Lung Cancer Screening, Version 3.2018, NCCN Clinical Practice Guidelines in Oncology

Diagnostic value of peripheral blood eosinophils for benign and malignant pulmonary nodule

This retrospective study aims to assess the diagnostic utility of peripheral blood eosinophil counts in distinguishing between benign and malignant pulmonary nodules (PNs) prior to surgical intervention. We involved patients presenting with PNs measuring ≤30 mm as the primary CT imaging finding prior to surgical procedures at the General Hospital of Northern Theater Command in Shenyang, China, during the period spanning 2021 to 2022. Multivariable logistic regression analysis and receiver operator characteristic curve analysis, along with area under the curve (AUC) calculations, were used to determine the diagnostic value of eosinophil. A total of 361 patients with PN were included, consisting of 135 with benign PN and 226 with malignant PN. Multivariable logistic regression analysis showed that eosinophil percentage (OR = 1.909, 95% CI: 1.323-2.844, P < .001), absolute eosinophil value (OR = 0.001, 95% CI: 0.000-0.452, P = .033), tumor diameter (OR = 0.918, 95% CI: 0.877-0.959, P < .001), nodule type (OR = 0.227, 95% CI: 0.125-0.400, P < .001), sex (OR = 2.577, 95% CI: 1.554-4.329, P < .001), and age (OR = 0.967, 95% CI: 0.945-0.989, P = .004) were independently associated with malignant PN. The diagnostic value of regression model (AUC [95% CI]: 0.775 [0.725-0.825]; sensitivity: 74.3%; specificity: 71.1%) was superior to eosinophil percentage (AUC [95% CI]: 0.616 [0.556-0.677]; specificity: 66.8%; specificity: 51.1%) (Delong test: P < .001). Peripheral blood eosinophil percentage might be useful for early malignant PN diagnosis, and combining that with other characteristics might improve the diagnostic performance.

Historical Perspective on Lung Cancer Screening

Lung cancer represents a large burden on society with a staggering incidence and mortality rate that has steadily increased until recently. The impetus to design an effective screening program for the deadliest cancer in the United States and worldwide began in 1950. It has taken more than 50 years of numerous clinical trials and continued persistence to arrive at the development of modern-day screening program. As the program continues to grow, it is important for clinicians to understand its evolution, track outcomes, and continually assess the impact and bias of screening on the medical, social, and economic systems.

Machine Learning Study of SNPs in Noncoding Regions to Predict Non-small Cell Lung Cancer Susceptibility

Non-small cell lung cancer (NSCLC) is the most common pathological subtype of lung cancer. Both environmental and genetic factors have been reported to impact the lung cancer susceptibility. We conducted a genome-wide association study (GWAS) of 287 NSCLC patients and 467 healthy controls in a Chinese population using the Illumina Genome-Wide Asian Screening Array Chip on 712,095 SNPs (single nucleotide polymorphisms). Using logistic regression modeling, GWAS identified 17 new noncoding region SNP loci associated with the NSCLC risk, and the top three (rs80040741, rs9568547, rs6010259) were under a stringent p-value (<3.02e-6). Notably, rs80040741 and rs6010259 were annotated from the intron regions of MUC3A and MLC1, respectively. Together with another five SNPs previously reported in Chinese NSCLC patients and another four covariates (e.g., smoking status, age, low dose CT screening, sex), a predictive model by machine learning methods can separate the NSCLC from healthy controls with an accuracy of 86%. This is the first time to apply machine learning method in predicting the NSCLC susceptibility using both genetic and clinical characteristics. Our findings will provide a promising method in NSCLC early diagnosis and improve our understanding of applying machine learning methods in precision medicine.

MANet: Multi-branch attention auxiliary learning for lung nodule detection and segmentation

BACKGROUND AND OBJECTIVE

Pulmonary nodule detection and segmentation are currently two primary tasks in analyzing chest computed tomography (Chest CT) in order to detect signs of lung cancer, thereby providing early treatment measures to reduce mortality. Even though there are many proposed methods to reduce false positives for obtaining effective detection results, distinguishing between the pulmonary nodule and background region remains challenging because their biological characteristics are similar and varied in size. The purpose of our work is to propose a method for automatic nodule detection and segmentation in Chest CT by enhancing the feature information of pulmonary nodules.

METHODS

We propose a new UNet-based backbone with multi-branch attention auxiliary learning mechanism, which contains three novel modules, namely, Projection module, Fast Cascading Context module, and Boundary Enhancement module, to further enhance the nodule feature representation. Based on that, we build MANet, a lung nodule localization network that simultaneously detects and segments precise nodule positions. Furthermore, our MANet contains a Proposal Refinement step which refines initially generated proposals to effectively reduce false positives and thereby produce the segmentation quality.

RESULTS

Comprehensive experiments on the combination of two benchmarks LUNA16 and LIDC-IDRI show that our proposed model outperforms state-of-the-art methods in the tasks of nodule detection and segmentation tasks in terms of FROC, IoU, and DSC metrics. Our method reports an average FROC score of 88.11% in lung nodule detection. For the lung nodule segmentation, the results reach an average IoU score of 71.29% and a DSC score of 82.74%. The ablation study also shows the effectiveness of the new modules which can be integrated into other UNet-based models.

CONCLUSIONS

The experiments demonstrated our method with multi-branch attention auxiliary learning ability are a promising approach for detecting and segmenting the pulmonary nodule instances compared to the original UNet design.

Diagnostic performance of a deep learning-based method in differentiating malignant from benign subcentimeter (≤10 mm) solid pulmonary nodules

Background

This study assessed the diagnostic performance of a deep learning (DL)-based model for differentiating malignant subcentimeter (≤10 mm) solid pulmonary nodules (SSPNs) from benign ones in computed tomography (CT) images compared against radiologists with 10 and 15 years of experience in thoracic imaging (medium-senior seniority).

Methods

Overall, 200 SSPNs (100 benign and 100 malignant) were retrospectively collected. Malignancy was confirmed by pathology, and benignity was confirmed by follow-up or pathology. CT images were fed into the DL model to obtain the probability of malignancy (range, 0-100%) for each nodule. According to the diagnostic results, enrolled nodules were classified into benign, malignant, or indeterminate. The accuracy and diagnostic composition of the model were compared with those of the radiologists using the McNemar-Bowker test. Enrolled nodules were divided into 3-6-, 6-8-, and 8-10-mm subgroups. For each subgroup, the diagnostic results of the model were compared with those of the radiologists.

Results

The accuracy of the DL model, in differentiating malignant and benign SSPNs, was significantly higher than that of the radiologists (71.5% vs. 38.5%, P<0.001). The DL model reported more benign or malignant deterministic results and fewer indeterminate results. In subgroup analysis of nodule size, the DL model also yielded higher performance in comparison with that of the radiologists, providing fewer indeterminate results. The accuracy of the two methods in the 3-6-, 6-8-, and 8-10-mm subgroups was 75.5% vs. 28.3% (P<0.001), 62.0% vs. 28.2% (P<0.001), and 77.6% vs. 55.3% (P=0.001), respectively, and the indeterminate results were 3.8% vs. 66.0%, 8.5% vs. 66.2%, and 2.6% vs. 35.5% (all P<0.001), respectively.

Conclusions

The DL-based method yielded higher performance in comparison with that of the radiologists in differentiating malignant and benign SSPNs. This DL model may reduce uncertainty in diagnosis and improve diagnostic accuracy, especially for SSPNs smaller than 8 mm.

Case report: Clinical management of recurrent small cell lung cancer transformation complicated with lung cancer-induced acute pancreatitis after lung adenocarcinoma surgery

In the diagnosis and treatment of non-small cell lung cancer (NSCLC), the histological type may change from lung adenocarcinoma to lung squamous cell cancer or small cell lung cancer (SCLC). Pancreatic metastasis is extremely rare in advanced lung cancer, and pancreatitis characterized by lung cancer metastasis-induced acute pancreatitis (MIAP) is more rare. This paper reports in detail the clinical diagnosis and treatment of a female patient with lung adenocarcinoma who relapsed after radical surgery and progressed after multiple treatments. A second pathological biopsy revealed SCLC transformation, and the patient developed pancreatic metastasis and lung cancer MIAP during follow-up treatment. This paper mainly suggests that clinicians should pay attention to the possibility of pathological type transformation in the progression of advanced NSCLC, closely observe the dynamic changes of tumor markers and pay attention to the re-biopsy pathological analysis. In addition, it provides clinical experience and scientific reference for the discovery, diagnosis and treatment of transforming SCLC and lung cancer MIAP.

