check offering lung cancer screening at one of four hospital sites, with non-responders re-invited after 4 months. Individuals were excluded if they had dementia or metastatic cancer, were receiving palliative care or were housebound, or declined research participation. The proportion of individuals invited who responded to the lung health check invitation by telephone was used to measure uptake. We used univariable and multivariable logistic regression analyses to estimate associations between uptake of a lung health check invitation and re-invitation of non-responders, adjusted for sex, age, ethnicity, smoking, and deprivation score. This study was registered prospectively with ClinicalTrials.gov, NCT03934866. FINDINGS: Between March 20 and Dec 12, 2019, the records of 2 333 488 individuals from 251 primary care practices across northeast and north-central London were screened for eligibility; 1 974 919 (84·6%) individuals were outside the eligible age range, 7578 (2·1%) had pre-existing medical conditions, and 11 962 (3·3%) had opted out of particpation in research and thus were not invited. 95 297 individuals were eligible for invitation, of whom 29 545 (31·0%) responded. Due to the COVID-19 pandemic, re-invitation letters were sent to only a subsample of 4594 non-responders, of whom 642 (14·0%) responded. Overall, uptake was lower among men than among women (odds ratio [OR] 0·91 [95% CI 0·88-0·94]; p<0·0001), and higher among older age groups (1·48 [1·42-1·54] among those aged 65-69 years vs those aged 55-59 years; p<0·0001), groups with less deprivation (1·89 [1·76-2·04] for the most vs the least deprived areas; p<0·0001), individuals of Asian ethnicity (1·14 [1·09-1·20] vs White ethnicity; p<0·0001), and individuals who were former smokers (1·89 [1·83-1·95] vs current smokers; p<0·0001). When ethnicity was subdivided into 16 groups, uptake was lower among individuals of other White ethnicity than among those with White British ethnicity (0·86 [0·83-0·90]), whereas uptake was higher among Chinese, Indian, and other Asian ethnicities than among those with White British ethnicity (1·33 [1·13-1·56] for Chinese ethnicity; 1·29 [1·19-1·40] for Indian ethnicity; and 1·19 [1·08-1·31] for other Asian ethnicity). INTERPRETATION: Inviting eligible adults for lung health checks in areas of socioeconomic and ethnic diversity should achieve favourable participation in lung cancer screening overall, but inequalities by smoking, deprivation, and ethnicity persist. Reminder and re-invitation strategies should be used to increase uptake and the equity of response. FUNDING: GRAIL.

Innovations in thoracic imaging: CT, radiomics, AI and x-ray velocimetry.

In recent years, pulmonary imaging has seen enormous progress, with the introduction, validation and implementation of new hardware and software. There is a general trend from mere visual evaluation of radiological images to quantification of abnormalities and biomarkers, and assessment of 'non visual' markers that contribute to establishing diagnosis or prognosis. Important catalysts to these developments in thoracic imaging include new indications (like computed tomography [CT] lung cancer screening) and the COVID-19 pandemic. This review focuses on developments in CT, radiomics, artificial intelligence (AI) and x-ray velocimetry for imaging of the lungs. Recent developments in CT include the potential for ultra-low-dose CT imaging for lung nodules, and the advent of a new generation of CT systems based on photon-counting detector technology. Radiomics has demonstrated potential towards predictive and prognostic tasks particularly in lung cancer, previously not achievable by visual inspection by radiologists, exploiting high dimensional patterns (mostly texture related) on medical imaging data. Deep learning technology has revolutionized the field of AI and as a result, performance of AI algorithms is approaching human performance for an increasing number of specific tasks. X-ray velocimetry integrates x-ray (fluoroscopic) imaging with unique image processing to produce quantitative four dimensional measurement of lung tissue motion, and accurate calculations of lung ventilation.

Acceptability of a standalone written leaflet for the National Health Service for England Targeted Lung Health Check Programme: A concurrent, think-aloud study.

