Early Detection Of Lung Cancer Research Articles

“Lung Cancer Prediction Using Machine Learning: A Comprehensive Study”

Lung cancer remains one of the leading causes of cancer-related deaths worldwide, largely due to the fact that it is often diagnosed at a late stage. This research aims to develop an automated system for early detection of lung cancer using machine learning techniques. By utilizing the Lungs CT Scan Dataset from Kaggle, we implement advanced image processing methods and convolutional neural networks (CNN) to accurately identify and classify lung nodules as either benign or malignant. Our methodology includes important data preprocessing steps, such as normalization and augmentation, to enhance the performance of the model.” “The results of our study show a significant improvement in detection accuracy compared to traditional diagnostic methods, with our model achieving a high accuracy rate. Additionally, our system has the potential to reduce diagnostic errors, increase early detection rates, and offer a cost-effective screening solution. By integrating this automated tool into clinical workflows, we aim to provide radiologists with reliable AI-generated insights, ultimately improving patient outcomes and easing the burden on healthcare systems.” This study highlights the transformative potential of machine learning in medical diagnostics and emphasizes the importance of continued research to further optimize these technologies for clinical use..

Electrochemical Biosensors in Diagnostic Medicine: Detecting Lung Cancer Biomarkers – A Comprehensive Review

AbstractElectrochemical biosensors use biomolecules, such as proteins, enzymes, and antibodies, to translate the analytical signals detected in a sample. They have diverse applications including pesticide detection in agriculture, water analysis in various sectors, and biomedical and forensic diagnostics. With the estimated number of cancer cases in the US in 2024 being over two million, particularly lung cancer, which is notoriously difficult to diagnose early, the integration of biosensors into the Point‐of‐care Testing (PoCT) strategy can significantly improve the detection of cancer biomarkers, contributing to early diagnosis and successful treatment. Three‐dimensional (3D) printing is a promising alternative for reducing production costs and customizing devices in various ways. This review highlights recent trends and research on the development of electrochemical biosensors for early detection of lung cancer. These biosensors are expected to be more sensitive and selective for a variety of real samples and are precise, accurate, and stable during their commercialization. Significant progress has been made in the development of electrochemical devices for the early diagnosis of lung cancer, with various biomarker anchoring and detection strategies addressed throughout the study. Overcoming these challenges is key to advancing the use of these biosensors, thus improving diagnostic accuracy and enabling the successful treatment of lung cancer patients.

Combination Diagnostics: Adding Blood-Based ctDNA Screening to Low-Dose CT Imaging for Early Detection of Lung Cancer.

Annual low-dose CT screening of individuals with a smoking history identifies early curable lung tumors and reduces cancer mortality by 20%, yet only a minority of eligible patients undergo such monitoring. Mazzone and colleagues apply a blood-based cfDNA fragmentomic assay as a high-sensitivity/low-specificity pre-screen to help stratify individuals who may benefit most from more definitive low-dose CT imaging. See related article by Mazzone et al., p. 2224.

Identification of Plasma Protein Biomarkers for Predicting Lung Cancer.

Lung cancer remains a leading cause of cancer-related mortality worldwide, necessitating the development of effective early diagnostic strategies. Despite advancements in imaging and screening technologies, late-stage diagnoses remain common, limiting treatment options and reducing survival rates. Thus, there is a critical need for reliable, minimally invasive biomarkers to improve early detection and patient outcomes. Plasma protein biomarkers offer promising potential for early lung cancer detection and continuous disease monitoring. This study explored the potential of specific plasma protein markers as early indicators of lung cancer. Plasma samples were collected from normal healthy individuals and lung cancer patients, and protein purification and analysis were conducted using LC-MS/MS. A mixed-effect model was applied to select lung cancer-related protein markers based on label-free relative quantification values. We identified 29 proteins with potential for early lung cancer diagnosis, including complement proteins (CFB, C3, C8G, C1QA, C1R, C6), orosomucoid proteins (ORM1, ORM2), ceruloplasmin (CP), alpha-1-B glycoprotein (A1BG), and others. These proteins play diverse roles in immune response, inflammation, and cell signaling, suggesting their relevance in lung cancer pathophysiology. Our findings suggest the potential of plasma proteins as early diagnostic biomarkers for lung cancer. Further validation in larger cohorts is needed to confirm their clinical utility. Integrating these biomarkers into existing diagnostic modalities could enhance early detection accuracy, leading to improved patient outcomes.