The influence of different sequences of vessel ligation on long-term survival during video-assisted thoracoscopic lobectomy for non-small cell lung cancer: A matched cohort study

In lobectomy of patients with lung cancer, the principle of operation is to cut off the pulmonary vein first, but it is often not taken seriously in clinical practice. We conducted this research to compare the impact of different sequences of pulmonary vessel ligation on the long-term survival of patients. This cohort study included 1239 patients treated surgically with video-assisted thoracoscopic lobectomy from January 2015 to December 2019 at The Second Affiliated Hospital of Nanchang University. Survival and perioperative indicators were compared between a Vein-first group (VF) and an artery-first group. After matching, 364 patients were included in each group for analysis. VF was associated with better overall survival (hazard ratio: 1.96 [1.4~2.74], P < .0001) and disease-free survival (hazard ratio: 1.65 [1.22~2.24], P = .0011). Meanwhile, the survival advantage of VF was achieved in almost all subgroups, particularly in the pathological tumor node metastasis stage I-II group and squamous cell carcinoma group. We obtained no significant differences in perioperative indications (operation time, hospital stay, etc) between VF and artery-first group. With better overall survival and disease-free survival, especially for pathological stage I-II squamous cell carcinoma, vein-first ligation should be strictly observed in lobectomy for patients with non-small cell lung cancer.

Survival after radiofrequency ablation and/or chemotherapy for lung cancer and pulmonary metastases: a systematic review and meta-analysis

Background

Radiofrequency ablation (RFA) and chemotherapy are used to treat lung cancer or pulmonary metastases, but no direct comparison of overall survival (OS) has been published. The present study aimed to assess the OS of RFA and/or chemotherapy in patients with lung cancer or pulmonary metastases who were not candidates for surgical resection.

Methods

To identify relevant studies, the following databases were electronically searched from their inception to 31 March 2023: PubMed, Embase, Web of Science, Cochrane Library, Scopus, Ovid, ScienceDirect, SinoMed, China National Knowledge Infrastructure Database, Chongqing VIP Chinese Science and Technology Periodical Database, Wanfang Database, LILACS, ClinicalTrials.gov, and Chictr.org. Manual retrieval was also conducted. We used published hazard ratios (HRs) if available or estimates from other survival data.

Results

A total of 1,387 participants from 14 trials were included in the final analysis. Patients treated with RFA combined with chemotherapy significantly improved OS compared with those treated with chemotherapy alone [HR 0.50, 95% confidence interval (CI) 0.41-0.61; p < 0.00001], with an absolute difference at 12 months of 29.6% (95% CI 23.7-35.5), at 24 months of 19.2% (95% CI 10.1-28.2), and at 36 months of 22.9% (95% CI 12.0-33.7). No statistically significant difference was observed in the subgroups of case type, cancer type, chemotherapy drugs, and tumor size. The HR for OS with RFA plus chemotherapy vs. RFA alone was 0.53 (95% CI 0.41-0.70; p < 0.00001), corresponding to a 27.1% (95% CI 18.3-35.8), 31.0% (95% CI 19.9-41.9), and 24.9% (95% CI 15.0-34.7) absolute difference in survival at 12 months, 24 months, and 36 months, respectively. Subgroup analysis by geographic region and TNM stage showed that RFA combined with chemotherapy still significantly improved OS compared to RFA. The HR of RFA vs. chemotherapy was 0.98 (95% CI 0.60-1.60; p = 0.94), with an absolute difference at 12 months of 1.4% (95% CI -19.2 to 22.1), at 24 months of 7.8% (95% CI -11.3 to 26.8), and at 36 months of 0.3% (95% CI -13.2 to 13.8). The overall indirect comparison of OS for RFA vs. chemotherapy was 0.95 (95% CI 0.72-1.26; p = 0.74). Data on progression-free survival were not sufficiently reported.

Conclusion

RFA combined with chemotherapy might be a better treatment option for patients with lung cancer or pulmonary metastases than chemotherapy alone or RFA alone. The comparison between RFA and/or chemotherapy remains to be specifically tested.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=335032, identifier CRD42022335032.

Lung Cancer Screening in Asia: An Expert Consensus Report

INTRODUCTION

The incidence and mortality of lung cancer are highest in Asia compared with Europe and USA, with the incidence and mortality rates being 34.4 and 28.1 per 100,000 respectively in East Asia. Diagnosing lung cancer at early stages makes the disease amenable to curative treatment and reduces mortality. In some areas in Asia, limited availability of robust diagnostic tools and treatment modalities, along with variations in specific health care investment and policies, make it necessary to have a more specific approach for screening, early detection, diagnosis, and treatment of patients with lung cancer in Asia compared with the West.

METHOD

A group of 19 advisors across different specialties from 11 Asian countries, met on a virtual Steering Committee meeting, to discuss and recommend the most affordable and accessible lung cancer screening modalities and their implementation, for the Asian population.

RESULTS

Significant risk factors identified for lung cancer in smokers in Asia include age 50 to 75 years and smoking history of more than or equal to 20 pack-years. Family history is the most common risk factor for nonsmokers. Low-dose computed tomography screening is recommended once a year for patients with screening-detected abnormality and persistent exposure to risk factors. However, for high-risk heavy smokers and nonsmokers with risk factors, reassessment scans are recommended at an initial interval of 6 to 12 months with subsequent lengthening of reassessment intervals, and it should be stopped in patients more than 80 years of age or are unable or unwilling to undergo curative treatment.

CONCLUSIONS

Asian countries face several challenges in implementing low-dose computed tomography screening, such as economic limitations, lack of efforts for early detection, and lack of specific government programs. Various strategies are suggested to overcome these challenges in Asia.

Motivation, acceptability and attitudes toward lung cancer screening among persons who attend a tobacco cessation program: A multicenter survey in Italy

This study aimed to evaluate smoking cessation (SC) motivation and the acceptability of a lung cancer screening (LCS) program with low-dose computed tomography (LDCT) among people who attend SC programs. A multicenter survey was conducted in the period January-December 2021 involving 197 people who attended group or individual SC courses in Reggio Emilia and Tuscany. Questionnaires, information sheets, and decision aids about the potential benefits and harms of LCS with LDCT were distributed at different time points during the course. The wish to protect own health (66%) was the most frequent reason given for quitting smoking, followed by cigarette dependence (40.6%) and current health problems (30.5%). Half of the participants (56%) considered periodic health checks including LDCT, as an advantageous activity. The great majority of participants were in favor of LCS (92%), with only 8% being indifferent, and no one was against these programs. Interestingly, those with sufficiently high smoking-related LC risk to be eligible for LCS and those attending the individual course were less in favor of LCS but also less concerned about the possible harms associated with LCS. The type of counseling was a significant predictor for both LCS acceptability and perceived harm of LCS. The favorable perception of LCS in people attending SC courses, despite the considerable preoccupation with potential harms, is an important finding of this study. Introducing a discussion on the benefits and harms of LCS in SC programs may prepare persons who smoke to make informed decisions on utilizing LCS.

Lung cancer screening adherence among people living with and without HIV: An analysis of an integrated health system in Florida, United States (2012-2021)

Although lung cancer is a leading cause of death among people living with HIV (PLWH), limited research exists characterizing real-world lung cancer screening adherence among PLWH. Our objective was to compare low-dose computed tomography (LDCT) adherence among PLWH to those without HIV treated at one integrated health system. Using the University of Florida's Health Integrated Data Repository (01/01/2012-10/31/2021), we identified PLWH with at least one LDCT procedure, using Current Procedural Terminology codes(S8032/G0297/71271). Lung cancer screening adherence was defined as a second LDCT based on the Lung Imaging Reporting and Data System (Lung-RADS®). Lung-RADS categories were extracted from radiology reports using a natural language processing system. PLWH were matched with 4 randomly selected HIV-negative patients based on (+/- 1 year) age, Lung-RADS category, and calendar year. Seventy-three PLWH and 292 matched HIV-negative adults with at least one LDCT were identified. PLWH were more likely to be male (66% vs.52%,p < 0.04), non-Hispanic Black (53% vs.23%,p < 0.001), and live in an area of high poverty (45% vs.31%,p < 0.001). PLWH were more likely to be diagnosed with lung cancer after first LDCT (8% vs.0%,p < 0.001). Seventeen percent of HIV-negative and 12% of PLWH were adherent to LDCT screenings. Only 25% of PLWH diagnosed with category 4A were adherent compared to 44% of HIV-negative. On multivariable analyses, those with older age (66-80 vs.50-64 years) and with either Medicaid, charity-based, or other government insurance (vs. Medicare) were less likely to be adherent to LDCT screenings. PLWH may have poorer adherence to LDCT compared to their HIV-negative counterparts.

Machine learning prediction models for different stages of non-small cell lung cancer based on tongue and tumor marker: a pilot study

OBJECTIVE

To analyze the tongue feature of NSCLC at different stages, as well as the correlation between tongue feature and tumor marker, and investigate the feasibility of establishing prediction models for NSCLC at different stages based on tongue feature and tumor marker.

METHODS

Tongue images were collected from non-advanced NSCLC patients (n = 109) and advanced NSCLC patients (n = 110), analyzed the tongue images to obtain tongue feature, and analyzed the correlation between tongue feature and tumor marker in different stages of NSCLC. On this basis, six classifiers, decision tree, logistic regression, SVM, random forest, naive bayes, and neural network, were used to establish prediction models for different stages of NSCLC based on tongue feature and tumor marker.

