Survival Rate For Lung Cancer Research Articles

Association of Computed Tomographic Screening Promotion With Lung Cancer Overdiagnosis Among Asian Women

As smoking continues to decline in many developed countries, the proportion of lung cancers in nonsmokers will rise. This shift may create substantial pressure to further expand lung cancer screening to lower-risk groups. To determine the association of lung cancer incidence with the promotion of screening in a largely nonsmoking population. This population-based ecological cohort study of stage-specific lung cancer incidence used the Taiwan Cancer Registry to identify women diagnosed with lung cancer from January 1, 2004, to December 31, 2018. Smoking prevalence among Taiwanese women has been less than 5% since 1980. Data were analyzed from February 13, 2020, to November 10, 2021. Low-dose computed tomography (LDCT) screening for lung cancer, initiated during the early 2000s. Change in stage-specific lung cancer incidence. An effective cancer screening program will not only increase the incidence of early-stage cancer but also decrease the incidence of cancer presenting at a late stage. A total of 57 898 women were diagnosed with lung cancer in a population of approximately 12 million Taiwanese women. After the introduction of LDCT screening, the incidence of early-stage (stages 0-I) lung cancer in women increased more than 6-fold, from 2.3 to 14.4 per 100 000 population (absolute difference, 12.1 [95% CI, 11.3-12.8]) from 2004 to 2018. There was no change, however, in the incidence of late-stage (stages II-IV) lung cancer, from 18.7 to 19.3 per 100 000 (absolute difference, 0.6 [95% CI, -0.5 to 1.7]). Because the additional 12.1 per 100 000 early-stage cancers were not accompanied by a concomitant decline in late-stage cancers, virtually all the additional cancers detected represent overdiagnosis. Despite stable mortality, 5-year survival more than doubled from 2004 to 2013, from 18% to 40%, which is arguably the highest lung cancer survival rate in the world. This population-based ecological cohort study found that low-dose computed tomographic screening of mostly nonsmoking Asian women was associated with considerable lung cancer overdiagnosis. Five-year survival is biased by the increased LDCT detection of indolent early-stage lung cancers. Unless randomized trials can demonstrate some value to low-risk groups, LDCT screening should remain targeted only to heavy smokers.

Loss of TIPE3 reduced the proliferation, survival and migration of lung cancer cells through inactivation of Akt/mTOR, NF-κB, and STAT-3 signaling cascades

Lung cancer is the foremost cause of cancer related mortality among men and one of the most fatal cancers among women. Notably, the 5-year survival rate of lung cancer is very low; 5% in developing countries. This low survival rate can be attributed to factors like late stage diagnosis, rapid postoperative recurrences in the patients undergoing treatment and development of chemoresistance against different agents used for treating lung cancer. Therefore, in this study we evaluated the potential of a recently identified protein namely TIPE3 which is known as a transfer protein of lipid second messengers as a lung cancer biomarker. TIPE3 was found to be significantly upregulated in lung cancer tissues indicating its role in the positive regulation of lung cancer. Supporting this finding, knockout of TIPE3 was also found to reduce the proliferation, survival and migration of lung cancer cells and arrested the G2 phase of cell cycle through inactivation of Akt/mTOR, NF-κB, STAT-3 signaling. It is well evinced that tobacco is the major risk factor of lung cancer which affects both males and females. Therefore, this study also evaluated the involvement of TIPE3 in tobacco mediated lung carcinogenesis. Notably, this study shows for the first time that TIPE3 positively regulates tobacco induced proliferation, survival and migration of lung cancer through modulation of Akt/mTOR signaling. Thus, TIPE3 plays critical role in the pathogenesis of lung cancer and hence it can be specifically targeted to develop novel therapeutic strategies.

Antineoplastic effects of erufosine on small cell and non-small cell lung cancer cells through induction of apoptosis and cell cycle arrest.