BACKGROUND: Many countries are introducing low-dose computed tomography screening programmes for people at high risk of lung cancer. Effective communication strategies that convey risks and benefits, including unfamiliar concepts and outcome probabilities based on population risk, are critical to achieving informed choice and mitigating inequalities in uptake. METHODS: This study investigated the acceptability of an aspect of NHS England's communication strategy in the form of a leaflet that was used to invite and inform eligible adults about the Targeted Lung Health Check (TLHC) programme. Acceptability was assessed in terms of how individuals engaged with, comprehended and responded to the leaflet. Semi-structured, 'think aloud' interviews were conducted remotely with 40 UK screening-naïve current and former smokers (aged 55-73). The verbatim transcripts were analysed thematically using a coding framework based on the Dual Process Theory of cognition. RESULTS: The leaflet helped participants understand the principles and procedures of screening and fostered cautiously favourable intentions. Three themes captured the main results of the data analysis: (1) Response-participants experienced anxiety about screening results and further investigations, but the involvement of specialist healthcare professionals was reassuring; (2) Engagement-participants were rapidly drawn to information about lung cancer prevalence, and benefits of screening, but deliberated slowly about early diagnosis, risks of screening and less familiar symptoms of lung cancer; (3) Comprehension-participants understood the main principles of the TLHC programme, but some were confused by its rationale and eligibility criteria. Radiation risks, abnormal screening results and numerical probabilities of screening outcomes were hard to understand. CONCLUSION: The TLHC information leaflet appeared to be acceptable to the target population. There is scope to improve aspects of comprehension and engagement in ways that would support informed choice as a distributed process in lung cancer screening. PATIENT OR PUBLIC CONTRIBUTION: The insight and perspectives of patient representatives directly informed and improved the design and conduct of this study.

Effects of Bevacizumab Combined with Chemotherapy on CT, CyFRA21-1, and ProGRP and Prognosis of Lung Cancer Patients under Nursing Intervention.

Objective: Molecular targeted drug therapy and chemotherapy are the main treatments for advanced non-small-cell lung cancer, and the combination of both has advantages in prolonging patients' progression-free survival and overall survival. This study investigated the effects of bevacizumab combined with chemotherapy under nursing intervention on CT, cytokeratin 19 fragment antigen 21-1 (CYFRA21-1), and gastrin-releasing peptide precursor (ProGRP) and prognosis of lung cancer patients. Methods: 102 patients with non-small-cell lung cancer admitted to our hospital from January 2018 to May 2019 were divided into observation group and control group, with 51 cases each. The control group was treated with basic chemotherapy, and the observation group was treated with bevacizumab in combination with the control group, and both groups used nursing interventions. The clinical effects, CYFRA21-1 and ProGRP levels, baseline data, CT parameters, 24-month cumulative survival, and the effects of CYFRA21-1 and ProGRP on long-term survival and lung function were compared. Results: The disease control rate of the observation group was 94.12%, which was significantly higher than that of the control group (76.47%); after 7 d, 30 d, 60 d, and 90 d of treatment, the levels of CYFRA21-1 and ProGRP were statistically downregulated. The difference in lymph node metastasis, lesion diameter, plain Eff-Z, venous stage, and arterial stage normalized iodine concentrations (NIC) was statistically significant; the survival rate at 24 months in the observation group was 74.51% (38/51); the cumulative survival rate at 24 months in the control group was 52.94% (27/51), and the difference was statistically significant (X 2 = 4.980, P = 0.026). The cumulative survival rate at 24 months was significantly lower in patients with high expression of CYFRA21-1 and ProGRP compared with those with low expression of CYFRA21-1 and ProGRP. After treatment, in the observation group, the forceful spirometry (FVC), forceful expiratory volume in one second (FEV1), and FEV1/FVC levels were significantly different from those before treatment and were significantly different from those in the control group. Conclusion: Bevacizumab in combination with standard chemotherapy regimens with nursing interventions could benefit patients with advanced non-small-cell lung cancer and had a good prospect of application.

Prior-aware autoencoders for lung pathology segmentation.

