Diagnosis Of Lung Cancer Research Articles

Case report: Secondary failure to tolvaptan in a patient with SCLC and paraneoplastic SIADH

The syndrome of inappropriate antidiuretic hormone secretion (SIADH) is frequent in lung cancer patients. Here, we report a case with persistent hyponatremia, which suggested malignant SIADH and facilitated an early diagnosis of small cell lung cancer (SCLC). A combined radio-chemotherapy led to a partial remission and resolution of SIADH. An early relapse was indicated by reoccurring severe hyponatremia and increased copeptin levels, which were used as surrogate markers for the antidiuretic hormone (ADH). As palliative immunochemotherapy, together with fluid restriction and solute substitution, were unable to control hyponatremia, treatment with the ADH V2-receptor antagonist tolvaptan was initiated. Over time, the dose of tolvaptan needed to be increased, paralleled by a well-documented exponential increase of copeptin levels. In summary and conclusion, this is a rare case of a secondary failure to tolvaptan with unique documentary evidence of increasing copeptin levels. This observation supports the hypothesis that exceedingly high ADH levels may lead to competitive displacement of tolvaptan from the V2 receptor.

Regulatory mechanism and expression level of PRPS2 in lung cancer.

Lung cancer, with high morbidity and mortality, is the commonest respiratory system neoplasm, which seriously endangers the life safety of patients. In this study, the effect of PRPS2 on cell progression was preliminarily investigated. Immunohistochemical staining, western blot and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) were performed to verify the expression level of PRPS2 in lung cancer. Lung cancer cell lines with stable downregulation of PRPS2 were constructed in A549 cells and NCIH460 cells. The function of PRPS2 silencing on the proliferation ability was verified by the EdU and cell colony formation experiment. Scratch and transwell tests were conducted to verify the role of PRPS2 silencing on the migratory and invasive ability of cells. The impact of PRPS2 silencing on cell apoptosis and cell cycle was verified by flow cytometry test. The effects of PRPS2 silencing on apoptosis-associated proteins were assessed by western blot assay. The function of PRPS2 silencing on tumor growth in vivo was studied through xenograft tumor experiment. In comparison with normal tissues, PRPS2 was upregulated in lung cancer tissues. PRPS2 knockdown notably hindered the migratory ability, invasive ability and proliferation, but accelerated cell apoptosis. In vivo experiments confirmed that PRPS2 silencing blocked the growth of transplanted tumors. In lung cancer, PRPS2 silencing suppressed the malignant progression, indicating that PRPS2 might be a novel biomarker for lung cancer treatment and diagnosis.

The value of dynamic FDG PET/CT in the differential diagnosis of lung cancer and predicting EGFR mutations

