Early Diagnosis Of Lung Cancer Research Articles

Chinese expert consensus on diagnosis and treatment of pulmonary nodules(2024)

Lung cancer is the leading cause of the incidence and mortality of malignant tumors in China. The 5-year survival rate released for China in 2018 was 19.7%. The 5-year survival rate for stage Ⅰ patients is 77%-92%. Early diagnosis and treatment of lung cancer are key to improving the 5-year overall survival rate and prognosis of lung cancer patients. Therefore, experts from the Academic Group of Lung Cancer in Chinese Thoracic Society and Chinese Alliance Against Lung Cancer Expert Group formulated the Chinese Expert Consensus on the Diagnosis and Treatment of Lung Nodules in 2015 to standardize the diagnosis and treatment of lung nodules. In 2018, this consensus was updated to formulate the Chinese Expert Consensus on Diagnosis and Treatment of Lung Nodules (2018 edition), and widely applied in multiple branch centers of Chinese Alliance Against Lung Cancer, proposing the Intelligent Treatment of Million Early Lung Cancer Project. Based on applied experience of the expert consensus in recent years, with reference to the latest evidence has been updated, Chinese Expert Consensus on Diagnosis and Treatment of Lung Nodules (2024 edition) was formulated. The updated content of this consensus mainly includes the following aspects: (1) Define the screening age of high-risk lung cancer populations in China based on the national conditions; (2) Propose definition of "difficult-to-determine pulmonary nodules" to avoid delaying the diagnosis and treatment; (3) Evaluate pulmonary nodules assisted by artificial intelligence (AI) imaging-assisted diagnostic system and propose human-machine MDT to avoid the limitations of AI; (4) Evaluate pulmonary nodules by routine and individualized evaluations for different populations, and make recommendations based on evidence-based management guidelines for different types and sizes of pulmonary nodules. In the updated consensus, 18 consensus points were recommended as a reference for clinical management of pulmonary nodules to improve the 5-year overall survival rate and the prognosis of lung cancer in China.

An enantioselective portable stochastic platform for quick screening of whole blood for methionine enantiomers. A new approach for early diagnosis of lung cancer

ObjectivesIn order to perform the enantioanalysis of L- and D-methionine in whole blood samples, this research suggests a 3D enantioselective stochastic platform. Materials and MethodsThe platform contains a 3D-printed stochastic sensor designed by mixing carbon nanopowder and europium oxide, modified with N-[5-methylpyridin-2-yl] octadec-9-enamide (namely N10-Eu2O3/CNP), an auxiliary Pt electrode, and an Ag/Ag Cl reference electrode. Stochastic method was applied for all measurements. ResultsThe quantification limits of L- and D-methionine were of 10-16 and 10−18 mol/L magnitude order, respectively. High sensitivities and large working concentration ranges were recorded. In whole blood, the enantiomers could be identified with recoveries greater than 99.00 %, and RSD% less than 1.00 %. ConclusionsThe proposed platform can be reliable used for enantioanalysis of methionine in whole blood samples. The preliminary results of the screening tests of whole blood shown that the D-methionine enantiomer is only present in the whole blood of the patients diagnosed with lung carcinoma.

(Invited) Multi-Modal Solid-State Gas Sensors-Based Breath Analysis System with Convolutional Neural Network (CNN) for Early Detection of Lung Cancer

