Classification Of Lung Cancer Research Articles

Multi-Tissue Controls and Multiplex Immunocytochemistry in Pulmonary Cytology.

The World Health Organization 2021 lung cancer classification highlights the central role of immunohistochemistry (IHC) in diagnostic pathology. Despite traditional IHC being essential, its limitation to one marker per tissue section brings challenges, particularly when facing cytological limitedly sized samples. To overcome these challenges, multiplex immunocytochemistry (mICC) techniques offer the simultaneous detection of multiple markers from a single section. These advances complement the highly complex imaging techniques that enable additional analyses of cellular interactions. The present study outlines a comprehensive mICC methodology of an automated multiplex immunoperoxidase staining method and multiple tissue hybrid controls for ICC/mICC. Protocols are presented in detail and demonstrate a careful approach to optimizing various markers for diagnostic work-up including immunotherapy. Multiplex IHC/ICC emerges as a transformative force in biomedical diagnostics and research. Beyond simultaneous marker detection, it unravels complexities within tissues-unveiling co-localization nuances, deciphering expression patterns, and enhancing understanding of cellular populations. As personalized treatments gain prominence, the study emphasizes the heightened importance of diagnostic tools and sample adequacy. The present methodological study, encapsulating an automated multiplex immunoperoxidase staining method, symbolizes a stride towards precision in pulmonary carcinoma diagnosis. Multi-tissue controls represent a key element in quality assurance in pathology laboratories.

Label-free surface-enhanced Raman spectroscopy analysis method for liquid biopsy and its application in serum-based lung cancer diagnosis and classification

Liquid biopsy offers promise for the diagnosis of malignant tumors or their precancerous lesions at the subclinical stage, which is crucial for improving the survival rate of cancer. Label-free surface-enhanced Raman spectroscopy (SERS) has become an emerging detection method in liquid biopsy. The accuracy of SERS-based diagnosis relies on both precise measurement and effective data analysis. In our previous study, a large laser spot SERS method was proposed for the precise detection of complex liquids. In this work, a logical Raman data analysis method was developed, to address the lack of Raman characteristics and biological significance in current SERS-based diagnosis data analysis. The measured Raman data is classified to obtain a weighting matrix of classification criteria, and this matrix is used to establish a material correspondence with the Raman spectra, extract key Raman peaks, determine corresponding biomolecules and biological processes, thus enabling a logical analysis of the data. This method has been used to analyze the SERS data of lung cancer, it can not only show exceptional performance in diagnosing lung cancer and differentiating small cell lung cancer, but also obtain the Raman diagnostic and classification criteria with clear numerical values and reasonable biological significance, demonstrating its great potential value in precise biosensing.

Chinese Medical Association guideline for clinical diagnosis and treatment of lung cancer (2024 edition)

To further standardize lung cancer prevention and treatment measures in China, enhance the quality of diagnosis and treatment, improve patient prognosis, and provide evidence-based medical guidance for clinicians at all levels, the Chinese Medical Association convened experts from respiratory medicine, oncology, thoracic surgery, radiotherapy, imaging, and pathology to develop the Chinese Medical Association's Clinical Diagnosis and Treatment Guidelines for Lung Cancer (2024 edition). This consensus resulted in several updates from the 2023 version. The 2024 guidelines highlight that the risk of lung cancer in smokers remains higher than that of non-smokers even 15 years after quitting. Additionally, a new lung cancer incidence risk model is expected to become a critical tool for screening high-risk groups. In pathology, the guidelines now include pathological evaluation of surgically resected lung cancer specimens following neoadjuvant therapy and suggest that immunohistochemical staining of certain transcription factors may aid in the classification of small cell lung cancer (SCLC). In molecular detection, the guidelines propose simultaneous detection of driver gene variations based on both RNA and DNA from specimens. The new edition also provides detailed descriptions of patient selection and surgical requirements for thoracic sub-lobectomy, aligned with the 9th TNM staging. Moreover, the guidelines expand treatment options, approving more therapies for immunoadjuvant and EGFR-TKI resistant lung cancer patients, as well as additional drug options for advanced non-small cell lung cancer (NSCLC) patients with EGFR mutations, EGFR 20 insertions, ALK fusions, and MET exon 14 skipping. These recommendations are based on State-approved drug applications, international guidelines, and current clinical practices in China, integrating the latest evidence-based medical research in screening, diagnosis, pathology, genetic testing, immune molecular marker detection, treatment methods, and follow-up care. The goal is to provide comprehensive and reasonable recommendations for clinicians, imaging specialists, laboratory technicians, rehabilitation professionals, and other medical staff at all levels.

