SVM kernel Methods with Data Normalization for Lung Cancer Survivability Prediction Application

The present study investigated the plant growth characteristics, dormancy breaking, yield, and biological activity properties (contents of flavonoid and phenolic compounds, nitrite scavenging activity, and the survival rate of lung cancer) of four asparagus cultivars. It aimed to examine selection possibilities for high efficacy cultivar to promote biological activity in asparagus cultivation in South Korea. The results showed that the number of asparagus buds and root fresh and dry weight of 'Jersey Giant', 'Jersey Supreme', and 'NJ953' were higher than those of 'UC157' cultivar, and there were no differences in the number of roots. The dormancy breaking period of 'Jersey Supreme' was prolonged compared to the other cultivars. 'Jersey Giant' and 'NJ953' had more total spears, and greater spear diameter, and yield than other cultivars. Total flavonoid content was unaffected by cultivar and was higher in ethanol extraction than in hot water extraction. Total phenolic compound content was the lowest in 'NJ953' for both hot water and ethanol extracts, whereas in the ethanol extraction, 'UC157' had the highest, with 39.23 mg·L -1 . 'Jersey Giant', 'Jersey Supreme', and 'NJ953' all showed greater than 70% nitrite scavenging activity. In the case of ethanol extraction, the survival rates of lung cancer in extracts from 'Jersey Giant', 'Jersey Supreme', and 'UC157' were lower than 'NJ 953' cultivar, regardless of extraction concentration. The survival rate of lung cancer was lower in hot water extraction than in ethanol extraction, so that the consumption of asparagus is also expected to be helpful in preventing lung cancer. The growth characteristics and biological activity effects of edible asparagus that were identified in the present study are expected to be useful in selection of high efficacy cultivars for biological activity and utilization.

Health monitoring and prognostics of axial piston pump is very helpful for the safety of aerial hydraulic system. Directing to the difficulties of measuring the wear loss between the valve plate and cylinder barrel, this paper presents a hybrid prediction method based on EMD (Empirical Mode Decomposition) and SVM (Support Vector Machine), in which EMD is used to get the pump's health state and PSO (Particle Swarm Optimization)-SVM is used to make a prediction for the pump's remaining useful lifetime. Oil-return flow was selected to indicate the wear condition of pump with several IMFs. SVM was trained by these IMFs, and then be used to predict the oil-return flow one-step ahead or multi-step ahead. In the SVM's training process, PSO was used to search the optimal parameter of SVM's kernel function. Applications show that hybrid prediction method has higher prediction precision and could be applied for the remaining useful lifetime prognostics.

When one is presented with a blinded report of survival rates for lung cancer across developed countries, how can one identify the result for the UK? Exactly, choose the one...

The effects of hormone replacement therapy on the survival rate of female lung cancer patients are still controversial. The Meta-analysis aims to systematically evaluate the effect of hormone replacement therapy on the survival rate of female lung cancer patients. Retrospective studies regarding the effect of hormone replacement therapy on female lung cancer patients' survival rate were searched from the database of Embase, Cochrane, Pubmed, CNKI, Wanfang, and Weipu. The Meta-analysis was conducted with Stata 12.0 software. I2 test was used to analyze the heterogeneity among included studies. The analysis was conducted by randomized model. Egger's test and Begg's test were used to assess the publication bias. Five retrospective studies were included, involving 2 582 female patients with lung cancer. There were 1 054 cases of female lung cancer with hormone replacement therapy and 1 528 cases of female lung cancer without hormone replacement therapy. No publication bias was observed among these studies. The sensitivity analysis result showed that the overall results were stable. Meta-analysis showed that compared with patients without hormone replacement therapy, patients with hormone replacement therapy had an increased survival time for 5 years (ES=0.346; 95% CI 0.216 to 0.476; P<0.001). Hormone replacement therapy improves 5-year survival in female lung cancer patients. Female lung cancer patients with menopausal syndrome can use hormone replacement therapy properly under their doctors' suggestion.