Currently, digital healthcare technologies based on Internet of Things (IOT) are emerging, and the market for digital healthcare was estimated to be 151.5 billion dollars by 2020. Among the digital healthcare technologies, disease diagnosis is attracting maximum attention. Traditional diagnostic methods, such as radiology and human biological material analysis, have limitations of long study time, high cost, radiation exposure, and pain. Therefore, non-invasive diagnostics methods, for early diagnosis of specific diseases and personal healthcare management, have attracted attention.Breath sensors are one of the promising candidates for the realization of non-invasive diagnosis owing to their ease of use, safety, and cost effectiveness. The exhaled breath analysis technique holds great promise as a non-invasive method for diagnosing lung cancer. In the breath of lung cancer patients, various volatile organic compounds (VOCs) markers are generated during metabolic processes. Analyzing these VOCs markers presents a compelling avenue for early and non-invasive lung cancer diagnosis. Solid state gas sensors capable of measuring specific VOCs are currently being researched and utilized as commercial gas sensors for this purpose. However, the concentration of lung cancer VOCs markers in exhaled breath is commonly very low, and individual commercial gas sensors often lack the necessary sensitivity and selectivity to accurately diagnose lung cancer. In this study, we propose a multimodal solid state electronic biosensor system designed to differentiate lung cancer VOCs characteristics among multiple VOCs, enabling exhalation-based lung cancer diagnosis. Initially, we developed multimodal biosensors using more than 20 gas sensors, consisting of metal oxide semiconductor sensors, electrochemical sensors, photoionized detectors, metalorganic frame sensors and so on. And each exhibiting varying sensitivity profiles for complex gas mixtures. Additionally, we designed a classification algorithm based on convolutional neural networks (CNN) and multi-layered perceptron (MLP) to distinguish between lung cancer patients and healthy individuals using the output from these diverse multimodal gas sensors. To evaluate the system's performance, we collected exhaled air samples from 74 lung cancer patients and 107 normal subjects, conducting clinical trials with our developed system. We did model evaluation with confusion matrix and ROC curve. The results of these tests confirmed that our system achieved a high predictive accuracy of over 95% in classifying lung cancer patients. The simplicity of the sensor configuration in our developed system makes it suitable for large-scale screening tests to identify potential candidates for precise lung cancer diagnosis. This technology has the potential to contribute to reduced national welfare costs and improved patient survival rates through early diagnosis.

Yolk-Shell Hierarchical Pore Au@MOF Nanostructures: Efficient Gas Capture and Enrichment for Advanced Breath Analysis.

Surface-enhanced Raman scattering (SERS) offers a promising, cost-effective alternative for the rapid, sensitive, and quantitative analysis of potential biomarkers in exhaled gases, which is crucial for early disease diagnosis. However, a major challenge in SERS is the effective detection of gaseous analytes, primarily due to difficulties in enriching and capturing them within the substrate's "hotspot" regions. This study introduces an advanced gas sensor combining mesoporous gold (MesoAu) and metal-organic frameworks (MOFs), exhibiting high sensitivity and rapid detection capabilities. The MesoAu provides abundant active sites and interconnected mesopores, facilitating the diffusion of analytes for detection. A ZIF-8 shell enveloping MesoAu further enriches target molecules, significantly enhancing sensitivity. A proof-of-concept experiment demonstrated a detection limit of 0.32 ppb for gaseous benzaldehyde, indicating promising prospects for the rapid diagnosis of early stage lung cancer. This research also pioneers a novel approach for constructing hierarchical plasmonic nanostructures with immense potential in gas sensing.

ALKU-Net: Adaptive Large Kernel Attention Convolution Network for Lung Nodule Segmentation

The accurate segmentation of lung nodules in computed tomography (CT) images is crucial for the early screening and diagnosis of lung cancer. However, the heterogeneity of lung nodules and their similarity to other lung tissue features make this task more challenging. By using large receptive fields from large convolutional kernels, convolutional neural networks (CNNs) can achieve higher segmentation accuracies with fewer parameters. However, due to the fixed size of the convolutional kernel, CNNs still struggle to extract multi-scale features for lung nodules of varying sizes. In this study, we propose a novel network to improve the segmentation accuracy of lung nodules. The network integrates adaptive large kernel attention (ALK) blocks, employing multiple convolutional layers with variously sized convolutional kernels and expansion rates to extract multi-scale features. A dynamic selection mechanism is also introduced to aggregate the multi-scale features obtained from variously sized convolutional kernels based on selection weights. Based on this, we propose a lightweight convolutional neural network with large convolutional kernels, called ALKU-Net, which integrates the ALKA module in a hierarchical encoder and adopts a U-shaped decoder to form a novel architecture. ALKU-Net efficiently utilizes the multi-scale large receptive field and enhances the model perception capability through spatial attention and channel attention. Extensive experiments demonstrate that our method outperforms other state-of-the-art models on the public dataset LUNA-16, exhibiting considerable accuracy in the lung nodule segmentation task.

Novel Hybrid Quantum Architecture-Based Lung Cancer Detection Using Chest Radiograph and Computerized Tomography Images.