An attention-based neural network for lung cancer classification and gradient in MRI

Accurate lung cancer classification in magnetic resonance imaging (MRI) remains challenging due to the difficulty in detecting cancerous patterns. In response, this study introduces an attention-based VGG19 neural network for enhanced classification performance. Leveraging the VGG19 architecture's deep learning capabilities, our model incorporates attention mechanisms to selectively emphasize salient features during training. The attention-based approach addresses the challenge of discerning subtle patterns indicative of malignancy, significantly improving classification accuracy. We train and evaluate the model on a diverse dataset, ensuring its capacity to generalize across various patient cases. The attention mechanism proves effective in prioritizing critical regions within MRI scans, enhancing sensitivity and specificity in lung cancer detection. Additionally, we employ gradient analysis to interpret the decision-making process, providing valuable insights into influential features. Results demonstrate the proposed model's superiority over baseline approaches, showcasing its efficacy in inaccurate lung cancer classification. The attention-based VGG19 neural network not only advances classification capabilities but also offers interpretability crucial for gaining trust in automated diagnostic systems. This research contributes a robust solution to a pressing medical imaging challenge, holding promise for practical implementation in clinical settings to support radiologists in timely and accurate lung cancer diagnosis.

Explainable lung cancer classification with ensemble transfer learning of VGG16, Resnet50 and InceptionV3 using grad-cam

Medical imaging stands as a critical component in diagnosing various diseases, where traditional methods often rely on manual interpretation and conventional machine learning techniques. These approaches, while effective, come with inherent limitations such as subjectivity in interpretation and constraints in handling complex image features. This research paper proposes an integrated deep learning approach utilizing pre-trained models—VGG16, ResNet50, and InceptionV3—combined within a unified framework to improve diagnostic accuracy in medical imaging. The method focuses on lung cancer detection using images resized and converted to a uniform format to optimize performance and ensure consistency across datasets. Our proposed model leverages the strengths of each pre-trained network, achieving a high degree of feature extraction and robustness by freezing the early convolutional layers and fine-tuning the deeper layers. Additionally, techniques like SMOTE and Gaussian Blur are applied to address class imbalance, enhancing model training on underrepresented classes. The model’s performance was validated on the IQ-OTH/NCCD lung cancer dataset, which was collected from the Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases over a period of three months in fall 2019. The proposed model achieved an accuracy of 98.18%, with precision and recall rates notably high across all classes. This improvement highlights the potential of integrated deep learning systems in medical diagnostics, providing a more accurate, reliable, and efficient means of disease detection.

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.

Deep learning for lungs cancer detection: a review

Although lung cancer has been recognized to be the deadliest type of cancer, a good prognosis and efficient treatment depend on early detection. Medical practitioners’ burden is reduced by deep learning techniques, especially Deep Convolutional Neural Networks (DCNN), which are essential in automating the diagnosis and classification of diseases. In this study, we use a variety of medical imaging modalities, including X-rays, WSI, CT scans, and MRI, to thoroughly investigate the use of deep learning techniques in the field of lung cancer diagnosis and classification. This study conducts a comprehensive Systematic Literature Review (SLR) using deep learning techniques for lung cancer research, providing a comprehensive overview of the methodology, cutting-edge developments, quality assessments, and customized deep learning approaches. It presents data from reputable journals and concentrates on the years 2015–2024. Deep learning techniques solve the difficulty of manually identifying and selecting abstract features from lung cancer images. This study includes a wide range of deep learning methods for classifying lung cancer but focuses especially on the most popular method, the Convolutional Neural Network (CNN). CNN can achieve maximum accuracy because of its multi-layer structure, automatic learning of weights, and capacity to communicate local weights. Various algorithms are shown with performance measures like precision, accuracy, specificity, sensitivity, and AUC; CNN consistently shows the greatest accuracy. The findings highlight the important contributions of DCNN in improving lung cancer detection and classification, making them an invaluable resource for researchers looking to gain a greater knowledge of deep learning’s function in medical applications.