Segmentation of lung pathology in Computed Tomography (CT) images is of great importance for lung disease screening. However, the presence of different types of lung pathologies with a wide range of heterogeneities in size, shape, location, and texture, on one side, and their visual similarity with respect to surrounding tissues, on the other side, make it challenging to perform reliable automatic lesion segmentation. To leverage segmentation performance, we propose a deep learning framework comprising a Normal Appearance Autoencoder (NAA) model to learn the distribution of healthy lung regions and reconstruct pathology-free images from the corresponding pathological inputs by replacing the pathological regions with the characteristics of healthy tissues. Detected regions that represent prior information regarding the shape and location of pathologies are then integrated into a segmentation network to guide the attention of the model into more meaningful delineations. The proposed pipeline was tested on three types of lung pathologies, including pulmonary nodules, Non-Small Cell Lung Cancer (NSCLC), and Covid-19 lesion on five comprehensive datasets. The results show the superiority of the proposed prior model, which outperformed the baseline segmentation models in all the cases with significant margins. On average, adding the prior model improved the Dice coefficient for the segmentation of lung nodules by 0.038, NSCLCs by 0.101, and Covid-19 lesions by 0.041. We conclude that the proposed NAA model produces reliable prior knowledge regarding the lung pathologies, and integrating such knowledge into a prior segmentation network leads to more accurate delineations.

Development of a multi-task learning V-Net for pulmonary lobar segmentation on CT and application to diseased lungs.

AIM: To develop a multi-task learning (MTL) V-Net for pulmonary lobar segmentation on computed tomography (CT) and application to diseased lungs. MATERIALS AND METHODS: The described methodology utilises tracheobronchial tree information to enhance segmentation accuracy through the algorithm's spatial familiarity to define lobar extent more accurately. The method undertakes parallel segmentation of lobes and auxiliary tissues simultaneously by employing MTL in conjunction with V-Net-attention, a popular convolutional neural network in the imaging realm. Its performance was validated by an external dataset of patients with four distinct lung conditions: severe lung cancer, COVID-19 pneumonitis, collapsed lungs, and chronic obstructive pulmonary disease (COPD), even though the training data included none of these cases. RESULTS: The following Dice scores were achieved on a per-segment basis: normal lungs 0.97, COPD 0.94, lung cancer 0.94, COVID-19 pneumonitis 0.94, and collapsed lung 0.92, all at p<0.05. CONCLUSION: Despite severe abnormalities, the model provided good performance at segmenting lobes, demonstrating the benefit of tissue learning. The proposed model is poised for adoption in the clinical setting as a robust tool for radiologists and researchers to define the lobar distribution of lung diseases and aid in disease treatment planning.

Contemporary Concise Review 2021: Pulmonary nodules from detection to intervention.

The US Preventive Task Force (USPSTF) has updated screening criteria by expanding age range and reducing smoking history required for eligibility; the International Lung Screen Trial (ILST) data have shown that PLCOM2012 performs better for eligibility than USPSTF criteria. Screening adherence is low (4%-6% of potential eligible candidates in the United States) and depends upon multiple system and patient/candidate-related factors. Smoking cessation in lung cancer improves survival (past prospective trial data, updated meta-analysis data); smoking cessation is an essential component of lung cancer screening. Circulating biomarkers are emerging to optimize screening and early diagnosis. COVID-19 continues to affect lung cancer treatment and screening through delays and disruptions; specific operational challenges need to be met. Over 70% of suspected malignant lesions develop in the periphery of the lungs. Bronchoscopic navigational techniques have been steadily improving to allow greater accuracy with target lesion approximation and therefore diagnostic yield. Fibre-based imaging techniques provide real-time microscopic tumour visualization, with potential diagnostic benefits. With significant advances in peripheral lung cancer localization, bronchoscopically delivered ablative therapies are an emerging field in limited stage primary and oligometastatic disease. In advanced stage lung cancer, small-volume samples acquired through bronchoscopic techniques yield material of sufficient quantity and quality to support clinically relevant biomarker assessment.

The state of the art for artificial intelligence in lung digital pathology.