ENHANCED TRANSFORMER-BASED DEEP KERNEL FUSED SELF ATTENTION MODEL FOR LUNG NODULE SEGMENTATION AND CLASSIFICATION

Lung cancer remains a leading cause of mortality globally, with outcomes heavily dependent on the timeliness and accuracy of diagnosis. Traditional medical imaging techniques, while foundational in detecting lung nodules, often falter in distinguishing malignant from benign lesions with high precision, largely due to their inability to contextualize the complex spatial relationships within the images. Precise segmentation and classification of lung nodules is crucial for the early detection of lung cancer. This paper presents a novel deep learning model that incorporates a transformer block to improve the performance of lung cancer detection and classification. From performance evaluation, it is evident that our proposed model has an average accuracy of 93%. 05%, which is superior to the existing D3DR _ MKCA model with a mean accuracy of 91.53%. These findings are especially important for the identification of Adenocarcinoma and Small Cell Carcinoma, as improvements in the precision and recall factors have been achieved in these cases.

Photonic-Metamaterial-Based, Near-Field-Enhanced Biosensing Approach for Early Detection of Lung and Ovarian Cancer

Early detection of lung and ovarian cancers relies heavily on identifying tumor biomarkers, but current methods require large blood samples and complex genetic testing. This study presents a novel photonic-metamaterial-based biosensing approach that leverages near-field radiative enhancement to detect cancer biomarkers (CA 125, CEA, and CYFRA 21-1) with high sensitivity. By utilizing structured photonic metamaterials, we optimize specific wavelengths to identify these biomarkers in interstitial fluid, which can be easily collected via minimally invasive microneedle arrays. Integrating near-field interactions with wavelength-selective metamaterials amplifies the thermal response at the nanoscale, allowing for the detection of deficient concentrations of biomarkers. This photonic metamaterial technique provides a faster, more accessible, and affordable alternative to conventional blood-based methods, significantly improving early detection and monitoring of cancer. Ultimately, this approach offers a transformative tool for clinical and research applications in cancer diagnostics.

The capture of circulating tumor cells by Labyrinth system as a tool for early stage lung cancer detection.

We focus on utilizing the Labyrinth system for the detection of circulating tumor cells (CTCs) in patients with lung nodules. Our aim is to evaluate CTCs isolated through the Labyrinth system as a biomarker for early-stage lung cancer (LC) detection. 167 patients with low dose computed tomography (LDCT) diagnostic results for lung nodules and 31 healthy volunteers (HV) were enrolled. Blood samples were processed for CTC detection. LDCT positive (LDCT+) patients underwent surgery and were categorized into those with LC and those with benign lung diseases (BLD) based on their biopsy results. BLD Patients, LDCT negative (LDCT-) patients and HV served as controls. The correlation of CTC counts with LC, BLD, LDCT- and HV was investigated. Receiver operating characteristic (ROC) curves were used to assess the Labyrinth system's diagnostic potential for early-stage LC. Median CTC counts for LC, BLD, LDCT- and HV were 2.7 CTC/mL, 0.6 CTC/mL, 0.4 CTC/mL, 0 CTC/mL, respectively. Statistical analysis indicated CTC counts could distinguish LC from BLD, LDCT- and HV (p-Values < 0.001). Using a cut-off of 1 CTC/mL, the study showed 84.4% sensitivity and 82.4% specificity for LDCT+ patients. Specificity increased to 85.9% for patients with lung nodules and 88.2% for all participants. In conclusion, CTCs detected by the Labyrinth system can serve as a biomarker for early-stage LC detection for patients with lung nodules. CTCs identified by the Labyrinth system are a promising biomarker for early-stage LC detection in clinical practice.