Objectives18F-fluorodeoxyglucose (FDG) PET/CT has been widely used for the differential diagnosis of cancer. Semi-quantitative standardized uptake value (SUV) is known to be affected by multiple factors and may make it difficult to differentiate between benign and malignant lesions. It is crucial to find reliable quantitative metabolic parameters to further support the diagnosis. This study aims to evaluate the value of the quantitative metabolic parameters derived from dynamic FDG PET/CT in the differential diagnosis of lung cancer and predicting epidermal growth factor receptor (EGFR) mutation status.MethodsWe included 147 patients with lung lesions to perform FDG PET/CT dynamic plus static imaging with informed consent. Based on the results of the postoperative pathology, the patients were divided into benign/malignant groups, adenocarcinoma (AC)/squamous carcinoma (SCC) groups, and EGFR-positive (EGFR+)/EGFR-negative (EGFR-) groups. Quantitative parameters including K1, k2, k3, and Ki of each lesion were obtained by applying the irreversible two-tissue compartmental modeling using an in-house Matlab software. The SUV analysis was performed based on conventional static scan data. Differences in each metabolic parameter among the group were analyzed. Wilcoxon rank-sum test, independent-samples T-test, and receiver-operating characteristic (ROC) analysis were performed to compare the diagnostic effects among the differentiated groups. P < 0.05 were considered statistically significant for all statistical tests.ResultsIn the malignant group (N = 124), the SUVmax, k2, k3, and Ki were higher than the benign group (N = 23), and all had-better performance in the differential diagnosis (P < 0.05, respectively). In the AC group (N = 88), the SUVmax, k3, and Ki were lower than in the SCC group, and such differences were statistically significant (P < 0.05, respectively). For ROC analysis, Ki with cut-off value of 0.0250 ml/g/min has better diagnostic specificity than SUVmax (AUC = 0.999 vs. 0.70). In AC group, 48 patients further underwent EGFR testing. In the EGFR (+) group (N = 31), the average Ki (0.0279 ± 0.0153 ml/g/min) was lower than EGFR (-) group (N = 17, 0.0405 ± 0.0199 ml/g/min), and the difference was significant (P < 0.05). However, SUVmax and k3 did not show such a difference between EGFR (+) and EGFR (-) groups (P>0.05, respectively). For ROC analysis, the Ki had a cut-off value of 0.0350 ml/g/min when predicting EGFR status, with a sensitivity of 0.710, a specificity of 0.588, and an AUC of 0.674 [0.523–0.802].ConclusionAlthough both techniques were specific, Ki had a greater specificity than SUVmax when the cut-off value was set at 0.0250 ml/g/min for the differential diagnosis of lung cancer. At a cut-off value of 0.0350 ml/g/min, there was a 0.710 sensitivity for EGFR status prediction. If EGFR testing is not available for a patient, dynamic imaging could be a valuable non-invasive screening method.

Cross-site validation of lung cancer diagnosis by electronic nose with deep learning: a multicenter prospective study

BackgroundAlthough electronic nose (eNose) has been intensively investigated for diagnosing lung cancer, cross-site validation remains a major obstacle to be overcome and no studies have yet been performed.MethodsPatients with lung cancer, as well as healthy control and diseased control groups, were prospectively recruited from two referral centers between 2019 and 2022. Deep learning models for detecting lung cancer with eNose breathprint were developed using training cohort from one site and then tested on cohort from the other site. Semi-Supervised Domain-Generalized (Semi-DG) Augmentation (SDA) and Noise-Shift Augmentation (NSA) methods with or without fine-tuning was applied to improve performance.ResultsIn this study, 231 participants were enrolled, comprising a training/validation cohort of 168 individuals (90 with lung cancer, 16 healthy controls, and 62 diseased controls) and a test cohort of 63 individuals (28 with lung cancer, 10 healthy controls, and 25 diseased controls). The model has satisfactory results in the validation cohort from the same hospital while directly applying the trained model to the test cohort yielded suboptimal results (AUC, 0.61, 95% CI: 0.47─0.76). The performance improved after applying data augmentation methods in the training cohort (SDA, AUC: 0.89 [0.81─0.97]; NSA, AUC:0.90 [0.89─1.00]). Additionally, after applying fine-tuning methods, the performance further improved (SDA plus fine-tuning, AUC:0.95 [0.89─1.00]; NSA plus fine-tuning, AUC:0.95 [0.90─1.00]).ConclusionOur study revealed that deep learning models developed for eNose breathprint can achieve cross-site validation with data augmentation and fine-tuning. Accordingly, eNose breathprints emerge as a convenient, non-invasive, and potentially generalizable solution for lung cancer detection.Clinical trial registrationThis study is not a clinical trial and was therefore not registered.

Surveillance of subsolid nodules avoids unnecessary resections in lung cancer screening: Long-term results of the prospective BioMILD trial