RESULTS

There were statistically significant differences in tongue feature between the non-advanced and advanced NSCLC groups. In the advanced NSCLC group, the number of indexes with statistically significant correlations between tongue feature and tumor marker was significantly higher than in the non-advanced NSCLC group, and the correlations were stronger. Support Vector Machine (SVM), decision tree, and logistic regression among the machine learning methods performed poorly in models with different stages of NSCLC. Neural network, random forest and naive bayes had better classification efficiency for the data set of tongue feature and tumor marker and baseline. The models' classification accuracies were 0.767 ± 0.081, 0.718 ± 0.062, and 0.688 ± 0.070, respectively, and the AUCs were 0.793 ± 0.086, 0.779 ± 0.075, and 0.771 ± 0.072, respectively.

CONCLUSIONS

There were statistically significant differences in tongue feature between different stages of NSCLC, with advanced NSCLC tongue feature being more closely correlated with tumor marker. Due to the limited information, single data sources including baseline, tongue feature, and tumor marker cannot be used to identify the different stages of NSCLC in this pilot study. In addition to the logistic regression method, other machine learning methods, based on tumor marker and baseline data sets, can effectively improve the differential diagnosis efficiency of different stages of NSCLC by adding tongue image data, which requires further verification based on large sample studies in the future.

The presence of autoantibodies is associated with improved overall survival in lung cancer patients

Objective

Autoantibodies have been reported to be associated with cancers. As a biomarker, autoantibodies have been widely used in the early screening of lung cancer. However, the correlation between autoantibodies and the prognosis of lung cancer patients is poorly understood, especially in the Asian population. This retrospective study investigated the association between the presence of autoantibodies and outcomes in patients with lung cancer.

Methods

A total of 264 patients diagnosed with lung cancer were tested for autoantibodies in Henan Provincial People's Hospital from January 2017 to June 2022. The general clinical data of these patients were collected, and after screening out those who met the exclusion criteria, 151 patients were finally included in the study. The Cox proportional hazards model was used to analyze the effect of autoantibodies on the outcomes of patients with lung cancer. The Kaplan-Meier curve was used to analyze the relationship between autoantibodies and the overall survival of patients with lung cancer.

Results

Compared to lung cancer patients without autoantibodies, those with autoantibodies had an associated reduced risk of death (HRs: 0.45, 95% CIs 0.27~0.77), independent of gender, age, smoking history, pathological type, and pathological stage of lung cancer. Additionally, the association was found to be more significant by subgroup analysis in male patients, younger patients, and patients with small cell lung cancer. Furthermore, lung cancer patients with autoantibodies had significantly longer survival time than those without autoantibodies.

Conclusion

The presence of autoantibodies is an independent indicator of good prognosis in patients with lung cancer, providing a new biomarker for prognostic evaluation in patients with lung cancer.

Optimizing Lung Cancer Screening With Risk Prediction: Current Challenges and the Emerging Role of Biomarkers

The Oncology Grand Rounds series is designed to place original reports published in the Journal into clinical context. A case presentation is followed by a description of diagnostic and management challenges, a review of the relevant literature, and a summary of the authors' suggested management approaches. The goal of this series is to help readers better understand how to apply the results of key studies, including those published in Journal of Clinical Oncology, to patients seen in their own clinical practice.Lung cancer screening has been demonstrated to reduce lung cancer mortality, but its benefits must be weighed against the potential harms of unnecessary procedures, false-positive radiological findings, and overdiagnosis. Individuals at highest risk of lung cancer are more likely to maximize benefits while minimizing harm from screening. Although current lung cancer screening guidelines recommended by the US Preventive Services Task Force (USPSTF) only consider age and smoking history for screening eligibility, National Comprehensive Cancer Network and other society guidelines recommend screening on the basis of individualized risk assessment including family history, environmental exposures, and presence of chronic lung disease. Risk prediction models have been developed to integrate various risk factors into an individualized risk prediction score. Previous evidence showed that risk prediction model-based screening eligibility could improve sensitivity for detecting lung cancer cases without reducing specificity. Furthermore, recent advances in lung cancer biomarkers have enhanced the performance of risk prediction in identifying lung cancer cases relative to the USPSTF criteria. These risk prediction models can be used to guide shared decision-making discussions before proceeding with lung cancer screening. This study aims to provide a concise overview of these prediction models and the emerging role of biomarker testing in risk prediction to facilitate conversations with patients. The goal was to assist clinicians in assessing individual patient risk, leading to more informed decision making.

Occupational Contributions to Respiratory Health Disparities

Occupation is an important contributor to disparities in respiratory disease, affecting financial status, health-care access, and exposure to hazardous substances. Although occupation and associated exposures are included in the socioecological models, work exposures remain persistently absent from research on health inequities and their contribution to health. This article focuses on the occupational contribution to disparities in asthma, chronic obstructive pulmonary disease, silicosis, coronavirus disease 2019, and lung cancer. Because occupational exposures are largely preventable through proper workplace controls, the recognition of occupational causes of disease can provide an opportunity for interventions to bring about health equity.

A two-stage genome-wide association study identified four potential early-onset nonsmall cell lung cancer risk loci based on 26,652 participants in Chinese population

Early-onset lung cancer is rare with an increasing incidence rate. Although several genetic variants have been identified for it with candidate gene approaches, no genome-wide association study (GWAS) has been reported. In this study, a two-stage strategy was adopted: firstly we performed a GWAS to identify variants associated with early-onset nonsmall-cell lung cancer (NSCLC) risk using 2556 cases (age ≤ 50 years) and 13,327 controls by logistic regression model. To further discriminate younger cases from older ones, we took a case-case analysis for the promising variants with above early-onset cases and 10,769 cases (age > 50 years) by Cox regression model. After combining these results, we identified four early-onset NSCLC susceptibility loci at 5p15.33 (rs2853677, odds ratio [OR] = 1.48, 95% confidence interval [CI]: 1.36-1.60, Pcase-control  = 3.58 × 10-21 ; hazard ratio [HR] = 1.10, 95% CI: 1.04-1.16, Pcase-case  = 6.77 × 10-4 ), 5p15.1 (rs2055817, OR = 1.24, 95% CI: 1.15-1.35, Pcase-control  = 1.39 × 10-7 ; HR = 1.08, 95% CI: 1.02-1.14, Pcase-case  = 6.90 × 10-3 ), 6q24.2 (rs9403497, OR = 1.24, 95% CI: 1.15-1.35, Pcase-control  = 1.61 × 10-7 ; HR = 1.11, 95% CI: 1.05-1.17, Pcase-case  = 3.60 × 10-4 ) and 12q14.3 (rs4762093, OR = 1.31, 95% CI: 1.18-1.45, Pcase-control  = 1.90 × 10-7 ; HR = 1.10, 95% CI: 1.03-1.18, Pcase-case  = 7.49 × 10-3 ). Except for 5p15.33, other loci were found to be associated with NSCLC risk for the first time. All of them had stronger effects in younger patients than in older ones. These results provide a promising overview for early-onset NSCLC genetics.

Lung Cancer Screening Adherence Among People with HIV Treated at an Integrated Health System in Florida

We evaluated low-dose computed tomography (LDCT) adherence among people with HIV (PWH) treated at University of Florida (UF). From the UF Health Integrated Data Repository, we identified PWH who underwent at least one LDCT procedure (January 1, 2012-October 31, 2021). Lung cancer screening adherence was defined as having a second LDCT within recommended observation window, based on the Lung Imaging Reporting and Data System (Lung-RADS®). We identified 73 PWH with a history of at least one LDCT. PWH were mostly male (66%), non-Hispanic Black (53%), and living in urban (86%), high poverty (45%) areas. Almost 1 in 10 of PWH were diagnosed with lung cancer after their first LDCT. Overall, 48% and 41% of PWH were diagnosed with Lung-RADS categories 1 and 2, respectively. We observed that 12% of PWH were adherent to LDCT. Only 25% of PWH diagnosed with category 4A were adherent. PWH may have poor adherence to lung cancer screening.

A Multi-institutional Analysis of the Combined Effect of Micropapillary Component and Consolidation-to-Tumor Ratio >0.5 on the Prognosis of Pathological, Stage IA3, Lung Adenocarcinoma

INTRODUCTION

The study investigated the synergistic effect of the micropapillary (MIP) component and consolidation-to-tumor ratio (CTR) on the recurrence and survival of patients with pathologic stage IA3 lung adenocarcinoma.

METHODS

We enrolled 419 patients confirmed pathological stage IA3 adenocarcinoma from four institutions. Kaplan-Meier analysis was performed to examine the value of the MIP component and CTR on relapse-free survival (RFS) and overall survival (OS). The cumulative recurrence between different stages was analyzed by using cumulative event curves.