A 16-year evaluation of opportunistic lung cancer screening with low-dose CT in China: comparative findings between non-smokers and smokers

BackgroundAlthough low-dose computed tomography (LDCT) screening effectively reduces LC mortality in high-risk individuals with a history of smoking in China, the feasibility and efficacy of lung cancer screening (LCS) in individuals who never smoked versus individuals who smoked remains unclear.MethodsWe conducted a retrospective analysis of prospective cohort studies at the National Cancer Center (NCC) in China from January 2006 to December 2022. A comprehensive LCS initiative was undertaken, involving 30,468 participants (54.5% male). Participants underwent LCS using LDCT. Potential malignancies were managed through joint consensus between patients and their physicians. Epidemiology, screening eligibility criteria, and LC detection rates and survival outcomes were compared between individuals who smoked and individuals who never smoked.ResultsAmong 30,468 participants, 339 LCs were pathologically confirmed in 289 patients. The LC detection rate was 0.9% (289/30,468) overall, 0.8% in individuals who smoked (71/9,042), and 1.0% in individuals who never smoked (218/21,426). In individuals who smoked, LC detection rates were 0.5% (21/4516) and 1.1% (50/4526) in the < 20 and ≥ 20 pack-year subgroups, respectively (P = 0.001). Early-stage LC (stage 0 or I) was detected in 73.8% of the individuals who smoked and in 78.8% of individuals who never smoked, while advanced LC (stage III–IV) was found 8.8% of individuals who smoked and 4.2% of individuals who never smoked, respectively. Significant differences in histologic types were found between individuals who smoked and individuals who never smoked (P = 0.01), although adenocarcinoma was the most prevalent in both groups, at 83.0% and 78.8%, respectively. The median nodule size was 9.9 mm (IQR, 8.0–13.8) in individuals who smoked and 9.2 mm (IQR, 6.8–13.6) in individuals who never smoked (P = 0.228). Individuals who never smoked tended to favour surgical treatment alone (88.0%) more than individuals who smoked (81.3%). The 10-year survival rate was higher in individuals who never smoked (92.6%) than in individuals who smoked (88.8%). Only 15.6% (45/289) of patients with LC met the United States Preventive Services Task Force (USPSTF) criteria for LDCT eligibility, while 29.0% (84/289) met the China guideline for the screening and early detection of lung cancer (CGSL) criteria. Median follow-up for those followed was 25.4 (IQR, 13.7–43.3) months.ConclusionsLDCT screening improves early LC detection and treatment outcomes for both individuals who smoked and individuals who never smoked. Significant differences exist in epidemiology, histologic type, and survival between these groups. The USPSTF and CGSL criteria miss a significant number of LC cases, particularly among individuals who never smoked. Integrating individuals who never smoked into LCS programs is essential, yet it comes with its own challenges, such as managing radiation risks, allocating resources effectively, and considering financial aspects. Consequently, there is an urgent need for LCS programs in China to better identify the “high-risk” non-smoker population susceptible to LC and to ensure that potential risks associated with screening are reduced.

Lightweight Advanced Deep Neural Network (DNN) Model for Early-Stage Lung Cancer Detection.

Background: Lung cancer, also known as lung carcinoma, has a high mortality rate; however, an early prediction helps to reduce the risk. In the current literature, various approaches have been developed for the prediction of lung carcinoma (at an early stage), but these still have various issues, such as low accuracy, high noise, low contrast, poor recognition rates, and a high false-positive rate, etc. Thus, in this research effort, we have proposed an advanced algorithm and combined two different types of deep neural networks to make it easier to spot lung melanoma in the early phases. Methods: We have used WDSI (weakly supervised dense instance-level lung segmentation) for laborious pixel-level annotations. In addition, we suggested an SS-CL (deep continuous learning-based deep neural network) that can be applied to the labeled and unlabeled data to improve efficiency. This work intends to evaluate potential lightweight, low-memory deep neural net (DNN) designs for image processing. Results: Our experimental results show that, by combining WDSI and LSO segmentation, we can achieve super-sensitive, specific, and accurate early detection of lung cancer. For experiments, we used the lung nodule (LUNA16) dataset, which consists of the patients' 3D CT scan images. We confirmed that our proposed model is lightweight because it uses less memory. We have compared them with state-of-the-art models named PSNR and SSIM. The efficiency is 32.8% and 0.97, respectively. The proposed lightweight deep neural network (DNN) model archives a high accuracy of 98.2% and also removes noise more effectively. Conclusions: Our proposed approach has a lot of potential to help medical image analysis to help improve the accuracy of test results, and it may also prove helpful in saving patients' lives.