Lung cancer (LC) is the most common types of cancer worldwide and is marked by high mortality rate. LC is classified into two major types due to their molecular and histological properties; non-small cell lung cancer (NSCLC) A549 and small cell lung cancer (SCLC). Currently, surgery, chemotherapy and radiation therapy are the most common treatment options of LC. However, the survival rate of LC is still very poor. Therefore, new treatment strategies are urgently needed. Erufosine (ErPC3) is a novel alkylphosphocholine and inhibits the translocation of Akt to the plasma membrane. In the current study, the effects of ErPC3 in NSCLC cell line A549 and SCLC cell line DMS 114 in terms of cell viability, induction of apoptosis, cell cycle phase distribution, gene and protein expression levels, and migration capacity were investigated. 25µM ErPC3 exhibited dose-dependent cytotoxicity against in both cancer cells. However, DMS 114 was more sensitive to ErPC3 than A549. Similarly, ErPC3 induced apoptotic cell ratio in DMS114 was significantly greater than A549. 25µM ErPC3 caused the accumulation of both cell in G2/M phase. The levels of BCL-2 were downregulated and CASPASE 3-7 and BAX were upregulated while p-Akt levels were reduced in A549 and DMS 114 cells treated with 25µM ErPC3. Besides, ErPC3 displayed anti-migratory effect on A549 and DMS 114. These findings suggest that ErPC3 may be a promising novel therapeutic candidate for treatment of LC. ErPC3 treatment merits further investigation as potential agent against LC.

Benefits of precision medicine in lung cancer: experience from a single institution

Lung cancer remains the leading cause of cancer death worldwide and in China. Over the past two decades, targeted therapy and immunotherapy have improved the five-year survival rate of lung cancer, and precision medicine has played a great part in this progress. However, owing to the heterogeneity, complexity, and economic disparity, the usage of precision medicine is still low, which generally results in a poor prognosis. In view of the current bottlenecks, such as low early diagnosis rates and the poor prognosis of lung cancer patients, our team predicted and identified early diagnosis markers of lung cancer. We have described the genetic characteristics of lung cancer patients, identified potential treatment targets, and explored accurate treatment schemes for the disease. We have also promoted their application by conducting bioinformatic analysis, high-throughput sequencing, liquid biopsy, and improved FISH and other technologies. This has formed an integrated system for the precise diagnosis, treatment, and transformation of lung cancer research, obtaining good social benefits after their application.

Lung cancer screening in the gulf: Rationale and recommendations.

Lung cancer is the leading cause of cancer-related death worldwide among both men and women. Although advances in therapy have been made, the 5-year survival rates for lung cancer remain poor, ranging from 10% to 20%. One of the main reasons is late presentation, as only 25% of patients are amenable to cure at the time of presentation. Therefore, the emphasis on lung cancer screening (LCS) is growing with the current evidence that has shown benefits with low-dose computed tomography scan of the chest in high-risk populations. LCS remains a debated topic in Gulf Cooperation Council (GCC) countries, possibly due to a lack of local experience. In this article, we explore the rationale and give recommendations on the best approach for LCS in GCC.

Construction and Validation of Early Warning Model of Lung Cancer Based on Machine Learning: A Retrospective Study.

Background: This study is a retrospective study. The purpose of this study is to construct and validate an early warning model of lung cancer through machine learning. Methods: The CDKN2A gene expression profile and clinical information were downloaded from The Cancer Genome Atlas (TCGA) database and divided into a tumor group and a normal group (n = 57). The top 5 somatic mutation-related genes were extracted from 567 somatic mutation data downloaded from TCGA database using random forest algorithm. Cox proportional hazard model and nomogram were constructed combining CDKN2A, 5 somatic mutation-related genes, gender, and smoking index. Patients were divided into high-risk and low-risk groups according to risk score. The predictability of the model in the prognosis of lung cancer was estimated by Kaplan-Meier survival analysis and receiver operating characteristics curve. Results: We constructed a prognostic model consisting of 5 somatic mutation-related genes (sphingosine 1-phosphate receptor 1 [S1PR1], dedicator of cytokinesis 7 [DOCK7], DEAD-box helicase 4 [DDX4], laminin subunit beta 3 [LAMB3], and importin 5 [IPO5]), cyclin-dependent kinase inhibitor 2A (CDKN2A), gender, and smoking indicators. The high-risk group had a lower overall survival rate compared to the low-risk group (hazard ratio = 2.14, P = 0 .0323). The area under the curve predicted for 3-year, 5-year, and 10-year survival rates are 0.609, 0.673, and 0.698, respectively. The accuracy, sensitivity, and specificity of the model for predicting the 10-year survival rate of lung cancer are 76.19%, 56.71%, and 86.23%. Conclusion: The lung cancer early warning model and nomogram may provide an essential reference for patients with lung cancer management in the clinic.