RESULTS

RFS (P < 0.0001) and OS (P = 0.008) in the presence of the MIP group were significantly lower than those in the absence of the MIP group, and CTR > 5 only reduced RFS (P = 0.0004), but not OS (P = 0.063), in the patients. In addition, the prognosis of patients with both the MIP component and CTR > 5 was worse than that of those without the MIP component or CTR ≤ 5. Therefore, we established new subtypes of the stage IA3: IA3a, IA3b, and IA3c. RFS and OS for IA3c staging were significantly lower than those for IA3a and IA3b. For IA3c, the cumulative incidence of local recurrence (P < 0.001) and that of distant metastasis (P = 0.004) were significantly higher than those for IA3a and IA3b.

CONCLUSIONS

The MIP component combined with CTR > 0.5 can effectively predict the prognosis of patients with pathological stage IA3 lung adenocarcinoma and may offer more detailed recurrence and survival information according to the established subtype stage of IA3.

Lung Cancer Screening: Low-Dose Thoracic Computed Tomography Performed in a High-Risk Portuguese Population

INTRODUCTION

The Urgeiriça mines were once the main uranium producer in Portugal. The aim of this study was to estimate the benefit of low-dose chest computed tomography (LDCT) for lung cancer screening in former miners that were considered as being at high-risk.

METHODS

A subgroup of former miners of the Uranium National Company exposed to uranium and with a smoking load greater than 20 pack-years, agreed to perform a LDCT. The Fleischner Society Guidelines were used to classify the nodules and establish follow-up.

RESULTS

Initially, 265 former employees of the Uranium National Company were included. The mean time of employment was 15 (0 - 45) years. The non-smokers represented 50.9% and 30.2% were ever smokers; the remaining chose not to respond. One diagnosis of lung cancer was initially made. In the second phase, a subgroup of 66 former miner underwent a LDCT, 37 of whom presented pulmonary nodules. Most computed tomography (CT) scans revealed one single nodule (n = 13) and the mean size was 5 (1 - 16) mm. A suspicious 16 mm spiculated nodule was evaluated with PET/CT, and percutaneous and surgical biopsies, ultimately revealing a benign lesion.

CONCLUSION

The data highlights the importance of lung cancer screening in high-risk populations. This was, to the best of our knowledge, the first study performed in Portugal and can act as a bridge towards a wider implementation in the country.

Capability for dose reduction while maintaining nodule detection: Comparison of silver and copper X-ray spectrum modulation filters for chest CT using a phantom study with different reconstruction methods

PURPOSE

To compare the capability of CTs obtained with a silver or copper x-ray beam spectral modulation filter (Ag filter and Cu filter) and reconstructed with FBP, hybrid-type IR and deep learning reconstruction (DLR) for radiation dose reduction for lung nodule detection using a chest phantom study.

MATERIALS AND METHODS

A chest CT phantom was scanned with a 320-detector row CT with Ag filter at 0.6, 1.6 and 2.5 mGy and Cu filters at 0.6, 1.6, 2.5 and 9.6 mGy, and reconstructed with the aforementioned methods. To compare image quality of all the CT data, SNRs and CNRs for any nodule were calculated for all protocols. To compare nodule detection capability among all protocols, the probability of detection of any nodule was assessed with a 5-point visual scoring system. Then, ROC analyses were performed to compare nodule detection capability of Ag and Cu filters for each radiation dose data with the same method and of the three methods for any radiation dose data and obtained with either filter.

RESULTS

At any of the doses, SNR, CNR and area under the curve for the Ag filter were significantly higher or larger than those for the Cu filter (p < 0.05). Moreover, with DLR, those values were significantly higher or larger than all the others for CTs obtained with any of the radiation doses and either filter (p < 0.05).

CONCLUSION

The Ag filter and DLR can significantly improve image quality and nodule detection capability compared with the Cu filter and other reconstruction methods at each of radiation doses used.

Artificial Intelligence in Lung Cancer Screening: The Future Is Now

Lung cancer has one of the worst morbidity and fatality rates of any malignant tumour. Most lung cancers are discovered in the middle and late stages of the disease, when treatment choices are limited, and patients' survival rate is low. The aim of lung cancer screening is the identification of lung malignancies in the early stage of the disease, when more options for effective treatments are available, to improve the patients' outcomes. The desire to improve the efficacy and efficiency of clinical care continues to drive multiple innovations into practice for better patient management, and in this context, artificial intelligence (AI) plays a key role. AI may have a role in each process of the lung cancer screening workflow. First, in the acquisition of low-dose computed tomography for screening programs, AI-based reconstruction allows a further dose reduction, while still maintaining an optimal image quality. AI can help the personalization of screening programs through risk stratification based on the collection and analysis of a huge amount of imaging and clinical data. A computer-aided detection (CAD) system provides automatic detection of potential lung nodules with high sensitivity, working as a concurrent or second reader and reducing the time needed for image interpretation. Once a nodule has been detected, it should be characterized as benign or malignant. Two AI-based approaches are available to perform this task: the first one is represented by automatic segmentation with a consequent assessment of the lesion size, volume, and densitometric features; the second consists of segmentation first, followed by radiomic features extraction to characterize the whole abnormalities providing the so-called "virtual biopsy". This narrative review aims to provide an overview of all possible AI applications in lung cancer screening.

Therapeutic Implications of PTEN in Non-Small Cell Lung Cancer

Lung cancer remains one of the major human malignancies affecting both men and women worldwide, with non-small cell lung cancer (NSCLC) being the most prevalent type. Multiple mechanisms have been identified that favor tumor growth as well as impede the efficacy of therapeutic regimens in lung cancer patients. Among tumor suppressor genes that play critical roles in regulating cancer growth, the phosphatase and tensin homolog (PTEN) constitutes one of the important family members implicated in controlling various functional activities of tumor cells, including cell proliferation, apoptosis, angiogenesis, and metastasis. Notably, clinical studies have also documented that lung tumors having an impaired, mutated, or loss of PTEN are associated with low survival or high tumor recurrence rates. To that end, PTEN has been explored as a promising target for anti-cancer agents. Importantly, the ability of PTEN to crosstalk with several signaling pathways provides new approaches to devise effective treatment options for lung cancer treatment. The current review highlights the significance of PTEN and its implications in therapeutic approaches against NSCLC.

Efficacy of lung cancer screening at the American University of Beirut Medical Center

Introduction

In Lebanon, a dedicated screening program for lung cancer is absent. Screening is largely based on the recommendation of an informed physician or the initiative of a patient. To better understand the situation, it is important to look at the available data on patients currently being screened for lung cancer in this country. Our aim in this study is to review the data and compare it with that in the literature as well as to assess the efficacy of the screening process followed.

Methods

Our study accessed the electronic medical records of patients at the American University of Beirut Medical Center (AUBMC), a tertiary care center in Lebanon. We collected information on patients who underwent screening low-dose computed tomography (LDCT) scan between June 2019 and June 2021 inclusive. Records of all patients who underwent a non-contrast computed tomography (CT) scan at AUBMC during this period were collected and analyzed.

Results

On average, our population had a 52.6 pack-year smoking history. Moreover, 47% of our population had an accurate pack-year reported, while 12% did not have enough information to even estimate their pack-year history. When looking at the accurate and estimated data, 5% of our population did not even meet the ≥20 pack-year smoking history. Eight patients had positive findings on the screening LDCT, which we defined as suspicious findings that require further workup (e.g., PET/CT or biopsy) or other significant incidental findings.

Conclusion

A well-organized program for lung cancer screening in Lebanon is absent. Screening largely depends on the initiative of the physician or the patient. We were able to uncover multiple flaws in the screening method used, including poor documentation and follow-up. Although the screening method adopted retained some benefits in terms of detecting early malignancy, it lacked proper organization and was not ideal. A better, systematized screening program is needed to have optimal outcomes.

ACR Lung CT Screening Reporting and Data System, a Systematic Review and Meta-Analysis Before Change in US Preventative Services Taskforce Eligibility Criteria: 2014 to 2021

OBJECTIVE

To review Lung CT Screening Reporting and Data System (Lung-RADS) scores from 2014 to 2021, before changes in eligibility criteria proposed by the US Preventative Services Taskforce.

METHODS

A registered systematic review and meta-analysis was conducted in MEDLINE, Embase, CINAHL, and Web of Science in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines; eligible studies examined low-dose CT (LDCT) lung cancer screening at institutions in the United States and reported Lung-RADS from 2014 to 2021. Patient and study characteristics, including age, gender, smoking status, pack-years, screening timeline, number of individual patients, number of unique studies, Lung-RADS scores, and positive predictive value (PPV) were extracted. Meta-analysis estimates were derived from generalized linear mixed modeling.