XU-NetFullSharp: The Novel Deep Learning Architecture for Chest X-ray Bone Shadow Suppression

Background and objectivesChest X-ray image (CXR) is vital for screening, preventing, and monitoring various lung diseases. In particular, the early detection of lung cancer can significantly improve patients’ chances of survival and quality of life. Unfortunately, approximately 82–95 % of missed pulmonary nodules are estimated to be obscured by rib shadows, making them difficult to recognize. This study addresses this problem by considering the rib shadows in CXRs as noise that can be reduced using deep learning. The result of the proposed model is a CXR with improved clarity for easier and more accurate analysis by radiologists or computer algorithms. MethodsAn automated deep learning-based model for bone shadow suppression from frontal CXRs, called xU-NetFullSharp, was proposed. This network is inspired by the most modern U-NetSharp architecture and was modified using different approaches to preserve as many details as possible and accurately suppress bone shadows. For comparison, recent state-of-the-art models were implemented and trained. JSRT, VinDr-CXR, and Gusarev DES datasets were utilized for the experiments, where the JSRT dataset was extensively augmented. ResultsThe performance of the proposed xU-NetFullSharp was analyzed using statistical measures and compared with that of other architectures. The proposed model significantly outperformed the others, reaching the best values of the most used metrics (0.9846 SSIM; 0.9870 MS-SSIM). It also achieves a correlation of 96.31 % and an intersection of 10.0285 between the predicted and ground truth histograms, together with the smallest value of the Bhattacharyya distance. The obtained results were validated by experts from the University Hospital Olomouc with positive feedback, thus achieving the best objective and subjective results. The proposed method has the potential to be implemented in hospital environments. ConclusionA comprehensive comparison of the proposed architecture with state-of-the-art methods proves its efficiency in suppressing noise in CXRs and its ability to distinguish the signals of important tissues from noise components. This methodology can potentially improve the performance of the existing CXR processing methods. The source code is released in a GitHub repository that can be accessed from the following link: https://github.com/xKev1n/xU-NetFullSharp.

The Potential Benefit of a Novel Urine Biosensor Platform for Lung Cancer Detection in the Decision-Making Process: From the Bench to the Bedside

Background: Lung cancer is the leading cause of cancer-related mortality worldwide. Lung cancer screening and early detection resulted in a decrease in cancer-specific mortality; however, it introduced additional dilemmas and adherence barriers for patients and providers. Methods: Innovations such as biomolecular diagnosis and biosensor-based technology improve the detection and stratification of high-risk patients and might assist in overcoming adherence barriers, hence providing new horizons for better selection of screened populations. Conclusions: In the present manuscript, we discuss some of the dilemmas clinicians are currently facing during the diagnosis and treatment processes. We further highlight the potential benefits of a novel biosensor platform for lung cancer detection during the decision making process surrounding lung cancer.

OA17.03 Missed Opportunities for Early Lung Cancer Detection in a Multi-Disciplinary Thoracic Oncology Clinic Cohort

OA17.03 Missed Opportunities for Early Lung Cancer Detection in a Multi-Disciplinary Thoracic Oncology Clinic Cohort

Machine learning-derived peripheral blood transcriptomic biomarkers for early lung cancer diagnosis: Unveiling tumor-immune interaction mechanisms.