Neutrosophic set with Adaptive Neuro-Fuzzy Inference System for Liver Tumor Segmentation and Classification Model

Lung cancer is the abnormal development of cells in the lung causes serious risk to the health since lung has an interconnected system of blood vessel and lymphatic channel exposed to metastasis. The survival rate of lung cancer depends greatly on the earlier diagnosis and staging of the lung cancer. Computed Tomography (CT) image is commonly employed for lung cancer diagnosis since they offer data regarding distinct portions of the lung. The exactness of finding tumor location, volume and shape acting a major role in positive treatment and diagnosis of tumor. This article designs a novel neutrosophic set with adaptive neuro-fuzzy inference system for liver tumor segmentation and classification (NSANFIS-LTSC) model. The presented NSANFIS-LTSC model aims to identify and classify the presence of liver tumor from medical images. The presented NSANFIS-LTSC model primarily undergoes pre-processing to eradicate the noise. Followed by, the neutrosophic set (NS) based segmentation is applied to identify the affected tumor regions in the CT images. Besides, DenseNet-169 model is utilized to create feature vectors and dragonfly algorithm (DFA) is applied to tune the hyper parameters of the DenseNet-169 model. Finally, ANFIS classifier is exploited for the occurrence and classification of liver tumor. The simulation analysis of the NSANFIS-LTSC model is experimented using benchmark dataset and the results are investigated under several aspects. The simulation outcome reported the betterment of the NSANFIS-LTSC model over the recent methodologies.

The roles of long non-coding RNAs in lung cancer.

Lung cancer is the most common malignancy, being a serious threat of human lives. The incidence and mortality of lung cancer has been increasing rapidly in the past decades. Although the development of new therapeutic modes, such as target therapy, the overall survival rate of lung cancer remains low. It is urgent to advance the understanding of molecular oncology and find novel biomarkers and targets for the early diagnosis, treatment, and prognostic prediction of lung cancer. Long non-coding RNAs (lncRNAs) are non-protein coding RNA transcripts that are more than 200 nucleotides in length. LncRNAs exert diverse biological functions by regulating gene expressions at transcriptional, translational, and post-translational levels. In the past decade, it has been shown that lncRNAs are extensively involved in the pathogenesis of various diseases, including lung cancer. In this review, we highlighted the lncRNAs characterized in lung cancer and discussed their translational potential in lung cancer clinics.

Factors associated with diagnosis of stages I and II lung cancer: a multivariate analysis.

ABSTRACTOBJECTIVE To present the overall survival rate for lung cancer and identify the factors associated with early diagnosis of stage I and II lung cancer.METHODS This is a retrospective cohort study including individuals diagnosed with lung cancer, from January 2009 to December 2017, according to the cancer registry at UMass Memorial Medical Center. Five-year overall survival and its associated factors were identified by Kaplan–Meier curves and Cox’s proportional hazards model. Factors associated with diagnosing clinical stage I and II lung cancer were identified by bivariate and multivariate backward stepwise logistic regression (Log-likelihood ratio (LR)) at 95% confidence interval (CI).RESULTS The study was conducted with data on 2730 individuals aged 67.9 years on average, 51.5% of whom female, 92.3% white, and 6.6% never smoked. Five-year overall survival was 21%. Individuals diagnosed with early-stage disease had a 43% five-year survival rate compared to 8% for those diagnosed at late stages. Stage at diagnosis was the main factor associated with overall survival [HR = 4.08 (95%CI: 3.62–4.59)]. Factors associated with early diagnosis included patients older than 68 years [OR = 1.23 (95%CI: 1.04–1.45)], of the female gender [OR = 1.47 (95%CI: 1.24–1.73)], white [OR = 1.63 (95%CI: 1.16–2.30)], and never-smokers [OR = 1.37 (95%CI: 1.01–1.86)]; as well as tumors affecting the upper lobe [OR = 1.46 (95%CI: 1.24–1.73)]; adenocarcinoma [OR = 1.43 (95%CI: 1.21–1.69)]; and diagnosis after 2014 [OR = 1.61 (95%CI: 1.37–1.90)].CONCLUSIONS Stage at diagnosis was the most decisive predictor for survival. Non-white and male individuals were more likely to be diagnosed at a late stage. Thus, promoting lung cancer early diagnosis by improving access to health care is vital to enhance overall survival for individuals with lung cancer.