Lung cancer, the second most common type of cancer worldwide, presents significant health challenges. Detecting this disease early is essential for improving patient outcomes and simplifying treatment. In this study, we propose a hybrid framework that combines deep learning (DL) with quantum computing to enhance the accuracy of lung cancer detection using chest radiographs (CXR) and computerized tomography (CT) images. Our system utilizes pre-trained models for feature extraction and quantum circuits for classification, achieving state-of-the-art performance in various metrics. Not only does our system achieve an overall accuracy of 92.12%, it also excels in other crucial performance measures, such as sensitivity (94%), specificity (90%), F1-score (93%), and precision (92%). These results demonstrate that our hybrid approach can more accurately identify lung cancer signatures compared to traditional methods. Moreover, the incorporation of quantum computing enhances processing speed and scalability, making our system a promising tool for early lung cancer screening and diagnosis. By leveraging the strengths of quantum computing, our approach surpasses traditional methods in terms of speed, accuracy, and efficiency. This study highlights the potential of hybrid computational technologies to transform early cancer detection, paving the way for wider clinical applications and improved patient care outcomes.

Enhanced Lung Cancer Detection Using a Combined Ratio of Antigen-Autoantibody Immune Complexes against CYFRA 21-1 and p53.

The early detection of lung cancer (LC) improves patient outcomes, but current methods have limitations. Autoantibodies against tumor-associated antigens have potential as early biomarkers. This study evaluated the 9G testTM Cancer/Lung, measuring circulating complexes of two antigen-autoantibody immune complexes (AIC) against their respective free antigens (CYFRA 21-1 and p53) for LC diagnosis. We analyzed 100 LC patients and 119 healthy controls using the 9G testTM Cancer/Lung, quantifying the levels of AICs (CYFRA 21-1-Anti-CYFRA 21-1 autoantibody immune complex (CIC) and p53-Anti-p53 autoantibody immune complex (PIC)), free antigens (CYFRA 21-1 and p53), and ratios of AICs/antigens (LC index). The levels of the CICs and PICs were significantly elevated in LC compared to the controls (p < 0.0062 and p < 0.0026), while free antigens showed no significant difference. The CIC/CYFRA 21-1 and PIC/p53 ratios were also significantly higher in LC (all, p < 0.0001). The LC index, when combining both ratios, exhibited the best diagnostic performance with an area under the curve (AUC) of 0.945, exceeding individual CICs, PICs, and free antigens (AUCs ≤ 0.887). At a cut-off of 3.60, the LC index achieved 81% sensitivity and 95% specificity for LC diagnosis. It detected early-stage (Stage I-II) LC with 87.5% sensitivity, exceeding its performance in advanced stages (72.7%). The LC index showed no significant differences based on age, gender, smoking status (former, current, or never smoker), or pack years smoked. The LC index demonstrates promising potential for early LC diagnosis, exceeding conventional free antigen markers.

Plasma-based near-infrared spectroscopy for early diagnosis of lung cancer

Lung cancer (LC) continues to be a leading death cause in China, primarily due to late diagnosis. This study aimed to evaluate the effectiveness of using plasma-based near-infrared spectroscopy (NIRS) for LC early diagnosis. A total of 171 plasma samples were collected, including 73 healthy controls (HC), 73 LC, and 25 benign lung tumors (B). NIRS was utilized to measure the spectra of samples. Pre-processing methods, including centering and scaling, standard normal variate, multiplicative scatter correction, Savitzky-Golay smoothing, Savitzky-Golay first derivative, and baseline correction were applied. Subsequently, 4 machine learning (ML) algorithms, including partial least squares (PLS), support vector machines (SVM), gradient boosting machine, and random forest, were utilized to develop diagnostic models using train set data. Then, the predictive performance of each model was evaluated using test set samples. The study was conducted in 5 comparisons as follows: LC and HC, LC and B, B and HC, the diseased group (D) and HC, as well as LC, B and HC. Among the 5 comparisons, SVM consistently generated the best performance with a certain pre-processing method, achieving overall accuracy of 1.0 (kappa: 1.0) in the comparisons of LC and HC, B and HC, as well as D and HC. Pre-processing was identified as a crucial step in developing ML models. Interestingly, PLS demonstrated remarkable stability and relatively high predictive performance across the 5 comparisons, even though it did not achieve the top results like SVM. However, none of these algorithms were able to effectively distinguish B from LC. These findings indicate that the combination of plasma-based NIRS with ML algorithms is a rapid, non-invasive, effective, and economical method for LC early diagnosis.