Lung cancer continues to be the leading cause of cancer-related mortality worldwide. Early detection and a comprehensive understanding of tumor-immune interactions are crucial for improving patient outcomes. This study aimed to develop a novel biomarker panel utilizing peripheral blood transcriptomics and machine learning algorithms for early lung cancer diagnosis, while simultaneously providing insights into tumor-immune crosstalk mechanisms. Leveraging a training cohort (GSE135304), we employed multiple machine learning algorithms to formulate a Lung Cancer Diagnostic Score (LCDS) based on peripheral blood transcriptomic features. The LCDS model's performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) in multiple validation cohorts (GSE42834, GSE157086, and an in-house dataset). Peripheral blood samples were obtained from 20 lung cancer patients and 10 healthy control subjects, representing an in-house cohort recruited at the Sixth People's Hospital of Chengdu. We employed advanced bioinformatics techniques to explore tumor-immune interactions through comprehensive immune infiltration and pathway enrichment analyses. Initial screening identified 844 differentially expressed genes, which were subsequently refined to 87 genes using the Boruta feature selection algorithm. The random forest (RF) algorithm demonstrated the highest accuracy in constructing the LCDS model, yielding a mean AUC of 0.938. Lower LCDS values were significantly associated with elevated immune scores and increased CD4+ and CD8+ T-cell infiltration, indicative of enhanced antitumor-immune responses. Higher LCDS scores correlated with activation of hypoxia, peroxisome proliferator-activated receptor (PPAR), and Toll-like receptor (TLR) signaling pathways, as well as reduced DNA damage repair pathway scores. Our study presents a novel, machine learning-derived peripheral blood transcriptomic biomarker panel with potential applications in early lung cancer diagnosis. The LCDS model not only demonstrates high accuracy in distinguishing lung cancer patients from healthy individuals but also offers valuable insights into tumor-immune interactions and underlying cancer biology. This approach may facilitate early lung cancer detection and contribute to a deeper understanding of the molecular and cellular mechanisms underlying tumor-immune crosstalk. Furthermore, our findings on the relationship between LCDS and immune infiltration patterns may have implications for future research on therapeutic strategies targeting the immune system in lung cancer.

Public perspectives on the benefits and harms of lung cancer screening: A systematic review and mixed-method integrative synthesis.

Screening for lung cancer with low dose computed tomography aims to reduce lung cancer mortality, but there is a lack of knowledge about how target populations consider its potential benefits and harms. We conducted a systematic review of primary empirical studies published in any jurisdiction since 2002 using an integrative meta-synthesis technique. We searched six health and social science databases. Two reviewers independently screened titles, abstracts, and potentially eligible full-text studies. Quantitative assessments and open-ended perspectives on benefits and harms were extracted and convergently integrated at analysis using a narrative approach. Study quality was assessed. The review included 26 quantitative, 18 qualitative, and 5 mixed methods studies. Study quality was acceptable. Lung cancer screening was widely perceived to be personally beneficial for early detection and reassurance. Radiation exposure and screening accuracy were recognised as harms, but these were frequently considered to be justified by early detection of lung cancer. Stigma, anxiety, and fear related to screening procedures and results were pervasive among current smokers. People with low incomes reported not participating in screening because of potential out-of-pocket costs and geographic access. Populations targeted for lung cancer screening tended to consider screening as personally beneficial and rationalised physical, but not psychological, harms. Screening programmes should be clear about benefits, use non-stigmatising design, and consider equity as a guiding principle.

Interpretable Machine Learning-Aided Optical Deciphering of Serum Exosomes for Early Detection, Staging, and Subtyping of Lung Cancer.

Lung cancer (LC) is the leading cause of cancer-related mortality worldwide, underscoring an urgent need for strategies that enable early detection and phenotypic classification. Here, we conducted a label-free surface-enhanced Raman spectroscopic (SERS) analysis of serum exosomes from 643 participants to elucidate the biochemical deregulation associated with LC progression and the unique phenotypes of different LC subtypes. Iodide-modified silver nanofilms were prepared to rapidly acquire SERS spectra with a high signal-to-noise ratio using 0.5 μL of patient exosomes. We performed interpretable and automated machine learning (ML) analysis of differential SERS features of serum exosomes to build LC diagnostic models, which achieved accuracies of 100% and 81% for stage I lung adenocarcinoma and its preneoplasia, respectively. In addition, the ML-derived exosomal SERS models effectively recognized different LC subtypes and disease stages to guide precision treatment. Our findings demonstrate that spectral fingerprinting of circulating exosomes holds promise for decoding the clinical status of LC, thus aiding in improving the clinical management of patients.

Serum exosomal small nucleolar RNA (snoRNA) signatures as a predictive biomarker for benign and malignant pulmonary nodules

Low-dose CT (LDCT) is increasingly recognized as the preferred method for detecting pulmonary nodules. However, distinguishing whether a nodule is benign or malignant often necessitates repeated scans or invasive tissue sampling procedures. Therefore, there is a pressing need for non-invasive techniques to minimize unnecessary interventions. This study aim to investigate the expression profile of exosomal snoRNA in the serum of patients with benign and malignant pulmonary nodules. We identified a total of 278 snoRNAs in serum exosomes, revealing significant differences in snoRNA levels between patients with malignant and benign nodules. Specifically, the upregulated snoRNAs U78 and U37 were validated through qRT-PCR and were found significantly elevated in the serum of patients with malignant pulmonary nodules, positioning them as promising biomarkers for the early detection of lung cancer. This study underscores the potential of serum exosomal U78 and U37 as critical tools for assessing the risk of pulmonary nodules identified through CT screening.