LCMD: Lung Cancer Metabolome Database

Lung cancer, one of the most common causes of cancer-related death worldwide, has been associated with high treatment cost and imposed great burdens. The 5-year postoperative survival rate of lung cancer (13%) is lower than many other leading cancers indicating the urgent needs to dissect its pathogenic mechanisms and discover specific biomarkers. Although several proteins have been proposed to be potential candidates for the diagnosis of lung cancer, they present low accuracy in clinical settings. Metabolomics has thus emerged as a very promising tool for biomarker discovery. To date, many lung cancer-related metabolites have been highlighted in the literature but no database is available for scientists to retrieve this information. Herein, we construct and introduce the first Lung Cancer Metabolome Database (LCMD), a freely available online database depositing 2013 lung cancer-related metabolites identified from 65 mass spectrometry-based lung cancer metabolomics studies. Researchers are able to explore LCMD via two ways. Firstly, by applying various filters in the “Browse Metabolites” mode, users can access a list of lung cancer-related metabolites that satisfy the filter specifications. For each metabolite, users can acquire the value of the fold change (cancer/normal), statistical significance (p-value) of the fold change, and the comparative research designs of all the mass spectrometry-based lung cancer metabolomics studies that identify this metabolite. Secondly, by applying various filters in the “Browse Studies” mode, users can obtain a list of mass spectrometry-based lung cancer metabolomics studies that satisfy the filter specifications. For each study, users can view the type of studied specimen, mass spectrometry (MS) method, MS data processing software, and differential analysis method, as well as all the identified lung cancer-related metabolites. Furthermore, the overview of each study is clearly illustrated by a graphical summary. The LCMD (http://cosbi7.ee.ncku.edu.tw/LCMD/) is the first database that brings together the meaningful information of lung cancer-related metabolites. The development of the LCMD is envisioned to promote the biomarker discovery of lung cancer.

Prognosis and Survival Analysis of 922,317 Lung Cancer Patients from the US Based on the Most Recent Data from the SEER Database (April 15, 2021).

BackgroundOn April 15, 2021, the Surveillance, Epidemiology, and End Results (SEER) database released the latest lung cancer follow-up data. We selected 922,317 lung cancer patients diagnosed from 2000 to 2017 for survival analysis to provide updated data for lung cancer researchers.Research QuestionThis study explored the latest trends of survival time in terms of gender, race, nationality, age, income, address, histological type and primary site.Study Design and MethodsThe SEER database covers 27.8% of the US population. We used life table, Kaplan–Meier, log-rank, Breslow and Tarone-Ware tests to calculate survival rate, time, and curve and to compare differences in survival distribution. We performed univariate and multivariate Cox proportional hazards analyses.ResultsThe median survival time of all lung cancer patients diagnosed in 2017 increased by 41.72% compared to 2000. Median survival time of female patients diagnosed in 2017 increased by 70.94% compared to 2000. Median survival time of those diagnosed in 2017 for different primary sites was as follows: right middle lobe was the longest, then left lower lobe, right upper lobe, right lower lobe, and left upper lobe. Lung cancer patients older than 75 years had a significantly shorter median survival time. Patients living in metropolitan areas of 250,000 to 1 million had a longer median survival time. Median survival time in the adenocarcinoma group was significantly greater than other patients. Median survival of Asian and other races diagnosed in 2017 was 97.87% higher than those diagnosed in 2000. Survival rate of lung cancer increased gradually with the year of diagnosis.InterpretationThe rapid improvement of the prognosis of female and young lung cancer patients contributes to the improvement of the overall prognosis. Primary lung cancer in the right middle lobe has the best prognosis.