Lung diseases carry a significant burden of morbidity and mortality worldwide. The advent of digital pathology (DP) and an increase in computational power have led to the development of artificial intelligence (AI)-based tools that can assist pathologists and pulmonologists in improving clinical workflow and patient management. While previous works have explored the advances in computational approaches for breast, prostate, and head and neck cancers, there has been a growing interest in applying these technologies to lung diseases as well. The application of AI tools on radiology images for better characterization of indeterminate lung nodules, fibrotic lung disease, and lung cancer risk stratification has been well documented. In this article, we discuss methodologies used to build AI tools in lung DP, describing the various hand-crafted and deep learning-based unsupervised feature approaches. Next, we review AI tools across a wide spectrum of lung diseases including cancer, tuberculosis, idiopathic pulmonary fibrosis, and COVID-19. We discuss the utility of novel imaging biomarkers for different types of clinical problems including quantification of biomarkers like PD-L1, lung disease diagnosis, risk stratification, and prediction of response to treatments such as immune checkpoint inhibitors. We also look briefly at some emerging applications of AI tools in lung DP such as multimodal data analysis, 3D pathology, and transplant rejection. Lastly, we discuss the future of DP-based AI tools, describing the challenges with regulatory approval, developing reimbursement models, planning clinical deployment, and addressing AI biases. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

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.

Efficacy Analysis of DSA-Guided Bronchial Arterial Chemoembolization Interventional Therapy in Patients with Middle-Advanced Primary Bronchial Lung Cancer.

Objective: To investigate the clinical efficacy of digital subtraction angiography- (DSA-) guided bronchial arterial chemoembolization (BACE) in patients with primary bronchial lung cancer. Methods: A total of 178 patients with primary intermediate and advanced bronchial lung cancer admitted to our hospital from February 2019 to March 2020 were selected as the subjects, and they were divided into control group (84 cases) and observation group (94 cases) according to the different chemotherapy regimens adopted by the patients. The control group was treated with traditional perfusion chemotherapy, and the observation group was treated with DSA-guided BACE interventional therapy, treated for 4 cycles, and followed up until the end of June 2021. The short-term clinical efficacy, hemoptysis remission, and incidence of adverse reactions were compared between the two groups. The mortality and recurrence rates between the two groups from treatment to the end of follow-up were counted, and the quality of life after treatment and 1 year after treatment were compared. Results: The short-term remission rate (73.40% vs. 58.33%), disease control rate (93.62% vs. 84.52%), hemoptysis remission rate (75.00% vs. 41.51%), the quality of life after chemotherapy cycle (90.86 ± 2.55 vs. 78.04 ± 2.21), and the quality of life after 1 year of follow-up (85.68 ± 2.23 vs. 70.27 ± 1.72) in the observation group were significantly higher than those in the control group, and the difference was statistically significant (P < 0.05). The incidence of adverse reactions (9.57% vs. 20.24%), mortality (10.64% vs. 21.43%), and recurrence rate (11.70% vs. 27.38%) during the follow-up period in the observation group were significantly lower than those in control group, and the differences were statistically significant (P < 0.05). Conclusion: DSA-guided BACE interventional therapy for patients with primary middle-advanced bronchial lung cancer has significant efficacy, which can not only reduce the mortality and recurrence rate of patients but also improve the quality of life of patients, with fewer adverse reactions and high safety, which is worthy of promotion.

Recognition of Peripheral Lung Cancer and Focal Pneumonia on Chest Computed Tomography Images Based on Convolutional Neural Network.

Introduction: Chest computed tomography (CT) is important for the early screening of lung diseases and clinical diagnosis, particularly during the COVID-19 pandemic. We propose a method for classifying peripheral lung cancer and focal pneumonia on chest CT images and undertake 5 window settings to study the effect on the artificial intelligence processing results. Methods: A retrospective collection of CT images from 357 patients with peripheral lung cancer having solitary solid nodule or focal pneumonia with a solitary consolidation was applied. We segmented and aligned the lung parenchyma based on some morphological methods and cropped this region of the lung parenchyma with the minimum 3D bounding box. Using these 3D cropped volumes of all cases, we designed a 3D neural network to classify them into 2 categories. We also compared the classification results of the 3 physicians with different experience levels on the same dataset. Results: We conducted experiments using 5 window settings. After cropping and alignment based on an automatic preprocessing procedure, our neural network achieved an average classification accuracy of 91.596% under a 5-fold cross-validation in the full window, in which the area under the curve (AUC) was 0.946. The classification accuracy and AUC value were 90.48% and 0.957 for the junior physician, 94.96% and 0.989 for the intermediate physician, and 96.92% and 0.980 for the senior physician, respectively. After removing the error prediction, the accuracy improved significantly, reaching 98.79% in the self-defined window2. Conclusion: Using the proposed neural network, in separating peripheral lung cancer and focal pneumonia in chest CT data, we achieved an accuracy competitive to that of a junior physician. Through a data ablation study, the proposed 3D CNN can achieve a slightly higher accuracy compared with senior physicians in the same subset. The self-defined window2 was the best for data training and evaluation.