An Improved Archimedes Optimization-aided Multi-scale Deep Learning Segmentation with dilated ensemble CNN classification for detecting lung cancer using CT images

ABSTRACT Early detection of lung cancer is necessary to prevent deaths caused by lung cancer. But, the identification of cancer in lungs using Computed Tomography (CT) scan based on some deep learning algorithms does not provide accurate results. A novel adaptive deep learning is developed with heuristic improvement. The proposed framework constitutes three sections as (a) Image acquisition, (b) Segmentation of Lung nodule, and (c) Classifying lung cancer. The raw CT images are congregated through standard data sources. It is then followed by nodule segmentation process, which is conducted by Adaptive Multi-Scale Dilated Trans-Unet3+. For increasing the segmentation accuracy, the parameters in this model is optimized by proposing Modified Transfer Operator-based Archimedes Optimization (MTO-AO). At the end, the segmented images are subjected to classification procedure, namely, Advanced Dilated Ensemble Convolutional Neural Networks (ADECNN), in which it is constructed with Inception, ResNet and MobileNet, where the hyper parameters is tuned by MTO-AO. From the three networks, the final result is estimated by high ranking-based classification. Hence, the performance is investigated using multiple measures and compared among different approaches. Thus, the findings of model demonstrate to prove the system’s efficiency of detecting cancer and help the patient to get the appropriate treatment.

Predictors of lung cancer subtypes and lymph node status in non-small-cell lung cancer: intravoxel incoherent motion parameters and extracellular volume fraction

ObjectiveTo determine the performance of intravoxel incoherent motion (IVIM) parameters and the extracellular volume fraction (ECV) in distinguishing between different subtypes of lung cancer and predicting lymph node metastasis (LNM) status in patients with non-small-cell lung cancer (NSCLC).MethodsOne hundred sixteen patients with lung cancer were prospectively recruited. IVIM, native, and postcontrast T1 mapping examinations were performed, and the T1 values were measured to calculate the ECV. The differences in IVIM parameters and ECV were compared between NSCLC and small-cell lung cancer (SCLC), adenocarcinoma (Adeno-Ca) and squamous cell carcinoma (SCC), and NSCLC without and with LNM. The assessment of each parameter’s diagnostic performance was based on the area under the receiver operating characteristic curve (AUC).ResultsThe apparent diffusion coefficient (ADC), true diffusion coefficient (D), and ECV values in SCLC were considerably lower compared with NSCLC (all p < 0.001, AUC > 0.887). The D value in SCC was substantially lower compared with Adeno-Ca (p < 0.001, AUC = 0.735). The perfusion fraction (f) and ECV values in LNM patients were markedly higher compared with those without LNM patients (p < 0.01, < 0.001, AUC > 0.708).ConclusionIVIM parameters and ECV can serve as non-invasive biomarkers for assisting in the pathological classification and LNM status assessment of lung cancer patients.Critical relevance statementIVIM parameters and ECV demonstrated remarkable potential in distinguishing pulmonary carcinoma subtypes and predicting LNM status in NSCLC.Key PointsLung cancer is prevalent and differentiating subtype and invasiveness determine the treatment course.True diffusion coefficient and ECV showed promise for subtyping and determining lymph node status.These parameters could serve as non-invasive biomarkers to help determine personalized treatment strategies.Graphical