RESULTS

The meta-analysis included 24 studies yielding 36,211 LDCT examinations for 32,817 patient encounters. The meta-analysis Lung-RADS 1-2 scores were lower than anticipated by ACR guidelines, at 84.4 (95% confidence interval [CI] 83.3-85.6) versus 90% respectively (P < .001). Lung-RADS 3 and 4 scores were both higher than anticipated by the ACR, at 8.7% (95% CI 7.6-10.1) and 6.5% (95% CI 5.707.4), compared with 5% and 4%, respectively (P < .001). The ACR's minimum estimate of PPV for Lung-RADS 3 to 4 is 21% or higher; we observed a rate of 13.1% (95% CI 10.1-16.8). However, our estimated PPV rate for Lung-RADS 4 was 28.6% (95% CI 21.6-36.8).

CONCLUSION

Lung-RADS scores and PPV rates in the literature are not aligned with the ACR's own estimates, suggesting that perhaps Lung-RADS categorization needs to be reexamined for better concordance with real-world screening populations. In addition to serving as a benchmark before screening guideline broadening, this study provides guidance for future reporting of lung cancer screening and Lung-RADS data.

Multi-Omic Biomarkers Improve Indeterminate Pulmonary Nodule Malignancy Risk Assessment

The clinical management of patients with indeterminate pulmonary nodules is associated with unintended harm to patients and better methods are required to more precisely quantify lung cancer risk in this group. Here, we combine multiple noninvasive approaches to more accurately identify lung cancer in indeterminate pulmonary nodules. We analyzed 94 quantitative radiomic imaging features and 41 qualitative semantic imaging variables with molecular biomarkers from blood derived from an antibody-based microarray platform that determines protein, cancer-specific glycan, and autoantibody-antigen complex content with high sensitivity. From these datasets, we created a PSR (plasma, semantic, radiomic) risk prediction model comprising nine blood-based and imaging biomarkers with an area under the receiver operating curve (AUROC) of 0.964 that when tested in a second, independent cohort yielded an AUROC of 0.846. Incorporating known clinical risk factors (age, gender, and smoking pack years) for lung cancer into the PSR model improved the AUROC to 0.897 in the second cohort and was more accurate than a well-characterized clinical risk prediction model (AUROC = 0.802). Our findings support the use of a multi-omics approach to guide the clinical management of indeterminate pulmonary nodules.

Effectiveness of smoking cessation on the high-risk population of lung cancer with early screening: a systematic review and meta-analysis of randomized controlled trials until January 2022

BACKGROUND

Lung cancer has always been the malignant tumor with the highest incidence rate. Smoking is the most important risk factor for lung cancer. Although potential positive effects of smoking cessation interventions on the high-risk population of lung cancer have been observed, evidence of its definitive effect remains uncertain. This study aimed to summarize the evidence related to the effects and safety of smoking cessation interventions for the high-risk population of lung cancer.

METHODS

A systematic literature search was conducted through the following seven databases: PubMed, Embase, Web of Science, CENTRAL, CINAHL, PsycINFO, and Science Direct. Screening and assessment for risk of bias were conducted by two independent reviewers. Meta-analysis was performed for the 7-day-point prevalence of smoking abstinence and continuous smoking abstinence using RevMan 5.3 software.

RESULTS

Meta-analysis results show that in the 7-day-point prevalence of smoking abstinence (by patient-reported outcome): individualized intervention was significantly higher than that of the standard care [RR = 1.46, 95%CI = (1.04,2.06), P < 0.05]. Moreover, the smoking cessation interventions were significantly elevated than that of standard care [RR = 1.58, 95%CI = (1.12, 2.23), P < 0.05] within 1-6 month follow-up time. In line with the findings in cigarette smoking, the continuous smoking abstinence of E-cigarettes (biochemical verified): E-cigarettes were significantly higher than that of the standard care [RR = 1.51, 95%CI = (1.03, 2.21), P < 0.05], and within 1-6 month follow-up time, the smoking cessation interventions were significantly greater than that of standard care [RR = 1.51, 95%CI = (1.03, 2.21), P < 0.05]. Publication bias was detected possibly.

CONCLUSIONS

The results of this systematic review show that smoking cessation intervention is effective for long-term lung cancer high-risk smokers who participate in early screening, of which E-cigarettes are the best, followed by individual smoking cessation.

TRIAL REGISTRATION

A review protocol was developed and registered in the International Prospective Register of Systematic Reviews (PROSPERO).

TRIAL REGISTRATION

CRD42019147151. Registered 23 June 2022.

Lung Cancer Screening Among U.S. Military Veterans by Health Status and Race and Ethnicity, 2017-2020: A Cross-Sectional Population-Based Study

Introduction

Veterans are at high risk for lung cancer and are an important group for lung cancer screening. Previous research suggests that lung cancer screening may not be reaching healthier and/or non-White individuals, who stand to benefit most from lung cancer screening. We sought to test whether lung cancer screening is associated with poor health and/or race and ethnicity among veterans.

Methods

This cross-sectional, population-based study included veterans eligible for lung cancer screening (aged 55-79 years, ≥30 pack-year smoking history, current smokers or quit within 15 years, no previous lung cancer) in the 2017-2020 Behavioral Risk Factor Surveillance System surveys. Exposures were (1) poor health, defined as fair/poor health status and difficulty walking or climbing stairs, aligning with eligibility criteria for a pivotal lung cancer screening trial, and (2) race/ethnicity. The outcome was a receipt of lung cancer screening. All variables were self-reported.

Results

Of 3,376 lung cancer screening-eligible veterans representing an underlying population of 866,000 individuals, 20.3% (95% CI=17.3, 23.6) had poor health, and 13.7% (95% CI=10.6, 17.5) identified as non-White. Poor health was strongly associated with lung cancer screening (adjusted RR=1.64, 95% CI=1.06, 2.27); one third of veterans screened for lung cancer would not qualify for a pivotal lung cancer screening trial in terms of health. Marked racial disparities were observed among veterans: after adjustment, non-White veterans were 67% less likely to report lung cancer screening than White veterans (adjusted RR=0.33, 95% CI=0.11, 0.66).

Conclusions

Lung cancer screening is correlated with poorer health and White race/ethnicity among veterans, which may undermine its population-level effectiveness. These results highlight the need to promote lung cancer screening, especially for healthier and/or non-White veterans, an important group of Americans for lung cancer screening.

Reliability and Validity of the Korean Version of Lung Cancer Screening Health Belief Scale

The purpose of this study was to verify the validity and reliability of the LCSHBS-K. This was a methodological study. The participants were adults aged between 50 and 74 years old, according to the selection criteria for lung cancer screening presented by the Comprehensive Cancer Network clinical practice guidelines in oncology recommendations. This study included 204 high-risk individuals who had not been diagnosed with lung cancer. The collected data were analyzed using the IBM SPSS Statistics software 26.0 version (IBM, New York, NY, USA). The reliability was analyzed by Cronbach's α for internal consistency, and the concurrent validity was analyzed by Pearson's correlation coefficients to identify the correlations with the health belief scale for Korean adults. To test the convergent validity, the average variance extracted (AVE) and composite reliability (CR) were calculated using confirmatory factor analysis. In addition, the model fit for a tool was CMIN (χ²/df), SRMR, RMSEA, GFI, and CFI as a comparative fit index. The discriminant validity was tested based on AVE > r². The average age of the participants was 55.49 (SD = 5.07), the average smoking history was 29.55 (SD = 8.12) years, and the average number of cigarettes smoked per day was 12.18 (SD = 7.77). The goodness of fit met the criteria with GFI = 0.81 (criteria > 0.9), CMIN = 1.69 (criteria < 2), SRMR = 0.06 (criteria < 0.08), RMSEA = 0.058 (criteria < 0.06), and CFI = 0.91 (criteria > 0.9). The LCSHBS-K showed a statistically significant positive correlation with the HBS (r = 0.32 (p < 0.001)). Cronbach's α was 0.80 for all the items in the LCSHBS-K. Therefore, the validity and reliability of the LCSHBS-K tool were confirmed. Based on the results of this study, the Korean version of the LCSHBS tool was found to be suitable for screening lung cancer in high-risk groups in Korea.

Improving the efficiency of identifying malignant pulmonary nodules before surgery via a combination of artificial intelligence CT image recognition and serum autoantibodies

OBJECTIVE

To construct a new pulmonary nodule diagnostic model with high diagnostic efficiency, non-invasive and simple to measure.

METHODS

This study included 424 patients with radioactive pulmonary nodules who underwent preoperative 7-autoantibody (7-AAB) panel testing, CT-based AI diagnosis, and pathological diagnosis by surgical resection. The patients were randomly divided into a training set (n = 212) and a validation set (n = 212). The nomogram was developed through forward stepwise logistic regression based on the predictive factors identified by univariate and multivariate analyses in the training set and was verified internally in the verification set.