BackgroundThe management of sub-solid nodules (SSNs) in lung cancer screening (LCS) is still a topic of debate, with no current uniform strategy to deal with these lesions at risk of overdiagnosis and overtreatment.ObjectiveThe BioMILD LCS trial has implemented a prospective conservative approach for SSNs, managing with annual low-dose CT nonsolid nodules (NSNs) and part-solid nodules (PSNs) with a solid component &lt;5 mm, regardless of the size of the nonsolid component. The present study aims to determine the lung cancer (LC) detection and survival in BioMILD volunteers with SSNs.Materials and methodsEligible participants were 758 out of 4071 (18.6%) BioMILD volunteers without baseline LC and at least one SSN detected at the baseline or further LDCT rounds. The outcomes of the study were LC detection and long-term survival.ResultsA total of 844 NSNs and 241 PSNs were included. LC detection was 3.7% (31/844) in NSNs and 7.1% (17/241) in PSNs, being significantly greater in prevalent than incident nodules (8.4%versus1.3% in NSNs; 14.1%versus2.1% in PSNs; p-value for both nodule types&lt;0.01). Most LCs from SSNs were stage I (42/48, 87.5%), resectable (47/48, 97.9%), and caused no deaths. The 8-year cumulative survival of volunteers with LC derived from SSNs and not derived from SSNs was 93.8% and 74.9%, respectively.ConclusionConservative management of SSNs in LCS enables timely diagnosis and treatment of LCs arising from SSNs while ensuring the resection of more aggressive LCs detected away from SSNs.

Microbes for lung cancer detection: feasibility and limitations

As the second most common cancer in the world, the development of lung cancer is closely related to factors such as heredity, environmental exposure, and lung microenvironment, etc. Early screening and diagnosis of lung cancer can be helpful for the treatment of patients. Currently, CT screening and histopathologic biopsy are widely used in the clinical detection of lung cancer, but they have many disadvantages such as false positives and invasive operations. Microbes are another genome of the human body, which has recently been shown to be closely related to chronic inflammatory, metabolic processes in the host. At the same time, they are important players in cancer development, progression, treatment, and prognosis. The use of microbes for cancer therapy has been extensively studied, however, the diagnostic role of microbes is still unclear. This review aims to summarize recent research on using microbes for lung cancer detection and present the current shortcomings of microbes in collection and detection. Finally, it also looks ahead to the clinical benefits that may accrue to patients in the future about screening and early detection.

An Overview of Lung Cancer

Lung cancer is characterised by unregulated cell growth. The most prevalent cancer killer worldwide is lung cancer. Lung cancer diagnoses and deaths are rising worldwide. Males and females over 70 have the highest lung cancer risk. Since 50% of lung cancer patients acquire a new cough, smokers or former smokers should be concerned. Lung cancer's complex pathogenesis is yet unknown. Smoking and carcinogen exposure can cause lung epithelial dysplasia. The most prevalent lung cancer diagnosis methods are flexible bronchoscopy and transthoracic sampling. Immunotherapy helps the immune system recognise and fight cancer cells as foreign intruders. Radiation and four to six chemotherapy cycles are usual for mediastinal or hilar lymph node LS-SCLC.

A multichannel analysis of imbalanced computed tomography data for lung cancer classification

Lung cancer holds the highest fatality rate among cancers, emphasizing the importance of early detection. Computer algorithms have gained prominence across various domains, including lung cancer diagnosis. These algorithms assist specialists, especially in medical imaging, yet current efforts lack comprehensive CT data analysis; especially in handling imbalanced datasets and fully exploiting spatial information. The lack of spatial analysis hinders the ability to identify subtle variations in texture and structure that are crucial for detecting lung cancer early and accurately. Therefore, this study uses a multichannel analysis of computed tomography (CT) images and deep learning-based ensemble learning (MC-ECNN) to find lung cancer even when the data is not balanced. Firstly, the data imbalance issue is tackled through the synthetic minority oversampling technique (SOMTE); afterwards, a multi-channel analysis of the data is performed to explore a distinct set of abstract features. Lastly, a deep ensemble learning method is used to classify the extracted distinct abstract feature set into the appropriate classes. The proposed method uses the discrete Fast Fourier transform (DFFT) and discrete cosine transform (DCT), along with the actual CT scans, for the multi-channel analysis of the data in different domains. The proposed model yielded 99.60% test accuracy on unseen data, which is at least 3% better than the other state-of-the-art studies considered for the comparison. In addition to the classification accuracy, the efficacy of the proposed model has also been justified through precision, recall, F1-score, support value, and misclassification rate.