Bibliometric analysis of the application of deep learning in cancer from 2015 to 2023

BackgroundRecently, the application of deep learning (DL) has made great progress in various fields, especially in cancer research. However, to date, the bibliometric analysis of the application of DL in cancer is scarce. Therefore, this study aimed to explore the research status and hotspots of the application of DL in cancer.MethodsWe retrieved all articles on the application of DL in cancer from the Web of Science database Core Collection database. Biblioshiny, VOSviewer and CiteSpace were used to perform the bibliometric analysis through analyzing the numbers, citations, countries, institutions, authors, journals, references, and keywords.ResultsWe found 6,016 original articles on the application of DL in cancer. The number of annual publications and total citations were uptrend in general. China published the greatest number of articles, USA had the highest total citations, and Saudi Arabia had the highest centrality. Chinese Academy of Sciences was the most productive institution. Tian, Jie published the greatest number of articles, while He Kaiming was the most co-cited author. IEEE Access was the most popular journal. The analysis of references and keywords showed that DL was mainly used for the prediction, detection, classification and diagnosis of breast cancer, lung cancer, and skin cancer.ConclusionsOverall, the number of articles on the application of DL in cancer is gradually increasing. In the future, further expanding and improving the application scope and accuracy of DL applications, and integrating DL with protein prediction, genomics and cancer research may be the research trends.

Non-small cell lung cancer cells and concomitant cancer therapy induce a resistance-promoting phenotype of tumor-associated mesenchymal stem cells.

The tumor microenvironment gained attraction over the last decades as stromal cells significantly impact on tumor development, progression and metastasis, and immune evasion as well as on cancer therapy resistance. We previously reported that lung-resident mesenchymal stem cells (MSCs) were mobilized and activated in non-small cell lung cancer (NSCLC) progression and could even mediate radiation resistance in co-cultured NSCLC cells. We investigated how MSCs were affected by NSCLC cells in combination with cancer (radiation) therapy in indirect co-cultures using tumor-conditioned medium and Transwells or direct three-dimensional NSCLC-MSC spheroid co-cultures in order to unravel the resistance-mediating action of tumor-associated MSCs. Although no obvious phenotypic and functional alterations in MSCs following NSCLC co-culture could be observed, MSC senescence was induced following co-applied radiotherapy (RT). Global gene expression profiling, in combination with gene set enrichment analysis upon treatment, was used to confirm the senescent phenotype of irradiated MSC and to reveal relevant senescence-associated secretory phenotype (SASP) factors that could meditate NSCLC RT resistance. We identified senescent tumor-associated MSC-derived serine proteinase inhibitor (serpin) E1/PAI1 as potential SASP factor mediating NSCLC progression and RT resistance. Specified intra-tumor-stroma interactions and cell type-specific pro-tumorigenic functions could not only improve lung cancer classification but could even be used for a more precise profiling of individual patients, finally paving an additional way for the discovery of potential drug targets for NSCLC patients.

Lung Cancer Detection and Classification Using CNN

Abstract: Recognized for its severity and widespread impact, cancer remains a leading cause of death worldwide, claiming countless lives annually. Touching upon cancer genres, pleura tumor tops the list as the most widespread and deadliest form. In the detection of pleura tumor, CT scans (computed topography) play a crucial role by offering a detailed view of tumor presence and progression. Although CT scans function as primary choice pleura tumor determination, the subjective nature of visual analysis can introduce inaccuracies and obstacles. Thus, the use of image processing methods has become prevalent in medaical research, particularly for the early determination of pleura tumor. This paper presents a new methodology for determine pleura cancer from given inputs. This study presents an algorithm aimed at detecting pleura tumor, which incorporates methods such as median filtering to preprocess images and a set of techniques used in image processing and computer vision for analyzing and manipulating shapes within images are employed to divide the bronchial area of focus. Geometrical features derived from this segmented area are then utilized in a support vector machine classifier to discern between healthy and peculiar CT scan images