RESULTS

A diagnostic nomogram was constructed based on the statistically significant variables of age as well as CT-based AI diagnostic, 7-AAB panel, and CEA test results. In the validation set, the sensitivity, specificity, positive predictive value, and AUC were 82.29%, 90.48%, 97.24%, and 0.899 (95%[CI], 0.851-0.936), respectively. The nomogram showed significantly higher sensitivity than the 7-AAB panel test result (82.29% vs. 35.88%, p < 0.001) and CEA (82.29% vs. 18.82%, p < 0.001); it also had a significantly higher specificity than AI diagnosis (90.48% vs. 69.04%, p = 0.022). For lesions with a diameter of ≤ 2 cm, the specificity of the Nomogram was higher than that of the AI diagnostic system (90.00% vs. 67.50%, p = 0.022).

CONCLUSIONS

Based on the combination of a 7-AAB panel, an AI diagnostic system, and other clinical features, our Nomogram demonstrated good diagnostic performance in distinguishing lung nodules, especially those with ≤ 2 cm diameters.

KEY POINTS

• A novel diagnostic model of lung nodules was constructed by combining high-specific tumor markers with a high-sensitivity artificial intelligence diagnostic system. • The diagnostic model has good diagnostic performance in distinguishing malignant and benign pulmonary nodules, especially for nodules smaller than 2 cm. • The diagnostic model can assist the clinical decision-making of pulmonary nodules, with the advantages of high diagnostic efficiency, noninvasive, and simple measurement.

Ten-year trends of the clinicopathological characteristics, surgical treatments and survival outcomes of operable lung cancer patients in monocenter: a retrospective cohort study

Background

Lung cancer is one of the cancers with the highest morbidity and mortality. During the last decade, the trends of clinical characteristics, surgical treatments and survival of lung cancer patients in China have remained unclear.

Methods

All lung cancer patients operated on from 2011 to 2020 were identified in a prospectively maintained database of Sun Yat-sen University Cancer Center.

Results

A total of 7,800 lung cancer patients were included in this study. Within the past 10 years, the average age at diagnosis of the patients remained stable, the proportion of asymptomatic, female and nonsmoking patients increased, and the average tumor size decreased from 3.766 to 2.300 cm. In addition, the proportion of early stage and adenocarcinoma increased, while that of squamous cell carcinoma decreased. Among the patients, the proportion of patients having video-assisted thoracic surgery increased. More than 80% of the patients underwent lobectomy and systematic nodal dissection over the 10 years. Additionally, both the average postoperative length of stay and 1-, 3-, and 6-month postoperative mortality decreased. Moreover, the 1-, 3-, and 5-year overall survival (OS) rates of all the operable patients increased from 89.8, 73.9, and 63.8% to 99.6, 90.7, and 80.8%, respectively. The 5-year OS rates of the patients with stage I, II, and III lung cancer were 87.6, 79.9, and 59.9%, respectively, which were higher than those in other published data.

Conclusion

There were significant changes in the clinicopathological characteristics, surgical treatments and survival outcomes of the patients with operable lung cancer from 2011 to 2020.

Facilitating combined biopsy and percutaneous microwave ablation of pulmonary ground-glass opacities using lipiodol localisation

OBJECTIVES

Whether preoperative localisation is necessary and valuable for the microwave ablation (MWA) of small pulmonary lesions with ground-glass opacity (GGO) remains unclear. This study aimed to explore the role of the Chiba needle and lipiodol localisation techniques in facilitating MWA and biopsy.

METHODS

This retrospective before-after study included patients with GGOs who underwent conventional MWA and biopsy treatment in our hospital between January 2018 and December 2019 (group A) or who underwent the Chiba needle and lipiodol localisation treatment before MWA and biopsy between January 2020 and December 2020 (group B). The characteristics of each patient and GGO lesion were collected and analysed to evaluate the safety and effectiveness of the localisation technique.

RESULTS

A total of 122 patients with 152 GGOs and 131 patients with 156 GGOs underwent MWA and biopsy in groups A and B, respectively. The primary technique efficacy rate of MWA differed significantly between the two groups (A vs. B: 94.1% vs. 99.4%; p = 0.009). The positive biopsy rate in the two groups was determined by the difference (A vs. B: 93.4% vs. 98.1%; p = 0.042). The incidence of complications did not increase in group B.

CONCLUSIONS

Compared with the unmarked group, the Chiba needle and lipiodol localisation technique improved the positive rate of biopsy and the initial effective rate of MWA, without significantly increasing the complication rate.

KEY POINTS

• The localisation of the Chiba needle and lipiodol could improve the positive biopsy rate and the initial effective rate of MWA. • The localisation of the Chiba needle and lipiodol does not affect the subsequent MWA and biopsy and does not increase the incidence of pneumothorax and haemorrhage.

Low-dose versus standard-dose computed tomography-guided biopsy for pulmonary nodules: a randomized controlled trial

BACKGROUND

To assess relative safety and diagnostic performance of low- and standard-dose computed tomography (CT)-guided biopsy for pulmonary nodules (PNs).

MATERIALS AND METHODS

This was a single-center prospective randomized controlled trial (RCT). From June 2020 to December 2020, consecutive patients with PNs were randomly assigned into low- or standard-dose groups. The primary outcome was diagnosis accuracy. The secondary outcomes included technical success, diagnostic yield, operation time, radiation dose, and biopsy-related complications. This RCT was registered on 3 January 2020 and listed within ClinicalTrials.gov (NCT04217655).

RESULTS

Two hundred patients were randomly assigned to low-dose (n = 100) and standard-dose (n = 100) groups. All patients achieved the technical success of CT-guided biopsy and definite final diagnoses. No significant difference was found in operation time (n = 0.231) between the two groups. The mean dose-length product was markedly reduced within the low-dose group compared to the standard-dose group (31.5 vs. 333.5 mGy-cm, P < 0.001). The diagnostic yield, sensitivity, specificity, and accuracy of the low-dose group were 68%, 91.5%, 100%, and 94%, respectively. The diagnostic yield, sensitivity, specificity, and accuracy were 65%, 88.6%, 100%, and 92% in the standard-dose group. There was no significant difference observed in diagnostic yield (P = 0.653), diagnostic accuracy (P = 0.579), rates of pneumothorax (P = 0.836), and lung hemorrhage (P = 0.744) between the two groups.

CONCLUSIONS

Compared with standard-dose CT-guided biopsy for PNs, low-dose CT can significantly reduce the radiation dose, while yielding comparable safety and diagnostic accuracy.

Shall We Screen Lung Cancer With Low-Dose Computed Tomography? Cost-Effectiveness in Hungary

OBJECTIVES

Clinical data and cost-effectiveness analyses from several countries support the use of low-dose computed tomography (LDCT) to screen patients with high risk of lung cancer (LC). This study aimed to explore the economic value of screening LC with LDCT in Hungary.

METHODS

Cohorts of screened and nonscreened subjects were simulated in a decision analytic model over their lifetime. Five steps in the patient trajectory were distinguished: no LC, nondiagnosed LC, screening, diagnosed LC, and post-treatment. Patient pathways were populated based on the Hungarian pilot study of screening, the Nederlands-Leuvens Longkanker Screenings Onderzoek (NELSON) LC screening trial, and local incidence and prevalence data. Healthcare costs were obtained from the National Health Insurance Fund. Utility data were obtained from international sources and adjusted to local tariffs. Scenarios according to screening frequency, age bands (50-74, 55-74 years), and smoking status were analyzed.

RESULTS

Annual LDCT-based screening compared with no screening for 55- to 74-year-old current smokers showed 0.031 quality-adjusted life-year (QALY) gains for an additional €137, which yields €5707 per QALY. Biennial screening for the same target population showed that purchasing 1 QALY would cost €10 203. The least cost-effective case was biennial screening of the general population aged 50 to 74 years, which yielded €37 931 per QALY.

CONCLUSIONS

Screening LC with LDCT for a high-risk population could be cost-effective in Hungary. For the introduction of screening with LDCT, targeting the most vulnerable groups while having a long-term approach on costs and benefits is essential.

The prevalence of comorbidity in the lung cancer screening population: A systematic review and meta-analysis

OBJECTIVE

Comorbidity is associated with adverse outcomes for all lung cancer patients, but its burden is less understood in the context of screening. This review synthesises the prevalence of comorbidities among lung cancer screening (LCS) candidates and summarises the clinical recommendations for screening comorbid individuals.

METHODS

We searched MEDLINE, EMBASE, EBM Reviews, and CINAHL databases from January 1990 to February 2021. We included LCS studies that reported a prevalence of comorbidity, as a prevalence of a particular condition, or as a summary score. We also summarised LCS clinical guidelines that addressed comorbidity or frailty for LCS as a secondary objective for this review. Meta-analysis was used with inverse-variance weights obtained from a random-effects model to estimate the prevalence of selected comorbidities.