Bronchoalveolar Lavage Fluid Microbiota is Associated with the Diagnosis and Prognosis Evaluation of Lung Cancer

Bronchoalveolar Lavage Fluid Microbiota is Associated with the Diagnosis and Prognosis Evaluation of Lung Cancer

Multiside graph neural network-based attention for local co-occurrence features fusion in lung nodule classification

Early diagnosis of lung cancer is critical as it can save people’s lives. Long-range dependencies within volumetric medical images are essential attributes for accurate lung nodule classification. Many deep learning-based methods are used for lung nodule classification; however, the construction of the lung nodule is not axial and can be any shape. Thus, the nodules are interrelated through their adjacent slices axially and diagonally, locally and globally, making their capture through local convolutional operations challenging. In this article, we benefited from local co-occurrences of texture features and graph neural networks (GNNs) to effectively capture important patterns considering the long-range dependencies among adjusted slices. The proposed framework comprises a multi-side graph construction layer (MSGCL) that computes informative texture features and captures spatial relationships from cross-sectional and longitude orientations of the nodule, creating two sets of nodes. Further, the graph-based fusion of long-range dependency layers (GFLL) is used to deeply fuse and generate attentive edges among fused nodes. The LIDC-IDRI dataset, used for training and testing the proposed Multi-side Graph Neural Network-based Attention for Local Co-occurrence Features Fusion (MS-GNN-ALCFF), achieves state-of-the-art. The LUNGx dataset used as an unseen dataset shows that our model is generalisable compared to baselines. The results of the proposed method when the LIDC-IDRI dataset was used for train and testing were 87.17 ± 0.84 %, 91.01 ± 1.14 %, 88.6 ± 1.42 %, 89.7 ± 0.87 %,95 ± 1.22 % and 76.7 ± 1.4 % in terms of Accuracy, Precision, Recall, F1-score, AUC and MCC respectively. When the LUNGx dataset is used for testing as an unseen dataset, the results were 69.86 ± 2.4 %, 75 ± 2.7 %, 71.4 ± 3.1 %, 73.17 ± 3.7 %, 70.2 ± 1.36 % and 71 ± 3.8 % for Accuracy, Precision, Recall, F1-score, AUC and MCC respectively. These findings represent the significance of the ability of GNNs to construct a multi-set of nodes through the proposed MSGCL layer and fuse deeply through the proposed GFLL layer.

Five years of advances in electrochemical analysis of protein biomarkers in lung cancer: a systematic review

Lung cancer is the leading cause of cancer death in both men and women. It represents a public health problem that must be addressed through the early detection of specific biomarkers and effective treatment. To address this critical issue, it is imperative to implement effective methodologies for specific biomarker detection of lung cancer in real clinical samples. Electrochemical methods, including microfluidic devices and biosensors, can obtain robust results that reduce time, cost, and assay complexity. This comprehensive review will explore specific studies, methodologies, and detection limits and contribute to the depth of the discussion, making it a valuable resource for researchers and clinicians interested in lung cancer diagnosis.