Integrated microfluidic chip with a MIP-gated organic electrochemical transistor for continuous, selective, and label-free quantification of tumor marker

Early diagnosis of cancer is crucial to effective treatment of the patients and to higher survival rates. Tumor marker detection as an effective and noninvasive cancer diagnosis method has important clinical significance. N1, N12-diacetylspermine (DAS) is a new tumor marker. The development of its detection methods is an urgent need for medical diagnosis and human health monitoring. The conventional detection methods of DAS are labor-intensive and time-consuming approaches. In this work, a novel electrochemical detection platform was established by integrating an organic electrochemical transistor (OECT) with a microfluidic chip and applied for trace measurement of DAS. The sensing unit is a gate electrode in the OECT modified with molecularly imprinted polymers (MIPs), which can be used as an artificial receptor to realize label-free detection. The high specific recognition ability of MIPs films combined with the amplified function of an OECT yielded a highly sensitive and selective electrochemical detection chip. The integrated microfluidic channel controlledthe exact amount of solution to be delivered and provided real-time detection of DAS. The experimental results indicated that changes in channel current was proportional to DAS concentrations from 1 nM to 10 μM, with the detection limits of 0.8 nM. It is the first demonstration of the integration of a microfluidic system with a MIP-gated OECT for real-time tumor marker detection. The study results of this work helped to develop Point-Of-Care (POC) tools for the early diagnosis of lung cancer, colon cancer and breast cancer.

Artificial intelligence-based plasma exosome label-free SERS profiling strategy for early lung cancer detection.

As a lung cancer biomarker, exosomes were utilized for in vitro diagnosis to overcome the lack of sensitivity of conventional imaging and the potential harm caused by tissue biopsy. However, given the inherent heterogeneity of exosomes, the challenge of accurately and reliably recognizing subtle differences in the composition of exosomes from clinical samples remains significant. Herein, we report an artificial intelligence-assisted surface-enhanced Raman spectroscopy (SERS) strategy for label-free profiling of plasma exosomes for accurate diagnosis of early-stage lung cancer. Specifically, we build a deep learning model using exosome spectral data from lung cancer cell lines and normal cell lines. Then, we extracted the features of cellular exosomes by training a convolutional neural network (CNN) model on the spectral data of cellular exosomes and used them as inputs to a support vector machine (SVM) model. Eventually, the spectral features of plasma exosomes were combined to effectively distinguish adenocarcinoma in situ (AIS) from healthy controls (HC). Notably, the approach demonstrated significant performance in distinguishing AIS from HC samples, with an area under the curve (AUC) of 0.84, sensitivity of 83.3%, and specificity of 83.3%. Together, the results demonstrate the utility of exosomes as a biomarker for the early diagnosis of lung cancer and provide a new approach to prescreening techniques for lung cancer.

Metaheuristic integrated machine learning classification of colon cancer using STFT LASSO and EHO feature extraction from microarray gene expressions

The microarray gene expression data poses a tremendous challenge due to their curse of dimensionality problem. The sheer volume of features far surpasses available samples, leading to overfitting and reduced classification accuracy. Thus the dimensionality of microarray gene expression data must be reduced with efficient feature extraction methods to reduce the volume of data and extract meaningful information to enhance the classification accuracy and interpretability. In this research, we discover the uniqueness of applying STFT (Short Term Fourier Transform), LASSO (Least Absolute Shrinkage and Selection Operator), and EHO (Elephant Herding Optimisation) for extracting significant features from lung cancer and reducing the dimensionality of the microarray gene expression database. The classification of lung cancer is performed using the following classifiers: Gaussian Mixture Model (GMM), Particle Swarm Optimization (PSO) with GMM, Detrended Fluctuation Analysis (DFA), Naive Bayes classifier (NBC), Firefly with GMM, Support Vector Machine with Radial Basis Kernel (SVM-RBF) and Flower Pollination Optimization (FPO) with GMM. The EHO feature extraction with the FPO-GMM classifier attained the highest accuracy in the range of 96.77, with an F1 score of 97.5, MCC of 0.92 and Kappa of 0.92. The reported results underline the significance of utilizing STFT, LASSO, and EHO for feature extraction in reducing the dimensionality of microarray gene expression data. These methodologies also help in improved and early diagnosis of lung cancer with enhanced classification accuracy and interpretability.