Spontaneous tumor regression following COVID-19 vaccination.

Vaccination against COVID-19 is critical for immuno-compromised individuals, including patients with cancer. Systemic reactogenicity, a manifestation of the innate immune response to vaccines, occurs in up to 69% of patients following vaccination with RNA-based COVID-19 vaccines. Tumor regression can occur following an intense immune-inflammatory response and novel strategies to treat cancer rely on manipulating the host immune system. Here, we report spontaneous regression of metastatic salivary gland myoepithelial carcinoma in a patient who experienced grade 3 systemic reactogenicity, following vaccination with the mRNA-1273 COVID-19 vaccine. Histological and immunophenotypic inspection of the postvaccination lung biopsy specimens showed a massive inflammatory infiltrate with scant embedded tumor clusters (<5%). Highly multiplexed imaging mass cytometry showed that the postvaccination lung metastasis samples had remarkable immune cell infiltration, including CD4+ T cells, CD8+ T cells, natural killer cells, B cells, and dendritic cells, which contrasted with very low levels of these cells in the prevaccination primary tumor and lung metastasis samples. CT scans obtained 3, 6, and 9 months after the second vaccine dose demonstrated persistent tumor shrinkage (50%, 67%, and 73% reduction, respectively), suggesting that vaccination stimulated anticancer immunity. Insight: This case suggests that the mRNA-1273 COVID-19 vaccine stimulated anticancer immunity and tumor regression.

Engaging veteran stakeholders to identify patient-centred research priorities for optimizing implementation of lung cancer screening.

BACKGROUND: Patient engagement in research agenda setting is increasingly being seen as a strategy to improve the responsiveness of healthcare to patient priorities. Implementation of low-dose computed tomography (LDCT) screening for lung cancer is suboptimal, suggesting that research is needed. OBJECTIVES: This study aimed to describe an approach by which a Veteran patient group worked with other stakeholders to develop a research agenda for LDCT screening and to describe the research questions that they prioritized. METHODS: We worked with Veterans organizations to identify 12 Veterans or family members at risk for or having experience with lung cancer to form a Patient Advisory Council (PAC). The PAC met repeatedly from June 2018 to December 2020, both independently and jointly, with stakeholders representing clinicians, health administrators and researchers to identify relevant research topics. The PAC prioritized these topics and then identified questions within these areas where research was needed using an iterative process. Finally, they ranked the importance of obtaining answers to these questions. RESULTS: PAC members valued the co-learning generated by interactions with stakeholders, but emphasized the importance of facilitation to avoid stakeholders dominating the discussion. The PAC prioritized three broad research areas-(1) the impact of insurance on uptake of LDCT; (2) how best to inform Veterans about LDCT; and (3) follow-up and impact of screening results. Using these areas as guides, PAC members identified 20 specific questions, ranking as most important (1) innovative outreach methods, (2) the impact of screening on psychological health, and (3) the impact of outsourcing scans from VA to non-VA providers on completion of recommended follow-up of screening results. The latter two were not identified as high priority by the stakeholder group. CONCLUSIONS: We present an approach that facilitates co-learning between Veteran patients and providers, researchers and health system administrators to increase patient confidence in their ability to contribute important information to a research agenda. The research questions prioritized by the Veterans who participated in this project illustrate that for this new screening technology, patients are concerned about the practical details of implementation (e.g., follow-up) and the technology's impact on quality of life. PATIENT OR PUBLIC CONTRIBUTION: Veterans and Veteran advocates contributed to our research team throughout the entire research process, including conceiving and co-authoring this manuscript.