LRFE: A NOVEL LOCAL RESPONSE FEATURE ELIMINATION PROCESS FOR IDENTIFICATION OF LUNG CANCER CELLS

One of the main causes of cancer-related mortality across the globe is lung cancer.  Early-stage lung cancer frequently exhibits no symptoms, which delays diagnosis until the illness has progressed. Before symptoms manifest, screening and early detection techniques can aid in the early diagnosis and treatment of lung cancer. Many olden research papers have implemented image processing and few latest papers have implemented computer vision techniques to detect lung cancer. Particularly when dealing with minor or subtle anomalies, image processing algorithms may not be able to detect lung cancer lesions with sufficient sensitivity and specificity. It is still difficult to increase the detection algorithms' accuracy and dependability, especially when dealing with early-stage lesions or situations where attributes overlap. It takes a lot of processing power, such as high-performance GPUs and enormous memory capacities, to train deep learning models, particularly large-scale convolutional neural networks (CNNs). In this proposed research, the model pre-processes the images using the ostu and sober filter mechanisms because Otsu's approach adjusts to the features of the input image, including noise, contrast, and lighting fluctuations. It is capable of handling images with varying dynamic ranges and intensity distributions without depending on pre-established threshold settings. When it comes to image noise, the Sobel filter is more resilient than other edge detection methods. It produces clearer edge maps and fewer false detections by determining the gradient magnitude, which amplifies edge information while suppressing noise. The features are extracted using the tuned AlexNet pre-trained model, in AlexNet there is a layer known as “Layer-wise Relevance Propagation”. By giving each pixel or feature in the input image a relevance score, the LRP layer offers fine-grained feature attribution. This makes it possible to analyze in great depth which particular elements or areas of the input image are most important for the network to forecast, which helps to clarify the underlying patterns that the network has learned. At last, the extracted features are further reduced using the enhanced feature elimination method. By iteratively selecting subsets of features based on their importance, RFE helps to identify the most relevant features for the classification task. Integrating RFE with SVM classifiers can lead to improved model performance by focusing on the subset of features that are most discriminative and informative for the classification problem.

Diagnosis of Medical Images Using Convolutional Neural Networks

Medical image diagnosis using Convolutional Neural Networks (CNNs) has emerged as a viable way to improve the accuracy and efficiency of disease identification and categorization in clinical settings. In this study, they look at how CNNs can be used to diagnose lung nodules from chest X-ray pictures, to provide insights into the technology's performance and future clinical applications. A dataset of 10,000 tagged chest X-ray pictures showing both benign and malignant lung nodules was obtained and preprocessed using standard methods. The dataset was used to construct and train a proprietary CNN architecture, which was then rigorously evaluated on distinct training, validation, and test sets. The CNN model showed good accuracy (94.8%), sensitivity (92.1%), specificity (96.5%), precision, recall, F1 score, and area under the ROC curve (AUC), indicating its robustness and generalization ability. These findings show that CNN-based diagnostic tools may help radiologists and physicians discover and diagnose lung cancer earlier, improving patient outcomes and optimizing healthcare delivery. However, difficulties such as interpretability, data privacy, and regulatory approval must be addressed before CNNs can be fully utilized in medical imaging. This study emphasizes CNNs' transformative significance in diagnostic medicine and the necessity for additional research and development to realize their full potential in clinical practice.

Enhancing lung lesion localization in CT-scans: a novel approach using FE_CXY and statistical analysis

Intelligence algorithm systems rely on a large dataset to effectively extract significant features that can recognize patterns for classification purposes and extensively utilized to assist the physicians in diagnosis of lung cancer. Extracting valuable features from the available dataset is crucial, especially in cases where additional real data may not be readily accessible. In this context, we propose a novel method called feature extraction based on centroid (FE_CXY) for lesion localization, utilizing a statistical approach. The approach begins with a segmentation process that employs image processing techniques to extract features of interest which is data centroid. This extracted data is then used to compute statistical measurements, revealing hidden patterns that contribute to distinguishing between lesion and non-lesion locations. The method’s efficiency is reflected in the development of robust models with improved performance in localizing lung lesions. The study’s statistical findings strongly indicate that FE_CXY plays a crucial role as an important feature for detecting lesion localization supported by a student’s t-test, which identifies a statistically significant difference in the patterns between lesion and non-lesion localization (p&amp;lt;0.05). By incorporating this method into lung cancer detection systems, we anticipate improved accuracy and efficacy, thereby benefiting early diagnosis and treatment planning.

Human epididymis protein 4, a novel potential biomarker for diagnostic and prognosis monitoring of lung cancer.