A human-in-the-loop method for pulmonary nodule detection in CT scans

Automated pulmonary nodule detection using computed tomography scans is vital in the early diagnosis of lung cancer. Although extensive well-performed methods have been proposed for this task, they suffer from the domain shift issue between training and test images. Unsupervised domain adaptation (UDA) methods provide a promising means to mitigate the domain variance; however, their performance is still limited since no target domain supervision is introduced. To make the pulmonary nodule detection algorithm more applicable in clinical practice and further boost the performance across domains, we propose a human-in-the-loop method in a semi-supervised fashion to enhance the model generalization ability when transferred from source domain to target domain. Specifically, we first train a detector model on source domain, and then the pre-trained detector is utilized with our proposed uncertainty-guided sample selection scheme (USSS) to find a few target domain samples worth annotating most and obtain their human annotations. Finally, the annotated and the rest unlabeled target domain samples are used together to refine the pre-trained model via our proposed zoom-in and zoom-out constraint (ZZC) strategy. We evaluate our method on the Nodule Analysis 2016 (LUNA16) and TianChi datasets. Experimental results show that our method surpasses recent competitive methods on source domain and also achieves surprising performance on target domain.

Deep learning in pulmonary nodule detection and segmentation: a systematic review.

The accurate detection and precise segmentation of lung nodules on computed tomography are key prerequisites for early diagnosis and appropriate treatment of lung cancer. This study was designed to compare detection and segmentation methods for pulmonary nodules using deep-learning techniques to fill methodological gaps and biases in the existing literature. This study utilized a systematic review with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, searching PubMed, Embase, Web of Science Core Collection, and the Cochrane Library databases up to May 10, 2023. The Quality Assessment of Diagnostic Accuracy Studies 2 criteria was used to assess the risk of bias and was adjusted with the Checklist for Artificial Intelligence in Medical Imaging. The study analyzed and extracted model performance, data sources, and task-focus information. After screening, we included nine studies meeting our inclusion criteria. These studies were published between 2019 and 2023 and predominantly used public datasets, with the Lung Image Database Consortium Image Collection and Image Database Resource Initiative and Lung Nodule Analysis 2016 being the most common. The studies focused on detection, segmentation, and other tasks, primarily utilizing Convolutional Neural Networks for model development. Performance evaluation covered multiple metrics, including sensitivity and the Dice coefficient. This study highlights the potential power of deep learning in lung nodule detection and segmentation. It underscores the importance of standardized data processing, code and data sharing, the value of external test datasets, and the need to balance model complexity and efficiency in future research. Deep learning demonstrates significant promise in autonomously detecting and segmenting pulmonary nodules. Future research should address methodological shortcomings and variability to enhance its clinical utility. Deep learning shows potential in the detection and segmentation of pulmonary nodules. There are methodological gaps and biases present in the existing literature. Factors such as external validation and transparency affect the clinical application.

Diagnostic and therapeutic delays in lung cancer during the COVID-19 pandemic: a single center experience at a German Cancer center

BackgroundThe COVID-19 pandemic has had negative drawbacks on the healthcare system worldwide and on individuals other than those directly affected by the virus. Delays in cancer therapy and diagnosis have been reported in the literature. We hypothesized similar effects on patients with lung cancer at our center.MethodsWe retrospectively analyzed data of patients referred to our center with newly diagnosed lung cancer from 2018 to 2022. We considered distribution of UICC Stages and time from case presentation in our multidisciplinary tumor board or from therapeutic indication from treating physician to therapy initiation (surgery, systemic therapies and radiation) to define delays in diagnosis and treatment.Results1020 patients with newly diagnosed lung cancer were referred to our center from 2018 to 2022, with a median of 206 cases yearly (range: 200–208). Cases with Stage IV in 2020–2022 were significantly higher than in 2018–2019 (57% vs. 46%, p = 0,001). 228 operative resections took place between 2018 and 2022, 100 from January 2018 to February 2020 and 128 from March 2020 to December 2022. Median time from presentation in our tumor board to resection was also significantly longer after the beginning of the pandemic than before (22 days vs. 15,5 days, p = 0,013). No significant delays were observed for administration of systemic treatment and initiation of radiation.ConclusionsDuring the pandemic higher disease stages were reported for patients with lung cancer, yet there were no clinically relevant delays in treatment. In the context of the post-covid era new diagnostic strategies are necessary to facilitate early diagnosis of lung cancer. Despite the pandemic, for patients with suspicious symptoms prompt access to healthcare facilities is essential for early diagnosis.