On utilizing 2D features from 3D scans to enhance the prediction of lung cancer survival rates

Accounting for 40% of diagnosed lung cancers, Adenocarcinoma is detected through Computed Tomography (CT) scans which provide images as “sliced” versions of the scans, as one progresses down the thoracic region. Typically, one extracts 2D features from the “sliced” images, and this been utilized to achieve various types of classification. In the same vein, the diagnosis and detection of Adenocarcinoma from such scans using image processing techniques, has been studied in the literature. However, as with any cancer, the survival rate of Adenocarcinoma patients depends on the spread of the cancer, the treatments the patient has undergone, and the severity of the cancer at the juncture when the treatment was initiated. This paper presents the ground reality whereby 2D features extracted from the images, or slices, can yield reasonable survival rate predictions of patients by changing the context goal from classification to a regression problem. Furthermore, the emphasis depicted in this paper revolves around the discovery of a strong correlation between the shapes of the 2D images at successive layers of the scans and the corresponding survival rates. Augmenting a classification feature set with aggregated shape-based feature computations of the tumour throughout the consecutive slices, such as the mean area along the z-axis, displays a significant improvement in the forecasting of the patients’ survival rates. We are not aware of any prior research in this area, and while these results have been proven to be relevant for lung cancer scenario, our position is that these principles are also valid for other tumour-based cancers. The dataset consists of scans from 60 different patients, with varying levels of severity, for which a wide range of survival rates was also available. A particular observation is that for patients that had a survival rate of up to 24 months (i.e., short term predictions), we achieved an error as low as 9%.

Post Thoracic Surgery Life Expectancy Prediction Using Machine Learning

Lung cancer survival rate is very limited post-surgery irrespective it is “small cell and non-small cell”. Lot of work have been carried out by employing machine learning in life expectancy prediction post thoracic surgery for patients with lung cancer. Many machine learning models like Multi-layer perceptron (MLP), SVM, Naïve Bayes, Decision Tree, Random forest, Logistic regression been applied for post thoracic surgery life expectancy prediction based on data sets from UCI. Also, work has been carried out towards attribute ranking and selection in performing better in improving prediction accuracy with machine learning algorithms So accordingly, we here have developed Deep Neural Network based approach in prediction of post thoracic Life expectancy which is the most advanced form of Neural Networks . This is based on dataset obtained from Wroclaw Thoracic Surgery Centre machine learning repository which contained 470 instances On comparing the accuracy, the results indicate that the deep neural network can be efficiently used for predicting the life expectancy.

Extended parallel process model (EPPM) in evaluating lung Cancer risk perception among older smokers

BackgroundThere is a lack of scientific literature on the application of fear appeals theories to evaluate lung cancer risk perception among smokers. The aim of the present study is to apply the Extended Parallel Process Model (EPPM) to discover the perception of the smokers about their lifetime risk of developing lung cancer (perceived susceptibility), their perception of lung cancer survival (perceived severity), response efficacy, self-efficacy, and readiness to quit.MethodsIn this cross-sectional study, 215 eligible smokers (aged 45 years and over who have smoked at least 1 pack per day in the last 5 years) were recruited. The data collection tool was designed using validate self-report questionnaires and it was contained items on the perceived risk of a smoker contracting lung cancer and perceived lung cancer survival rate. It also had questions to measure the main constructs of the EPPM and Readiness to quit (“Low_Readiness”, and “High_Readiness”). To test how the data support conceptual EPPM to data, Generalized Structural Equation Modeling (GSEM) was used.ResultsFindings showed a significant relationship between Perceived_Susceptibility and Perceived_Response Efficacy; (B = 1.16, P < 0.001); between Perceived_Susceptibility and Perceived_Self Efficacy, (B = -0.93, P < 0.001), Perceived_Severity, and Perceived_Response Efficacy (B = 1.07, P < 0.001). There was also a significant relationship between Perceived_Threat and Perceived_Response Efficacy; between Perceived_Threat and Perceived_Self Efficacy. The relationship between High_Readiness and Perceived_Self Efficacy, and between High_Readiness and Perceived_Severity also were significant. However, the relationships between High_Readiness and Perceived_Threat were not significant (P > 0.05).ConclusionPerceived_threat and Perceived_efficacy were important for smokers with low readiness to quit, while Perceived_efficacy was most important for smokers with high readiness to quit. These findings could be used in promoting lung cancer awareness and designing smoking cessation programs based on smokers’ stages of change.