This study aimed to explore the application value of human epididymis protein 4 (HE4) in diagnosing and monitoring the prognosis of lung cancer. First, TCGA (The Cancer Genome Atlas) databases were used to analyze whey-acidic-protein 4-disulfide bond core domain 2 (WFDC2) gene expression levels in lung cancer tissues. Then, a total of 160 individuals were enrolled, categorized into three groups: the lung cancer group (n = 80), the benign lesions group (n = 40), and the healthy controls group (n = 40). Serum HE4 levels and other biomarkers were quantified using an electro-chemiluminescent immunoassay. Additionally, the expression of HE4 in tissues was analyzed through immunohistochemistry (IHC). In vitro cultures of human airway epithelial (human bronchial epithelial [HBE]) cells and various lung cancer cell lines (SPC/PC9/A594/H520) were utilized to detect HE4 levels via western blot (WB). Analysis of the TCGA and UALCAN (The University of Alabama at Birmingham Cancer Data Analysis Portal) databases showed that WFDC2 gene expression levels were upregulated in lung cancer tissues (p < 0.01). Compared with the control group and the benign group, HE4 was significantly higher in the serum of patients with lung cancer (p < 0.001). Receiver operating characteristic (ROC) analysis confirmed that HE4 had better diagnostic efficacy than classical markers in the differential diagnosis of lung cancer and benign lesions and had the highest diagnostic value in lung adenocarcinoma (area under the ROC curve [AUC] = 0.826). HE4 increased in early lung cancer and positively correlated with poor prognosis (p < 0.001). Moreover, the results of WB and IHC revealed that the expression of HE4 was increased in lung cancer cells (SPC/A549/H520) and lung cancer tissues but decreased in PC9 cells with a lack of exon EGFR19 (p < 0.05). Serum HE4 emerges as a promising novel biomarker for the diagnosis and prognosis assessment of lung cancer.

A novel microRNA miR-4433a-3p as a potential diagnostic biomarker for lung adenocarcinoma

BackgroundLung adenocarcinoma is one of the leading causes of cancer-related deaths because of the lack of early specific clinical indicators. MicroRNAs (miRNAs) have become the focus in lung cancer diagnosis. Further studies are required to explore miRNA expression in the serum of lung adenocarcinoma patients and their correlation with therapy and analyse specific messenger RNA targets to improve the specificity and sensitivity of early diagnosis. MethodsThe Toray 3D-Gene miRNA array was used to compare the expression levels of various miRNAs in the sera of patients with lung adenocarcinoma and healthy volunteers. Highly expressed miRNAs were selected for further analysis. To verify the screening results, serum and pleural fluid samples were analysed using qRT-PCR. Serum levels of the miRNAs and their correlation with the clinical information of patients with lung adenocarcinoma were analysed. The functions of miRNAs were further analysed using the Kyoto Encyclopedia of Gene and Genomes and Gene Ontology databases. ResultsMicroarray analysis identified 60 and 50 miRNAs with upregulated and downregulated expressions, respectively, in the serum of patients with lung adenocarcinoma compared to those in healthy individuals. Using qRT-qPCR to detection of miRNAs expression in the serum or pleural effusion of patients with early and advanced lung adenocarcinoma, we found that miR-4433a-3p could be used as a diagnostic marker and therapeutic evaluation indicator for lung adenocarcinoma. Serum of miR-4433a-3p levels significantly correlated with the clinical stage. miR-4433a-3p may be more suitable than other tumour markers for the early diagnosis and evaluation of therapeutic effects in lung adenocarcinoma. miR-4433a-3p may affect tumour growth and metastasis by acting on target genes (PIK3CD, UBE2J2, ICMT, PRDM16 and others) and regulating tumour-related signalling pathways (MAPK signal pathway, Ras signalling pathway and others). ConclusionmiR-4433a-3p may serve as a biomarker for the early diagnosis of lung adenocarcinoma and monitoring of therapeutic effects.

Targeted Treatment of Lung Cancer using Nanomaterials: Prospective and Advances

Background: Lung cancer is the second most lethal type of cancer, with a poor survival rate of 5 years. It is one of those malignant tumors that has grown most rapidly in the context of mortality and morbidity. Aim: This review article aims to provide insight into current nanotechnological approaches taken into consideration that provide advantages over conventional chemotherapy. Result and Discussion: After comparing conventional chemotherapy and nanotechnology-based therapies for lung cancer, the results showed that recent advances in nanomaterials proved to be more effective in lung cancer diagnosis, mitigation and treatment. Here, Surface-engineered smart nanocarrier- based inhalations, Bio-nanocarriers for lung cancer, gas plasma nanoparticles, and magnetic nanoparticles are discussed. Conclusion: After summarizing these nanomaterials, investigators concluded that the in-vivo and invitro effectiveness of recently developed nanoparticles was found to be better than that of conventional nanoparticles.