RESULTS

We included 69 studies in the review; seven reported comorbidity summary scores, two reported performance status, 48 reported individual comorbidities, and 12 were clinical guideline papers. The meta-analysis of individual comorbidities resulted in an estimated prevalence of 35.2% for hypertension, 23.5% for history of chronic obstructive pulmonary disease (COPD) (10.7% for severe COPD), 16.6% for ischaemic heart disease (IHD), 13.1% for peripheral vascular disease (PVD), 12.9% for asthma, 12.5% for diabetes, 4.5% for bronchiectasis, 2.2% for stroke, and 0.5% for pulmonary fibrosis.

CONCLUSIONS

Comorbidities were highly prevalent in LCS populations and likely to be more prevalent than in other cancer screening programmes. Further research on the burden of comorbid disease and its impact on screening uptake and outcomes is needed. Identifying individuals with frailty and comorbidities who might not benefit from screening should become a priority in LCS research.

Can quantitative parameters of spectral computed tomography predict lymphatic metastasis in lung cancer? A systematic review and meta-analysis

BACKGROUND AND PURPOSE

This study evaluated the use of quantitative spectral computed tomography (CT) parameters to identify lymph node metastasis (LM) in lung cancer.

MATERIALS AND METHODS

Literature about LM in lung cancer diagnosed using spectral CT up to September 2022 was retrieved from the PubMed, EMBASE, Cochrane Library, Web of Science, Chinese National Knowledge Infrastructure, and Wanfang databases. The literature was strictly screened according to the inclusion and exclusion criteria. Data were extracted, quality assessment was performed, and heterogeneity was evaluated. The pooled sensitivity (SEN), specificity (SPE), positive likelihood ratio (+LR), -LR, and diagnostic odds ratio (DOR) for normalized iodine concentration (NIC) and spectral attenuation curve (λHU) were calculated. The subject receiver operating characteristic (SROC) curves were used, and the area under the curve (AUC) was calculated.

RESULTS

Eleven studies, including 1,290 cases, without obvious publication bias were enrolled. In eight articles, the pooled AUC of NIC in the arterial phase (AP) was 0.84 (SEN=0.85, SPE=0.74, +LR=3.3, -LR=0.20, DOR=16) while that of NIC in the venous phase (VP) was 0.82 (SEN=0.78, SPE=0.72). Additionally, the pooled AUC for λHU (AP) was 0.87 (SEN=0.74, SPE=0.84, +LR=4.5, -LR=0.31, DOR=15) and that for λHU (VP) was 0.81 (SEN=0.62, SPE=0.81). Lymph node (LN) short-axis diameter was ranked last, with a pooled AUC of 0.81 (SEN=0.69, SPE=0.79).

CONCLUSION

Spectral CT is a suitable noninvasive and cost-effective method for determining LM in lung cancer. Additionally, NIC and λHU in the AP have good discrimination ability than short-axis diameter, providing a valuable basis and reference for preoperative evaluation. (registration number INPLASY202290096).

Nanoparticle targeting of mechanically modulated glycocalyx

The mechanical properties and forces in the extracellular environment surrounding alveolar epithelial cells have the potential to modulate their behavior. Particularly, breathing applies 3-dimensional cyclic stretches to the cells, while the stiffness of the interstitium changes in disease states, such as fibrosis and cancer. A platform was developed that effectively imitates the active forces in the alveolus, while allowing one to control the interstitium matrix stiffnesses to mimic fibrotic lung tumor microenvironments. Alveolar epithelial cancer cells were cultured on these platforms and changes in the glycocalyx expression were evaluated. A complex combination of stiffness and dynamic forces altered heparan sulfate and chondroitin sulfate proteoglycan expressions. Consequently, we designed liposomal nanoparticles (LNPs) modified with peptides that can target heparan sulphate and chondroitin sulfates of cell surface glycocalyx. Cellular uptake of these modified nanoparticles increased in stiffer conditions depending on the stretch state. Namely, chondroitin sulfate A targeting improved uptake efficiency in cells experiencing dynamic stretches, while cells seeded on static stiff interstitium preferentially took up heparan sulfate targeting LNPs. These results demonstrate the critical role that mechanical stiffness and stretching play in the alveolus and the importance of including these properties in nanotherapeutic design for cancer treatment.

A systematic review and meta-analysis of the diagnostic value of circulating microRNA-17-5p in patients with non-small cell lung cancer

BACKGROUND

nonSmall Cell Lung Cancer (NSCLC) is the most common type of lung cancer with high morbidity and mortality rates. Studies have shown that miR-17-5p levels are significantly increased in the circulating blood of NSCLC patients. This meta-analysis aimed to investigate the diagnostic value of miR-17-5p in NSCLC in China.

METHODS

A literature search was conducted for studies on the correlation between miR-25 and the diagnosis of NSCLC until October 2022 using English and Chinese databases. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) was adopted to evaluate the quality of studies in the literature. Numerical values for sensitivity and specificity were obtained from false negative (FN), false positive (FP), true negative (TN), and true positive (TP) rates, presented alongside graphical representations with boxes marking the values and horizontal lines showing the confidence intervals. Summary Receiver Operating Characteristic (SROC) curves were applied to assess the performance of the diagnostic tests. The data were processed using RevMan 5.3.

RESULTS

Three studies (208 cases of NSCLC patients and 198 healthy controls) met our evaluation criteria. The sensitivity was 0.70 to 0.75, and the specificity value was 0.82 to 0.83. The Area Under the Curve (AUC) from the SROC curves was > 80%; therefore, it was classified as a good category.

CONCLUSION

Our meta-analysis shows that miR-17-5p can be used for the diagnosis of NSCLC and may serve as a biomarker for the detection of early NSCLC in the Chinese population.

Uniportal VATS for Diagnosis and Staging in Non-Small Cell Lung Cancer (NSCLC)

Uniportal VATS has become an accepted approach in minimally invasive thoracic surgery since its first report for lobectomy in 2011. Since the initial restrictions in indications, it has been used in almost all procedures, from conventional lobectomies to sublobar resections, bronchial and vascular sleeve procedures and even tracheal and carinal resections. In addition to its use for treatment, it provides an excellent approach for suspicious solitary undiagnosed nodules after bronchoscopic or transthoracic image-guided biopsy. Uniportal VATS is also used as a surgical staging method in NSCLC due to its low invasiveness in terms of chest tube duration, hospital stay and postoperative pain. In this article, we review the evidence of uniportal VATS accuracy for NSCLC diagnosis and staging and provide technical details and recommendations for its safe performance for that purpose.

Clinical validation of a ctDNA-Based Assay for Multi-Cancer Detection: An Interim Report from a Vietnamese Longitudinal Prospective Cohort Study of 2795 Participants

The SPOT-MAS assay "Screening for the Presence Of Tumor by Methylation And Size" detects the five most common cancers in Vietnam by evaluating circulating tumor DNA in the blood. Here, we validated its performance in a prospective multi-center clinical trial, K-DETEK. Our analysis of 2795 participants from 14 sites across Vietnam demonstrates its ability to detect cancers in asymptomatic individuals with a positive predictive value of 60%, with 83.3% accuracy in detecting tumor location. We present a case report to support further using SPOT-MAS as a complementary method to achieve early cancer detection and provide the opportunity for early treatment.

Implementing physician education to increase lung cancer screening uptake

Aim

Lung cancer (LC) is the leading cause of cancer-related deaths worldwide. The US Preventive Services Task Force and National Comprehensive Cancer Network recommend annual low-dose computed tomography (LDCT) for eligible adults. We conducted a study to assess physician LDCT referral patterns.

Methods

The study was divided into a pre-, intervention, and post-intervention periods. The intervention was a LC screening educational series. We evaluated rates of LDCT screening referrals during pre- and post-intervention periods.

Results

In the pre-intervention period, 75 patients fulfilled US Preventive Services Task Force and/or National Comprehensive Cancer Network criteria and 27% underwent LDCT. In the post-intervention period, 135 patients fulfilled either screening criteria of whom 61.5% underwent LDCT.

Conclusion

In our study, educational lectures improved compliance significantly and should be used as tool for primary care providers to effectively increase LDCT screening referrals.

Multisystem Imaging Recommendations/Guidelines: In the Pursuit of Precision Oncology

With an increasing rate of cancers in almost all age groups and advanced screening techniques leading to an early diagnosis and longer longevity of patients with cancers, it is of utmost importance that radiologists assigned with cancer imaging should be prepared to deal with specific expected and unexpected circumstances that may arise during the lifetime of these patients. Tailored integration of preventive and curative interventions with current health plans and global escalation of efforts for timely diagnosis of cancers will pave the path for a cancer-free world. The commonly encountered circumstances in the current era, complicating cancer imaging, include coronavirus disease 2019 infection, pregnancy and lactation, immunocompromised states, bone marrow transplant, and screening of cancers in the relevant population. In this article, we discuss the imaging recommendations pertaining to cancer screening and diagnosis in the aforementioned clinical circumstances.

The impact of low-dose CT on smoking behavior among non-smokers, former-smokers, and smokers: A population-based screening cohort in rural China

BACKGROUND

Lung cancer screening may provide a "teachable moment" for the smoking cessation and relapse prevention. However, the impact of lung cancer screening on smoking initiation in non-smokers has not been reported.