Downregulation of LINC01296 suppresses non-small-cell lung cancer via targeting miR-143-3p/ATG2B.

The 5-year survival rate of lung cancer is one of the lowest among various malignant tumors. Long noncoding RNAs (lncRNAs), noncoding RNAs longer than 200 nucleotides, can function either as tumor suppressors or as oncogenes. The aim of this study is to investigate the function of lncRNA LINC01296 and its molecular mechanism in non-small-cell lung cancer (NSCLC). According to the Gene Expression Omnibus database, 10 differentially expressed lncRNAs in NSCLC cells and patient tissues are upregulated. LINC01296 is the one with the most significant overexpression. Knockdown of LINC01296 inhibits the growth and migration, arrests the cell cycle, and promotes the apoptosis of NSCLC cells. Knocking down LINC01296 in vivo suppresses tumor growth and metastasis. LINC01296 also acts as the sponge of miR-143-3p. Lowering the expression of LINC01296 leads to decreased expression of autophagy-related 2B (ATG2B), a target gene of miR-143-3p. Moreover, downregulation of LINC01296 promotes paclitaxel sensitivity in NSCLC. These results demonstrated that the LINC01296/miR-143-3p/ATG2B axis is crucial in promoting the development of NSCLC and paclitaxel resistance. Our study may provide new ideas for the further research of clinical chemotherapy of NSCLC in the near future.

FP02.03 Factors Associated With Delayed Lung Cancer Diagnosis

The five-year survival rate for lung cancer is increased with localized disease, however only a small portion of patients are diagnosed at early stages. Identifying factors contributing to delayed or increased times to diagnosis can help inform clinical practice and increase early-stage diagnosis rates.

Post Thoracic Surgery Life Expectancy Prediction Using Machine Learning

Lung cancer survival rate is very limited post-surgery irrespective it is “small cell and non-small cell”. Lot of work have been carried out by employing machine learning in life expectancy prediction post thoracic surgery for patients with lung cancer. Many machine learning models like Multi-layer perceptron (MLP), SVM, Naïve Bayes, Decision Tree, Random forest, Logistic regression been applied for post thoracic surgery life expectancy prediction based on data sets from UCI. Also, work has been carried out towards attribute ranking and selection in performing better in improving prediction accuracy with machine learning algorithms So accordingly, we here have developed Deep Neural Network based approach in prediction of post thoracic Life expectancy which is the most advanced form of Neural Networks . This is based on dataset obtained from Wroclaw Thoracic Surgery Centre machine learning repository which contained 470 instances On comparing the accuracy, the results indicate that the deep neural network can be efficiently used for predicting the life expectancy.

Post Thoracic Surgery Life Expectancy Prediction Using Machine Learning

Lung cancer survival rate is very limited post-surgery irrespective it is “small cell and non-small cell”. Lot of work have been carried out by employing machine learning in life expectancy prediction post thoracic surgery for patients with lung cancer. Many machine learning models like Multi-layer perceptron (MLP), SVM, Naïve Bayes, Decision Tree, Random forest, Logistic regression been applied for post thoracic surgery life expectancy prediction based on data sets from UCI. Also, work has been carried out towards attribute ranking and selection in performing better in improving prediction accuracy with machine learning algorithms So accordingly, we here have developed Deep Neural Network based approach in prediction of post thoracic Life expectancy which is the most advanced form of Neural Networks . This is based on dataset obtained from Wroclaw Thoracic Surgery Centre machine learning repository which contained 470 instances On comparing the accuracy, the results indicate that the deep neural network can be efficiently used for predicting the life expectancy.