Lung Cancer classification using an ensemble of CNNs Method in CT Scan Images

Lung Cancer classification using an ensemble of CNNs Method in CT Scan Images

Lungs Tumor Classification using Convolutional Neural Network

The research focuses on classifying lung cancer using the VGG-19 architecture. The datasets were sourced from Iraq-Oncology Teaching Hospital with 70% of the data allocated for training and 30% for testing. Performance metrics were computed to evaluate the effectiveness of the classification method. Python is utilized for designing the algorithm and executed using Goggle Colab. The lung tumor classification using VGG-19 offers an accuracy of 95%, sensitivity of 88.79%, specificity of 98.25 %, and F1-Score of 93.28%. However, the low sensitivity value indicates that the VGG-19 architecture is not accurately predicting benign and malignant cases.

Optimizing double-layered convolutional neural networks for efficient lung cancer classification through hyperparameter optimization and advanced image pre-processing techniques.

Lung cancer remains a leading cause of cancer-related mortality globally, with prognosis significantly dependent on early-stage detection. Traditional diagnostic methods, though effective, often face challenges regarding accuracy, early detection, and scalability, being invasive, time-consuming, and prone to ambiguous interpretations. This study proposes an advanced machine learning model designed to enhance lung cancer stage classification using CT scan images, aiming to overcome these limitations by offering a faster, non-invasive, and reliable diagnostic tool. Utilizing the IQ-OTHNCCD lung cancer dataset, comprising CT scans from various stages of lung cancer and healthy individuals, we performed extensive preprocessing including resizing, normalization, and Gaussian blurring. A Convolutional Neural Network (CNN) was then trained on this preprocessed data, and class imbalance was addressed using Synthetic Minority Over-sampling Technique (SMOTE). The model's performance was evaluated through metrics such as accuracy, precision, recall, F1-score, and ROC curve analysis. The results demonstrated a classification accuracy of 99.64%, with precision, recall, and F1-score values exceeding 98% across all categories. SMOTE significantly enhanced the model's ability to classify underrepresented classes, contributing to the robustness of the diagnostic tool. These findings underscore the potential of machine learning in transforming lung cancer diagnostics, providing high accuracy in stage classification, which could facilitate early detection and tailored treatment strategies, ultimately improving patient outcomes.

Lung Cancer Classification based on Auxiliary Classifier (WGAN) Optimised with HOA from CT Images

ABSTRACT The cancer diagnosis is currently experiencing an alteration of molecular biomarkers using s paradigm for the diagnostic panel. One of the most important genomic datasets showing genetic sequences is the classification of lung cancer. Many research studies have been conducted in this area, but none of them yields satisfied outcomes on account of lower classification accuracy. At first, the data are collected through the lung cancer dataset. Afterward, data are fed into pre-processing. The pre-processing segment removes the noise and enhances the input images utilising Dynamic Context-Sensitive Filtering. The pre-processing output is fed to the feature extraction segment. Here, four statistical features are extracted based on the Force Invariant Improved Feature Extraction Model. The extracting features are fed to the feature segmentation segment. Lung cancer images can segment the ROI region using Adaptive Density-Based Spatial Clustering. After that, the segmentation features are given to ACWGAN for effectively categorizing cancer and non-cancer of the lung cancer Hence, the Hunter–prey optimisation algorithm is employed to optimise the ACWGAN classifier. It classifies the lung cancer images accurately. The LCC-ACWGAN-HPOA-CTI algorithm is activated in MATLAB under the lung cancer database. The performance of the LCC-ACWGAN-HPOA-CTI approach shows higher accuracy and lower computation time than those of the existing methods.

Automated histopathological detection and classification of lung cancer with an image pre-processing pipeline and spatial attention with deep neural networks

Automated histopathological detection and classification of lung cancer with an image pre-processing pipeline and spatial attention with deep neural networks

A Multichannel CT and Radiomics-Guided CNN-ViT (RadCT-CNNViT) Ensemble Network for Diagnosis of Pulmonary Sarcoidosis.