Progress and Outlook on Electrochemical Sensing of Lung Cancer Biomarkers.

Electrochemical biosensors have emerged as powerful tools for the ultrasensitive detection of lung cancer biomarkers like carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), and alpha fetoprotein (AFP). This review comprehensively discusses the progress and potential of nanocomposite-based electrochemical biosensors for early lung cancer diagnosis and prognosis. By integrating nanomaterials like graphene, metal nanoparticles, and conducting polymers, these sensors have achieved clinically relevant detection limits in the fg/mL to pg/mL range. We highlight the key role of nanomaterial functionalization in enhancing sensitivity, specificity, and antifouling properties. This review also examines challenges related to reproducibility and clinical translation, emphasizing the need for standardization of fabrication protocols and robust validation studies. With the rapid growth in understanding lung cancer biomarkers and innovations in sensor design, nanocomposite electrochemical biosensors hold immense potential for point-of-care lung cancer screening and personalized therapy guidance. Realizing this goal will require strategic collaboration among material scientists, engineers, and clinicians to address technical and practical hurdles. Overall, this work provides valuable insight for developing next-generation smart diagnostic devices to combat the high mortality of lung cancer.

Exosomal microRNAs in lung cancer: a narrative review.

Exosomes are nanoscale extracellular vesicles secreted by cells, which can release bioactive macromolecules, such as microRNA (miRNA) to receptor cells. Exosomes can efficiently penetrate various biological barriers which mediate intercellular communication. MiRNA are a class of non-coding RNA that primarily regulate messenger RNA (mRNA) at the post-transcriptional level. MiRNA is abundant in exosomes, which plays an important role by being transported and released through exosomes secreted by lung cancer cells. This review aims to elucidate the roles of exosome-derived miRNAs in lung cancer. We focused on the roles of exosome-derived miRNAs in cancer occurrence and development, including angiogenesis, cell proliferation, invasion, metastasis, immune escape, drug resistance, and their clinical value as new diagnostic and prognostic markers for lung cancer. Exosomal miRNA can not only affect angiogenesis of lung cancer, induce epithelial-mesenchymal transformation, and promote reprogramming of tumor microenvironment, but also affect immune regulation and drug resistance transmission and participate in regulating lung cancer cell proliferation. Therefore, understanding the regulatory roles of exosomal miRNAs in tumor invasion and metastasis can provide new ideas for the treatment of lung cancer. Exosomal miRNA can provide some unique ideas on how to improve the efficiency of diagnosis and treatment of lung cancer in the future. Targeting tumor-specific exosomal miRNA represents a new strategy for clinical treatment of lung cancer, which can provide potential non-invasive biomarkers in the early diagnosis of lung cancer. Investigation of the involvement of exosomal miRNAs in the occurrence and progression of tumors can yield new opportunities for the clinical diagnosis and treatment of lung cancer.

The Role of Early Diagnosis and Intervention in Improving Outcomes for Lung Cancer

Introduction : For lung cancer patients, early diagnosis and treatments are essential to battering outcomes. The objective of this world is to compare early diagnosis vs late diagnosis in terms of overall survival ,progression _free survival, quality of life and adverse events associated to treatment. Methodology: A Prospective cohort trial of seventy_ eight lung cancer patients was Conducted at Hayatabad Medical Complex in Peshawar from December 2021 to March 2024. 39 patients had early diagnosis (n=39) while 39 received late diagnosis(n=39) . Low_ dose Computed Tomography (DCT) screening and prompt symptom presentation lead to early diagnosis; incidental or symptomatic results were the basis for late diagnosis. Overall Survival(OS) , progression_ free survival (PFS), quality of life(QoL) , and frequency of serious adverse events associated to therapy were the outcomes. Results: At a median 'OS of 38 months compared to 14 months in the late diagnosis group (p&lt;0.001) and median PFS of 34 months compared to 10 months(p&lt;0.001), the early diagnosis group showed noticeably superior results. At 12 months, 'the early diagnosis group had a better QoL scores' a mean global health status of 75 compared to 55(p&lt;0.01). Early diagnosis group (18%) had a lower frequency of serious adverse effects than did the late diagnosis group(45%). Conclusion: In patients with lung cancer, early detection and intervention greatly increases survival rates, prolong intervals without progression, improves quality of life, and lower serious side effects associated to therapy. The results emphasized the need of early diagnosis techniques and efficient screening programs in the treatment of lung cancer. Keywords: lung Cancer, early diagnosis, low dose computed tomography, overall survival, progression free survival, quality of life, treatment related adverse events