Lung cancer is one of the most common types of cancer in the United Kingdom. It is often diagnosed late. The 5-year survival rate for lung cancer is below 10%. Early diagnosis may improve survival. Software that has an artificial intelligence-developed algorithm might be useful in assisting with the identification of suspected lung cancer. This review sought to identify evidence on adjunct artificial intelligence software for analysing chest X-rays for suspected lung cancer, and to develop a conceptual cost-effectiveness model to inform discussion of what would be required to develop a fully executable cost-effectiveness model for future economic evaluation. The data sources were MEDLINE All, EMBASE, Cochrane Database of Systematic Reviews, Cochrane CENTRAL, Epistemonikos, ACM Digital Library, World Health Organization International Clinical Trials Registry Platform, clinical experts, Tufts Cost-Effectiveness Analysis Registry, company submissions and clinical experts. Searches were conducted from 25 November 2022 to 18 January 2023. Rapid evidence synthesis methods were employed. Data from companies were scrutinised. The eligibility criteria were (1) primary care populations referred for chest X-ray due to symptoms suggestive of lung cancer or reasons unrelated to lung cancer; (2) study designs that compared radiology specialist assessing chest X-ray with adjunct artificial intelligence software versus radiology specialists alone and (3) outcomes relating to test accuracy, practical implications of using artificial intelligence software and patient-related outcomes. A conceptual decision-analytic model was developed to inform a potential full cost-effectiveness evaluation of adjunct artificial intelligence software for analysing chest X-ray images to identify suspected lung cancer. None of the studies identified in the searches or submitted by the companies met the inclusion criteria of the review. Contextual information from six studies that did not meet the inclusion criteria provided some evidence that sensitivity for lung cancer detection (but not nodule detection) might be higher when chest X-rays are interpreted by radiology specialists in combination with artificial intelligence software than when they are interpreted by radiology specialists alone. No significant differences were observed for specificity, positive predictive value or number of cancers detected. None of the six studies provided evidence on the clinical effectiveness of adjunct artificial intelligence software. The conceptual model highlighted a paucity of input data along the course of the diagnostic pathway and identified key assumptions required for evidence linkage. This review employed rapid evidence synthesis methods. This included only one reviewer conducting all elements of the review, and targeted searches that were conducted in English only. No eligible studies were identified. There is currently no evidence applicable to this review on the use of adjunct artificial intelligence software for the detection of suspected lung cancer on chest X-ray in either people referred from primary care with symptoms of lung cancer or people referred from primary care for other reasons. Future research is required to understand the accuracy of adjunct artificial intelligence software to detect lung nodules and cancers, as well as its impact on clinical decision-making and patient outcomes. Research generating key input parameters for the conceptual model will enable refinement of the model structure, and conversion to a full working model, to analyse the cost-effectiveness of artificial intelligence software for this indication. This study is registered as PROSPERO CRD42023384164. This award was funded by the National Institute for Health and Care Research (NIHR) Evidence Synthesis programme (NIHR award ref: NIHR135755) and is published in full in Health Technology Assessment; Vol. 28, No. 50. See the NIHR Funding and Awards website for further award information.

Review question/objective The objective of this systematic review is to identify, appraise and synthesize qualitative evidence on the common meanings and shared experiences of caregivers looking after patients receiving chemotherapy for lung cancer. Inclusion criteria Types of participants People who provide unpaid care to patients receiving chemotherapy for lung cancer. This is likely to include a patient's spouse, family, friends and community. Types of intervention(s)/phenomena of interest Delivery of home-based care by informal carers of patients receiving chemotherapy for lung cancer. Types of outcomes Outcomes of interest are experiential accounts of informal caregivers.

Breast cancer is a highly prevalent malignant tumor among women worldwide, and early accurate diagnosis is crucial for improving prognosis. Traditional diagnostic methods have limitations such as high rates of misdiagnosis and missed diagnosis, while existing machine learning studies lack targeted strategies for screening nuclear morphological features. This study is the first to adopt Pearson correlation coefficient for targeted screening of breast cancer nuclear morphological features, retaining 25 features with an absolute correlation coefficient greater than 0.2 with diagnostic results; these features achieve a cumulative explained variance of 92.5% and accurately match the core pathological indicator of "nuclear pleomorphism". To address the class imbalance in the original dataset (569 cases, including 357 benign and 212 malignant cases), 143 synthetic samples were generated using the SMOTE algorithm, constructing a balanced dataset of 712 cases. After dividing the training and testing sets via 7:3 stratified sampling, a diagnostic model was built using a radial basis function (RBF) kernel support vector machine (SVM) and compared with a random forest model. Results showed that the SVM achieved an accuracy of 97.2%, precision of 98.1%, and recall of 96.3% on the testing set, with a 10-fold cross-validation average accuracy of 0.968 (with a fluctuation of 0.026); in contrast, the random forest model yielded corresponding indicators of 95.3%, 96.2%, 94.4%, and a 10-fold cross-validation average accuracy of 0.934 (with a fluctuation of 0.022), demonstrating that SVM significantly outperformed random forest. Additionally, the SVM achieved an AUC value of 0.968 on the ROC curve, showing excellent performance in the low false-positive range. Studies have shown that the SVM model based on features selected via the Pearson correlation coefficient exhibits significant advantages in breast cancer diagnosis. Compared with traditional diagnostic methods, such as mammography which has a misdiagnosis rate of 10%-15%, this model can reduce the risk of misdiagnosis by approximately 70%. Meanwhile, the model can effectively capture the clinical significance of microscopic nuclear features, not only providing a reliable tool for accurate breast cancer diagnosis but also promoting the in-depth integration of machine learning and pathological diagnosis.