Pulmonary sarcoidosis is a multisystem granulomatous interstitial lung disease (ILD) with a variable presentation and prognosis. The early accurate detection of pulmonary sarcoidosis may prevent progression to pulmonary fibrosis, a serious and potentially life-threatening form of the disease. However, the lack of a gold-standard diagnostic test and specific radiographic findings poses challenges in diagnosing pulmonary sarcoidosis. Chest computed tomography (CT) imaging is commonly used but requires expert, chest-trained radiologists to differentiate pulmonary sarcoidosis from lung malignancies, infections, and other ILDs. In this work, we develop a multichannel, CT and radiomics-guided ensemble network (RadCT-CNNViT) with visual explainability for pulmonary sarcoidosis vs. lung cancer (LCa) classification using chest CT images. We leverage CT and hand-crafted radiomics features as input channels, and a 3D convolutional neural network (CNN) and vision transformer (ViT) ensemble network for feature extraction and fusion before a classification head. The 3D CNN sub-network captures the localized spatial information of lesions, while the ViT sub-network captures long-range, global dependencies between features. Through multichannel input and feature fusion, our model achieves the highest performance with accuracy, sensitivity, specificity, precision, F1-score, and combined AUC of 0.93 ± 0.04, 0.94 ± 0.04, 0.93 ± 0.08, 0.95 ± 0.05, 0.94 ± 0.04, and 0.97, respectively, in a five-fold cross-validation study with pulmonary sarcoidosis (n = 126) and LCa (n = 93) cases. A detailed ablation study showing the impact of CNN + ViT compared to CNN or ViT alone, and CT + radiomics input, compared to CT or radiomics alone, is also presented in this work. Overall, the AI model developed in this work offers promising potential for triaging the pulmonary sarcoidosis patients for timely diagnosis and treatment from chest CT.

Significance of metastatic lymph nodes ratio in overall survival for patients with resected nonsmall cell lung cancer: a retrospective cohort study.

The tumor, node and metastasis stage is widely applied to classify lung cancer and is the foundation of clinical decisions. However, increasing studies have pointed out that this staging system is not precise enough for the N status. In this study, we aim to build a convenient survival prediction model that incorporates the current items of lymph node status. We performed a retrospective cohort study and collected the data from resectable nonsmall cell lung cancer (NSCLC) (IA-IIIB) patients from the Surveillance, Epidemiology, and End Results database (2006-2015). The x-tile program was applied to calculate the optimal threshold of metastatic lymph node ratio (MLNR). Then, independent prognostic factors were determined by multivariable Cox regression analysis and enrolled to build a nomogram model. The calibration curve as well as the Concordance Index (C-index) were selected to evaluate the nomogram. Finally, patients were grouped based on their specified risk points and divided into three risk levels. The prognostic value of MLNR and examined lymph node numbers (ELNs) were presented in subgroups. 40853 NSCLC patients after surgery were finally enrolled and analyzed. Age, metastatic lymph node ratio, histology type, adjuvant treatment and American Joint Committee on Cancer 8th T stage were deemed as independent prognostic parameters after multivariable Cox regression analysis. A nomogram was built using those variables, and its efficiency in predicting patients' survival was better than the conventional American Joint Committee on Cancer stage system after evaluation. Our new model has a significantly higher concordance Index (C-index) (training set, 0.683 v 0.641, respectively; P < 0.01; testing set, 0.676 v 0.638, respectively; P < 0.05). Similarly, the calibration curve shows the nomogram was in better accordance with the actual observations in both cohorts. Then, after risk stratification, we found that MLNR is more reliable than ELNs in predicting overall survival. We developed a nomogram model for NSCLC patients after surgery. This novel and useful tool outperforms the widely used tumor, node and metastasis staging system and could benefit clinicians in treatment options and cancer control.

Role of Computed Tomography Imaging for the Diagnosis and Classification of Lung Cancer using Machine Learning

Role of Computed Tomography Imaging for the Diagnosis and Classification of Lung Cancer using Machine Learning