A Clinical-Information-Free Method for Early Diagnosis of Lung Cancer from the Patients with Pulmonary Nodules Based on Backpropagation Neural Network Model

Lung cancer is the main cause of cancer-related deaths worldwide. Due to lack of obvious clinical symptoms in the early stage of the lung cancer, it is hard to distinguish between malignancy and pulmonary nodules. Understanding the immune responses in the early stage of malignant lung cancer patients may provide new insights for diagnosis. Here, using high-through-put sequencing, we obtained the TCRβ repertoires in the peripheral blood of 100 patients with Stage I lung cancer and 99 patients with benign pulmonary nodules. Our analysis revealed that the usage frequencies of TRBV, TRBJ genes, and V-J pairs and TCR diversities indicated by D50s, Shannon indexes, Simpson indexes, and the frequencies of the largest TCR clone in the malignant samples were significantly different from those in the benign samples. Furthermore, reduced TCR diversities were correlated with the size of pulmonary nodules. Moreover, we built a backpropagation neural network model with no clinical information to identify lung cancer cases from patients with pulmonary nodules using 15 characteristic TCR clones. Based on the model, we have created a web server named “Lung Cancer Prediction” (LCP), which can be accessed at http://i.uestc.edu.cn/LCP/index.html.

Ultrasensitive DNA Origami Plasmon Sensor for Accurate Detection in Circulating Tumor DNAs

AbstractEarly lung cancer screening by computed tomography is hampered by pulmonary nodules caused by massive COVID‐19 infections, necessitating an ultrasensitive approach for the early diagnosis of lung cancers at the single‐base level from circular tumor DNAs (ctDNAs). This study introduces an approach that merges DNA origami and DNA scissors technologies in a framework of surface plasmon resonance (SPR) biosensors. By combining the precision of DNA origami probes with the inherent single‐base resolution of DNA scissors, this method systematically addresses the limitations of conventional SPR techniques, resulting in enhanced detection accuracy. The synergistic interplay between DNA scissors and DNA origami enables the SPR biosensors to achieve unprecedented levels of sensitivity, precision, and practical utility. This efficacy allows the precise identification of mutations, demonstrated here by detection of the T790M mutation in the EGFR gene and the G12C mutation in the KRAS gene of non‐small cell lung cancer patients. With this technique, single‐base resolution as well as near zeptomolar‐level sensitivity is achieved. As a result, this discovery holds significant potential to advance the field of precision diagnostics.

Decision-level data fusion based on laser-induced breakdown and Raman spectroscopy: A study of bimodal spectroscopy for diagnosis of lung cancer at different stages

Lung cancer staging is crucial for personalized treatment and improved prognosis. We propose a novel bimodal diagnostic approach that integrates LIBS and Raman technologies into a single platform, enabling comprehensive tissue elemental and molecular analysis. This strategy identifies critical staging elements and molecular marker signatures of lung tumors. LIBS detects concentration patterns of elemental lines including Mg (I), Mg (II), Ca (I), Ca (II), Fe (I), and Cu (II). Concurrently, Raman spectroscopy identifies changes in molecular content, such as phenylalanine (1033 cm−1), tyrosine (1174 cm−1), tryptophan (1207 cm−1), amide III (1267 cm−1), and proteins (1126 cm−1 and 1447 cm−1), among others. The bimodal information is fused using a decision-level Bayesian fusion model, significantly enhancing the performance of the convolutional neural network architecture in classification algorithms, with an accuracy of 99.17 %, sensitivity of 99.17 %, and specificity of 99.88 %. This study provides a powerful new tool for the accurate staging and diagnosis of lung tumors.