METHODS

A baseline smoking behavior survey was conducted in 2000 participants who were screened by low-dose computed tomography (LDCT) from 2014 to 2018. All participants were re-surveyed on their smoking behavior in 2019. Of these, 312 participants were excluded, leaving 1688 participants in the final analysis. The smoking initiation rate in baseline non-smokers, the relapse rate in baseline former smokers, and the abstinence rate in baseline current smokers were calculated, respectively. The associations between screening results, demographic characteristics, and smoking behavior change were analyzed using multivariable logistic regression.

RESULTS

From 2014 to 2019, smoking prevalence significantly decreased from 52.6% to 49.1%. The prevalence of smoking initiation, relapse, and abstinence in baseline non-smokers, former, and current smokers was 16.8%, 22.9%, and 23.7%, respectively. The risk of smoking initiation in baseline non-smokers was significantly higher in those with negative screening result (adjusted OR = 2.97, 95% CI: 1.27-6.94). Compared to smokers who only received baseline screening, the chance of smoking abstinence in baseline current smokers was reduced by over 80% in those who attended 5 rounds of screening (adjusted OR = 0.15, 95% CI:0.08-0.27). No significant associations were found between smoking relapse and prior screening frequency, with at least one positive screening result. Age, gender, occupational exposure, income, and smoking pack years were also associated with smoking behavior changes.

CONCLUSIONS

The overall decreased smoking prevalence indicated an overwhelming effect of "teachable moment" on "license to smoke." A tailored smoking cessation strategy should be integrated into lung cancer screening.

GM, Sasidharan B, Thangakunam B, Pavamani S, Isiah R, Joel A, Bhat TA. Imaging Recommendations for Diagnosis, Staging, and Management of Lung Cancer

Globally and in India, lung cancer is one of the leading malignancies in terms of incidence and mortality. Smoking and environmental pollution are the common risk factors for developing lung cancer. Traditionally, lung cancer is divided into small cell and nonsmall cell types, with nonsmall cell carcinomas including squamous cell carcinoma, adenocarcinoma, and large cell carcinoma.

In this review article, we describe the imaging recommendations and findings in the diagnosis, staging, and management of lung cancer, including the imaging of treatment-related complications.

Radiation Exposure to Low-Dose Computed Tomography for Lung Cancer Screening: Should We Be Concerned?

Lung cancer screening (LCS) programs through low-dose Computed Tomography (LDCT) are being implemented in several countries worldwide. Radiation exposure of healthy individuals due to prolonged CT screening rounds and, eventually, the additional examinations required in case of suspicious findings may represent a concern, thus eventually reducing the participation in an LCS program. Therefore, the present review aims to assess the potential radiation risk from LDCT in this setting, providing estimates of cumulative dose and radiation-related risk in LCS in order to improve awareness for an informed and complete attendance to the program. After summarizing the results of the international trials on LCS to introduce the benefits coming from the implementation of a dedicated program, the screening-related and participant-related factors determining the radiation risk will be introduced and their burden assessed. Finally, future directions for a personalized screening program as well as technical improvements to reduce the delivered dose will be presented.

Artificial Neural Networks in Lung Cancer Research: A Narrative Review

BACKGROUND

Artificial neural networks are statistical methods that mimic complex neural connections, simulating the learning dynamics of the human brain. They play a fundamental role in clinical decision-making, although their success depends on good integration with clinical protocols. When applied to lung cancer research, artificial neural networks do not aim to be biologically realistic, but rather to provide efficient models for nonlinear regression or classification.

METHODS

We conducted a comprehensive search of EMBASE (via Ovid), MEDLINE (via PubMed), Cochrane CENTRAL, and Google Scholar from April 2018 to December 2022, using a combination of keywords and related terms for "artificial neural network", "lung cancer", "non-small cell lung cancer", "diagnosis", and "treatment".

RESULTS

Artificial neural networks have shown excellent aptitude in learning the relationships between the input/output mapping from a given dataset, without any prior information or assumptions about the statistical distribution of the data. They can simultaneously process numerous variables, managing complexity; hence, they have found broad application in tasks requiring attention.

CONCLUSIONS

Lung cancer is the most common and lethal form of tumor, with limited diagnostic and treatment methods. The advances in tailored medicine have led to the development of novel tools for diagnosis and treatment. Artificial neural networks can provide valuable support for both basic research and clinical decision-making. Therefore, tight cooperation among surgeons, oncologists, and biostatisticians appears mandatory.

Psychological distress as risk factor for the efficacy of whole-brain radiotherapy in brain metastasis patients

Aim: To evaluate the relationship between psychological distress and the efficacy of whole-brain radiotherapy (WBRT) in advanced brain metastasis patients. Methods: Brain metastasis patients (40 with psychological distress and 47 without psychological distress) completed distress thermometer tests before WBRT, and progression-free survival (PFS) was acquired during the follow-up period. Results: Psychological distress was a risk factor for poorer PFS in patients treated with WBRT (p < 0.01). The PFS of survivors who underwent WBRT was superior for those without psychological distress (hazard ratio: 0.295; 95% CI: 0.173-0.500; p < 0.01). Conclusion: The survival of brain metastasis patients receiving WBRT was influenced by psychological distress, which negatively affected the treatment outcome and is likely to be a potential risk indicator in advanced cancer patients receiving WBRT.

Feasibility of Single - Encounter Telemedicine Lung Cancer Screening: A Retrospective Cohort Study in an Underserved Population

BACKGROUND

COVID-19 forced a delay of non-essential health services, including lung cancer screening. Our institution developed a single-encounter, telemedicine (SET) lung cancer screening whereby patients receive low-dose CT in-person, but counseling regarding results, coordination of follow-up care and smoking cessation is delivered using telemedicine. This study compares outcomes of SET lung cancer screening to our pre-COVID, single-visit, in-person (SIP) lung cancer screening.

METHODS

A retrospective cohort study was performed we recorded independent variables of gender, race/ethnicity, age, educational attainment, smoking status and dependent variables including cancer diagnosis, stage and treatment between March 2019 to July 2021. Using retrospective analysis, we compared outcomes of SIP lung cancer screening before COVID-19 and SET lung cancer screening amid COVID-19.

RESULTS

There was a significant difference in number of patients screened pre- and amid COVID-19.673 people were screened via SIP, while only 440 were screened via SET. SIP screening consisted of 52.5% Black/African American patients, which decreased to 37% with SET lung cancer screening. There was no significant difference in gender, age, or educational attainment. There was also no significant difference in Lung-RADS score between the 2 methods of screening or diagnostic procedures performed. Ultimately telemedicine based screening diagnosed fewer cancers, 1.6% diagnosed via telemedicine vs 3.3% screened by in person.

CONCLUSION

We implemented SET lung cancer screening to continue lung cancer screening during a global pandemic. Our study established feasibility of telemedicine-based lung cancer screening among our predominantly African American/Black population, though fewer patients were screened. We found no difference in distribution between age, or educational attainment suggesting other factors discouraging lung cancer screening amid COVID-19.

CLSSL-ResNet: Predicting malignancy of solitary pulmonary nodules from CT images by chimeric label with self-supervised learning

BACKGROUND

Pulmonary granulomatous nodules (GN) with spiculation or lobulation have a similar morphological appearance to solid lung adenocarcinoma (SADC) under computed tomography (CT). However, these two kinds of solid pulmonary nodules (SPN) have different malignancies and are sometimes misdiagnosed.

OBJECTIVE

This study aims to predict malignancies of SPNs by a deep learning model automatically.

METHODS

A chimeric label with self-supervised learning (CLSSL) is proposed to pre-train a ResNet-based network (CLSSL-ResNet) for distinguishing isolated atypical GN from SADC in CT images. The malignancy, rotation, and morphology labels are integrated into a chimeric label and utilized to pre-train a ResNet50. The pre-trained ResNet50 is then transferred and fine-tuned to predict the malignancy of SPN. Two image datasets of 428 subjects (Dataset1, 307; Dataset2, 121) from different hospitals are collected. Dataset1 is divided into training, validation, and test data by a ratio of 7:1:2 to develop the model. Dataset2 is utilized as an external validation dataset.

RESULTS

CLSSL-ResNet achieves an area under the ROC curve (AUC) of 0.944 and an accuracy (ACC) of 91.3%, which was much higher than that of the consensus of two experienced chest radiologists (77.3%). CLSSL-ResNet also outperforms other self-supervised learning models and many counterparts of other backbone networks. In Dataset2, AUC and ACC of CLSSL-ResNet are 0.923 and 89.3%, respectively. Additionally, the ablation experiment result indicates higher efficiency of the chimeric label.

CONCLUSION

CLSSL with morphology labels can increase the ability of feature representation by deep networks. As a non-invasive method, CLSSL-ResNet can distinguish GN from SADC via CT images and may support clinical diagnoses after further validation.