P3.01-039 JAK2 Participates in Lung Cancer Cells Proliferation, Migration and Invasion: Topic: Functional Biology in Lung Cancer

Introduction: In recent years, immune checkpoint inhibitors (ICI) have revolutionized the landscape of treatment across a variety of cancers. Potential concerns regarding racial disparities in ICI access and therapeutic efficacy have been well documented. Recent studies have indicated improved ICI access and response outcomes in White patients compared to minority groups with head and neck cancer (HNC) and melanoma patients. In contrast, patients diagnosed with lung cancer have been mixed, with some research suggesting black patients may fare better with ICI therapy. Existing literature is largely limited to retrospective studies with small population sizes, and lacks comparisons of racial disparities across malignancies. We aim to describe and compare racial inequities in ICI outcomes in HNC, melanoma, and lung cancer. Methods: The TriNetX US Collaborative Network database was queried for patients &amp;gt;18 years-old with a diagnosis of head and neck cancer, melanoma or lung cancer that were treated with ICIs. Malignancy cohorts were defined by race: non-Hispanic White (NHW) and Black. Race cohorts were 1:1 propensity score matched (PSM) for comorbidities, oncologic stage, and chemoradiation treatment. Demographic differences in cohorts were evaluated prior to matching. Following PSM, overall survival and rates of autoimmunity were assessed at 5 years. Results: 19,443 NHW and 2,827 Black patients treated with ICI were identified. At a baseline, the Black patient cohorts had significantly higher rates of comorbidies (p&amp;lt;0.02). Across all malignancies, the geographic distribution of NHW patients was more equally distributed compared to Black patients (majority south). With PSM, black HNC patients had significantly lower survival rates compared to NHW [HR: 1.4 (1.2, 1.6), p=0.0002]. In melanoma, the same trend was present, though not significant [HR: 1.3 (0.95, 1.7), p=0.1]. In contrast, in lung cancer, Black patients had significantly higher survival rates compared to NHW patients [HR 0.78 (0.70, 0.86), p&amp;lt;0.0001]. In HN and lung cancer, NHW patients had significantly higher rates of autoimmune conditions after ICI therapy compared to Black [OR: 2.0 (1.4,2.8), 1.2 (1.0, 1.4), respectively]. Conclusion: This study represents the most extensive analysis of racial disparities on ICI outcomes across malignancies. This study found that black patients treated with ICIs had decreased survival rates at 5 years compared to NHW patients in HNC and melanoma, but increased survival rate in lung cancer. This disparity persisted following PSM, matching cohorts for comorbidities, oncologic stage and adjuvant therapy. Black patients also had significantly lower rates of autoimmunity in HN and lung cancer. Noted differences in survival may be a reflection of geographic practice differences, access to care and/or socioeconomic status. Further investigation is required to elucidate the underlying etiologies of these trends. Citation Format: Kathryn L. Nunes, Pablo Llerena, Praneet Kaki, Shreya Mandloi, Annie Moroco, Eric Mastrolonardo, David M. Cognetti, Adam J. Luginbuhl, Joseph M. Curry. Evaluating racial disparities in immune checkpoint inhibitor survival and autoimmunity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4845.

Informing Patient Surveillance for the Growing Number of Survivors of Lung Cancer

As a result of an increased focus on early detection including lung cancer screening, combined with more curative treatment options, the 5-year survival rates for lung cancer are improving. Welcome though this is, it brings new, hitherto unseen challenges. As more patients are cured and survive longer, they are at risk of developing second primary cancers, particularly lung cancer. In this review, we examine the challenges that surveillance, diagnosis, and management of second primary lung cancer (SPLC) bring and how these can be addressed. Recent data from prospective follow-up studies suggests that the incidence of SPLC may be higher than previously appreciated, partly due to an increase in multi-focal adenocarcinoma spectrum disease. Over 5 years, up to 1 in 6 long-term lung cancer survivors may develop a SPLC. Although not routinely used in clinical practice at present, genomic approaches for differentiating SPLC from intrapulmonary metastases of the first primary are emerging, and we highlight how this could be used to help differentiate lesions. An accurate distinction between SPLC and the recurrence of the first primary is of paramount importance due to the very different management strategies that may be required. Wrongly classifying an SPLC as a recurrence of the first primary may have significant consequences for patient management and overall survival. Updated approaches to the classification of SPLC combining clinical history, histopathological assessment, and genomic profiling are needed. Finally, we review the potential role of early detection biomarkers in the identification of SPLC, focusing in particular on blood-based biomarkers that are being examined in a multi-center prospective study recruiting lung cancer survivors.