Advanced Deep Learning Fusion Model for Early Multi-Classification of Lung and Colon Cancer Using Histopathological Images.

In recent years, the healthcare field has experienced significant advancements. New diagnostic techniques, treatments, and insights into the causes of various diseases have emerged. Despite these progressions, cancer remains a major concern. It is a widespread illness affecting individuals of all ages and leads to one out of every six deaths. Lung and colon cancer alone account for nearly two million fatalities. Though it is rare for lung and colon cancers to co-occur, the spread of cancer cells between these two areas-known as metastasis-is notably high. Early detection of cancer greatly increases survival rates. Currently, histopathological image (HI) diagnosis and appropriate treatment are key methods for reducing cancer mortality and enhancing survival rates. Digital image processing (DIP) and deep learning (DL) algorithms can be employed to analyze the HIs of five different types of lung and colon tissues. Therefore, this paper proposes a refined DL model that integrates feature fusion for the multi-classification of lung and colon cancers. The proposed model incorporates three DL architectures: ResNet-101V2, NASNetMobile, and EfficientNet-B0. Each model has limitations concerning variations in the shape and texture of input images. To address this, the proposed model utilizes a concatenate layer to merge the pre-trained individual feature vectors from ResNet-101V2, NASNetMobile, and EfficientNet-B0 into a single feature vector, which is then fine-tuned. As a result, the proposed DL model achieves high success in multi-classification by leveraging the strengths of all three models to enhance overall accuracy. This model aims to assist pathologists in the early detection of lung and colon cancer with reduced effort, time, and cost. The proposed DL model was evaluated using the LC25000 dataset, which contains colon and lung HIs. The dataset was pre-processed using resizing and normalization techniques. The model was tested and compared with recent DL models, achieving impressive results: 99.8% for precision, 99.8% for recall, 99.8% for F1-score, 99.96% for specificity, and 99.94% for accuracy. Thus, the proposed DL model demonstrates exceptional performance across all classification categories.

An integrated method for detecting lung cancer via CT scanning via optimization, deep learning, and IoT data transmission.

With its increasing global prevalence, lung cancer remains a critical health concern. Despite the advancement of screening programs, patient selection and risk stratification pose significant challenges. This study addresses the pressing need for early detection through a novel diagnostic approach that leverages innovative image processing techniques. The urgency of early lung cancer detection is emphasized by its alarming growth worldwide. While computed tomography (CT) surpasses traditional X-ray methods, a comprehensive diagnosis requires a combination of imaging techniques. This research introduces an advanced diagnostic tool implemented through image processing methodologies. The methodology commences with histogram equalization, a crucial step in artifact removal from CT images sourced from a medical database. Accurate lung CT image segmentation, which is vital for cancer diagnosis, follows. The Otsu thresholding method and optimization, employing Colliding Bodies Optimization (CBO), enhance the precision of the segmentation process. A local binary pattern (LBP) is deployed for feature extraction, enabling the identification of nodule sizes and precise locations. The resulting image underwent classification using the densely connected CNN (DenseNet) deep learning algorithm, which effectively distinguished between benign and malignant tumors. The proposed CBO+DenseNet CNN exhibits remarkable performance improvements over traditional methods. Notable enhancements in accuracy (98.17%), specificity (97.32%), precision (97.46%), and recall (97.89%) are observed, as evidenced by the results from the fractional randomized voting model (FRVM). These findings highlight the potential of the proposed model as an advanced diagnostic tool. Its improved metrics promise heightened accuracy in tumor classification and localization. The proposed model uniquely combines Colliding Bodies Optimization (CBO) with DenseNet CNN, enhancing segmentation and classification accuracy for lung cancer detection, setting it apart from traditional methods with superior performance metrics.

Evaluation of the role of EGFR exon 19 747-750 deletion mutation and plasma amino acid profile in the development of lung cancer.