CHARACTERISTICS AND TREATMENT OUTCOMES OF METASTATIC NON-SMALL CELL LUNG CANCER IN NORTHEAST OHIO: A COMMUNITY PRACTICE EXPERIENCE

TOPIC: Lung Cancer TYPE: Original Investigations PURPOSE: Lung cancer is the leading cause of cancer related death in Ohio in 2019. As per “State of Lung Cancer report”, Ohio’s five-year lung cancer survival rate is 20% (below the national average of 21.7%). It is reported that 55% of patients with non–small cell lung cancer (NSCLC) present with metastatic disease (mNSCLC). Despite the introduction of checkpoint inhibitors, survival benefit remains variable. In Northeast Ohio, occupational exposure to asbestos at steel mills has been well recognized. The contributory effect of an asbestos exposure on survival in mNSCLC is unknown. The objective of this study is to investigate the outcome of mNSCLC in the Northeast Ohio population. METHODS: Retrospective study was conducted using data collected from a community-based cancer care practice in Northeast Ohio. Patients with mNSCLC diagnosed from January 2016, through December 2019 were identified. Patients were included if they had stage IV NSCLC at diagnosis, performance status 0-3, received chemotherapy/ targeted therapy or immune checkpoint inhibitors (ICIs). Patients younger than 18 years, diagnosed with more than one malignancy, on renal dialysis or missing information on tumor characteristics or follow-up were excluded from study.Patient characteristics, EGFR/ALK gene mutation status, metastatic locations, and response to treatment were studied. Overall survival in patients who received chemotherapy was compared to patients receiving chemotherapy plus ICIs or targeted therapy using cox proportional hazard model. RESULTS: A total of 61 patients met inclusion criteria. The median age of the patient cohort was 64.3 years with 60.6% of patients being male. Former or current smokers constituted 91% of the subjects. Multiple metastases at time of diagnosis were recognized in 22 patients (36%) of patients. Nine patients (14.7%) were EGFR mutation or ALK rearrangement positive. Of 61 mNSCLC patients, 33 (54%) received chemotherapy only while 28 (46%) received additional ICIs. Patients who underwent ≥ 2 lines of treatment were 26.2%. Surprisingly, one-year and three-year overall survival (OS) rates were greater in patients who received chemotherapy +/- targeted therapy compared to chemotherapy plus ICIs therapy combinations. OS was 10.0 months in the chemotherapy plus checkpoint inhibitor group vs 17.0 months in the chemotherapy/ targeted therapy group (P value <0.04). Asbestos exposure was not associated with statistically significant impact on OS (P value 0.7). Patients with EGFR mutation/ALK rearrangement status and those with single metastases at time of presentation had better survival at 1 and 3 years (p value 0.02 each). CONCLUSIONS: Patients with single metastasis at the time of presentation had better OS. There was no statistical significance of asbestos exposure on OS. Targeted therapy improved OS in mNSCLC patients with gene mutations. ICIs were most often used as second- or third-line treatment, which might explain lower survival in the immunotherapy plus chemotherapy group. CLINICAL IMPLICATIONS: This study within its limits suggests that survival benefit of ICIs on OS in real-world patients may be different than in clinical trial cohorts. This difference might be explained by different trial eligibility criteria, population biologics, and environmental differences. DISCLOSURES: No relevant relationships by Gary Gibson, source=Web Response, value=I honestly don't know Removed 04/30/2021 by Gary Gibson, source=Web Response No relevant relationships by Lori Hemrock, source=Web Response No relevant relationships by Hamid Ismail, source=Web Response No relevant relationships by Parth Shah, source=Web Response No relevant relationships by Noha Soror, source=Web Response

Modelling lung cancer diagnostic pathways using discrete event simulation

ABSTRACT The United Kingdom has one of the poorest lung cancer survival rates in Europe. In this study, to help design and evaluate a single lung cancer pathway (SCP) for Wales, existing diagnostic pathways and processes have been mapped and then modelled with a discrete event simulation. The validated models have been used to provide key performance indicators and to examine different diagnostic testing strategies. Under the current diagnostic pathways, the mean time to treatment was 72 days for surgery patients, 56 days for chemotherapy patients, and 61 days for radiotherapy patients. Our research demonstrated that by ensuring that the patient attends their first outpatient appointment within 7 days and streamlining the diagnostic tests would have the potential to remove approximately 11 days from the current lung cancer pathway resulting in a 21% increase in patients receiving treatment within the Welsh Government set target of 62 days.