Sub-femtomolar vertical graphene field effect immunosensor for detection of lung tumor markers

Lung cancer is the main cancer that endangers human life worldwide, with the highest mortality rate. The detection of lung tumor markers is of great significance for the early diagnosis and subsequent treatment of lung cancer. In this study, a vertical graphene field effect transistor (VGFET) immunosensor based on graphene/C60 heterojunction was created to offer quantitative detections for the lung tumor markers carcinoembryonic antigen (CEA), cytokeratin 19 fragment (Cyfra21-1), and neuron-specific enolase (NSE). The experimental results showed that the sensitive range for standard antigen is between 1 pg/ml to 100 ng/ml, with a limit of detection (LOD) of 5.6 amol/ml for CEA, 33.3 amol/ml for Cyfra 21–1 and 12.8 amol/ml for NSE (1 pg/ml for all). The detection accuracy for these tumor markers was compared with the clinically used method for clinical patients on serum samples. Results are highly consistent with clinically used immunoassay in its efficient diagnosis concentration range. Subsequently, the mesoporous silica nanospheres (MSNs) with an average size of 90 nm were surface modified with glutaraldehyde, and a second antibody was assembled on MSNs, which fixes nanospheres on the antigen and amplified the field effect. The LODs for three markers are 100 fg/ml (0.56 amol/ml for CEA) under optimal circumstances of detection. This result indicates that specific binding to MSNs enhances local field effects and can achieve higher sensing efficiency for tumor marker detection at extremely low concentrations, providing effective assistance for the early diagnosis of lung cancer.

Biosensor for the early diagnosis of lung cancer: A DFT study on the applicability of metal-doped graphene structures

The usability of metal-doped graphene structures as biosensor was investigated to develop a preliminary diagnosis of lung cancer in this study. The adsorption and sensing properties of graphene structures formed by doping metal atoms (Pt, Pd, Ni, Ir and Cu) toward aniline, toluene, styrene and benzene gases were examined. Using the DFT/WB97XD method with 6–31 G (d, p)/LANL2DZ basis sets, significant charge transfer from the target gases to the metal-doped graphene structures were observed. After adsorption, the HOMO-LUMO gap values of graphene structures doped with Pt, Cu, and Ni atoms have decreased. All adsorption processes are spontaneous according to the adsorption Gibbs free energy. The findings indicate that copper-doped and platinum-doped graphene have strong potential as electronic and work function sensors for detecting aniline, toluene, styrene, and benzene, which are biomarkers for lung cancer.

Effectiveness Analysis of the Combined Detection of CEA, SCC, CYFRA21-1, NSE and ProGRP for Early Diagnosis of Lung Cancer

Objective To explore the diagnostic value and application effect of the combined detection of CEA (carcinoembryonic antigen), SCC (squamous cell carcinoma antigen), CYFRA21-1 (soluble fragment of cytokeratin 19), NSE (neuron specific enolase) and proGRP (gastrin releasing peptide precursor) in early lung cancer. Method 66 patients who admitted to the hospital from April 2019 to February 2024 were selected as the early stage lung cancer group and the other 159 patients were selected as the benign lung group, in which all subjects were tested for NSE, CEA, ProGRP, CYFRA21-1, and SCC levels and were confirmed by histopathological analysis. In ROC analysis, the cut-off point, AUC, accuracy, sensitivity and specificity between single and combined detection indicators are analyzed. Result The size of cut-off point, AUC, accuracy, sensitivity, specificity, positive and negative detection rate and accuracy rate is 0.3072, 0.790, 89.33%, 0.894, 0.893, 89.40% (59/66), 89.31% (142/159) and 89.33% ((142+59)/225) respectively and it is obviously superior to those of single detection method. Meanwhile, indicator of SCC showed the worst performance by single detection. Conclusion The combined detection indicators have high diagnostic value in early diagnosis of lung cancer, and the sensitivity, positive detection rate, AUC value and accuracy have been significantly improved.