Lung cancer (LC) is the most common form of cancer in the world. Of the proteins involved in cell differentiation and proliferation, the epidermal growth factor receptor (EGFR) is among the most significant. Amino acids play a crucial role in cell physiology as metabolic regulators. The benefits of liquid biopsies are their non-invasive nature, ease of collection, and ability to depict the entire tumor's status. The present study is designed to detect the relation between the EGFR exon 19 747-750 deletion mutation and lung cancer and investigate the patterns of alterations of plasma-free amino acids (PFAA) in lung cancer patients of different histopathological types and stages as biomarkers for early detection of lung cancer. The study sample comprised 60 lung cancer patients and 60 age- and sex-matched healthy individuals as the control group. Polymerase chain reaction and restriction fragment length polymorphism (PCR-RFLP) were used to examine the EGFR exon 19 747-750 deletion mutation, and an AA analyzer was used to quantify the plasma free amino acid (PFAA) profile. Compared with controls, LC patients had significantly higher levels of three AAs and significantly lower levels of fifteen AAs. Thirteen AAs varied significantly between stages I and II. In the lung cancer group, the percentage of cases of mutant EGFR exon-19 deletion increased to 30% from 13.3% in the control group. The histological forms of lung cancer did not significantly differ in this rise. Valine and citrulline plasma levels were substantially greater in the mutant than in the wild-type. Lysine, histidine, and methionine were the independent predictors of the LC group in multivariate analysis. Lung cancer development is influenced by the EGFR exon 19 747-750 deletion mutation, and the prognosis and early prediction of lung cancer are greatly affected by the amino acid profile concentrations.

Using artificial intelligence based imaging to predict lymph node metastasis in non-small cell lung cancer: a systematic review and meta-analysis.

Lung cancer, especially non-small cell lung cancer (NSCLC), is one of the most-deadly malignancies worldwide. Lung cancer has a worse 5-year survival rate than many primary malignancies. Thus, the early detection and prognosis prediction of lung cancer are crucial. The early detection and prognosis prediction of lung cancer have improved with the widespread use of artificial intelligence (AI) technologies. This meta-analysis examined the accuracy and efficacy of AI-based models in predicting lymph node metastasis (LNM) in NSCLC patients using imaging data. Our findings could help clinicians predict patient prognosis and select alternative therapies. We searched the PubMed, Web of Science, Cochrane Library, and Embase databases for relevant articles published up to January 31, 2024. Two reviewers individually evaluated all the retrieved articles to assess their eligibility for inclusion in the meta-analysis. The systematic assessment and meta-analysis comprised articles that satisfied the inclusion criteria (e.g., randomized or non-randomized trials, and observational studies) and exclusion criteria (e.g., articles not published in English), and provided data for the quantitative synthesis. The quality of the included articles was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). The pooled sensitivity, specificity, and area under the curve (AUC) were used to evaluate the ability of AI-based imaging models to predict LNM in NSCLC patients. Sources of heterogeneity were investigated using meta-regression. Covariates, including country, sample size, imaging modality, model validation technique, and model algorithm, were examined in the subgroup analysis. The final meta-analysis comprised 11 retrospective studies of 6,088 NSCLC patients, of whom 1,483 had LNM. The pooled sensitivity, specificity, and AUC of the AI-based imaging model for predicting LNM in NSCLC patients were 0.87 [95% confidence interval (CI): 0.80-0.91], 0.85 (95% CI: 0.78-0.89), and 0.92 (95% CI: 0.90-0.94). Based on the QUADAS-2 results, a risk of bias was detected in the patient selection and diagnostic tests of the included articles. However, the quality of the included articles was generally acceptable. The pooled sensitivity and specificity were heterogeneous (I2>75%). The meta-regression and subgroup analyses showed that imaging modality [computed tomography (CT) or positron emission tomography (PET)/CT], and the neural network method model design significantly affected heterogeneity of this study. Models employing sample size data from a single center and the least absolute shrinkage and selection operator (LASSO) method had greater sensitivity than other techniques. Using the Deek' s funnel plot, no publishing bias was found. The results of the sensitivity analysis showed that deleting each article one by one did not change the findings. Imaging data models based on AI algorithms have good diagnostic accuracy in predicting LNM in patients with NSCLC and could be applied in clinical settings.