Lung cancer remains the most common cancer worldwide, with an incidence of 1.35 million new cases in 2002 (1). It also is the leading cause of cancer mortality globally, with 1.18 million deaths. This represents over 12 percent of all newly diagnosed cancers, and nearly 18 percent of all cancer deaths in the world (1). In the United States, it is estimated that 215,000 new cases of lung cancer will be diagnosed in 2008, and nearly 162,000 people will die from the disease. Though the trend in new lung cancer cases appears to be declining in men and plateauing in women, the overall five-year survival rate for lung cancer in the United States still remains around 15 percent (2). The importance of accurate staging in lung cancer is based on the intertwined principles of treatment and prognosis. Without establishing a precise stage, it is unclear which treatment modalities should be offered, which clinical trials the patient would be eligible to participate in, or how to assess the outcomes of any treatment. Moreover, given the wide range of potential survival based on how localized or advanced the disease is, the clinician cannot counsel the patient adequately without this knowledge. In this chapter, the staging system for non-small cell lung cancer will be discussed. The goals of the chapter are:

BackgroundBreast cancer 1 (BRCA1) and breast cancer 2 (BRCA2) tumor suppressor genes play crucial roles in DNA repair and regulation of transcription. Mutations in these genes are closely associated with the occurrence of cancers. However, the mutation status of BRCA gene in central south Chinese lung cancer patients remains unclear, and its expression levels in lung cancer also need to be further explored.MethodsIn this study, we use next‐generation sequencing (NGS) technology to analyze the BRCA genes mutations in 462 central south Chinese lung cancer patients. Public databases including cBioportal, Catalogue Of Somatic Mutations In Cancer (COSMIC), The Cancer Genome Atlas (TCGA), Human Protein Atlas (HPA) and Expression Profiling Interactive Analysis (GEPIA) are also applied to explore the expression level and mutation status of BRCA in lung cancer patients and their relationships with the prognosis.ResultsWe found that the mutation rate of BRCA1/2 in central south Chinese lung cancer patients is 4.3% and 6.5% respectively, and missense mutations account for the majority in both BRCA1/2, which are similar to the international status of BRCA1/2 from public databases. In addition, 45 novel mutations of BRCA1/2 in lung cancer are reported in this study. Furthermore, we find that the BRCA2 mutations are negatively correlated with overall survival rate in lung cancer using cBioportal. Last, we demonstrate that both of the mRNA and protein levels of BRCA1/2 are upregulated in lung cancer, and the elevated mRNA expression levels are positively linked with poor prognosis.ConclusionIn general, our study better complements knowledge of the BRCA1/2 mutation status in the Chinese lung cancer patients, and firstly reveals the association between BRCA1/2 expression levels and prognosis of lung cancer patients, which may provide great value for the early diagnosis and clinical treatment of lung cancer.

Existing epidemiologic reports or studies of cancer statistics in Korea lack sufficient data on cancer severity distributions and observed survival rates. This study analyzed trends in major cancer statistics according to sex and severity levels in Korea from 2006 to 2013. We included eight cancers (hepatocellular carcinoma, and thyroid, colorectal, gastric, lung, prostate, breast, and cervical cancer), using Korea Central Cancer Registry data. Severity level was classified by Surveillance, Epidemiology, and End Results (SEER) stage as follows: localized, regional, distant, or unknown. Numbers of incident cancer cases from 2006 to 2013 were described by sex and SEER stage. We estimated up to 8-year observed survival rates of major cancers by sex and SEER stage, and provided prevalence rates by sex and SEER stage in 2011, 2012, and 2013. Although increases in new cancer cases are slowing and the total number of incident cancer cases in 2013 decreased for the first time since 2006, the number of prevalent cancer cases was 663,530 in 2013, an increase of 13.3% compared to 2011. Among the five cancers affecting both sexes, sex-related differences in 5-year observed survival rates for lung cancer were greatest in the localized stage (men, 31.9%; women, 48.1%), regional stage (men, 20.0%; women, 31.3%), and unknown stage (men, 24.3%; women, 37.5%). The sum of the proportions of localized and regional stages for thyroid and breast cancer was over 90% in 2013, while the sum of the proportions of localized and regional stages for lung cancer was only 56.7% in 2013. Differences in observed survival rates between men and women were prominent in lung cancer for all SEER stages. The reported epidemiologic data from this study can be used to obtain a more valid measure of cancer burden using a summary measure of population health.

BackgroundIn the last 10 years, the survival rate in lung cancer in the UK has improved, but remains lower than some counterpart European countries. The cancer waiting time initiative aimed...