Classification Of Lung Cancer Research Articles

Classification of Lung Cancer using Pre-Trained Deep Learning Models

Many Computer Assisted Diagnosis (CAD) systems have been designed and used in recent past for diagnosingdifferenttypes of cancer. Identification of carcinoma at an earlier stage is more important, and it is made possible due to the use modern image processing and deep learning methods.The occurrence of Lung cancer is seen to be increased and Computed Tomography (CT) scan images were utilized in investigation to locate and classify lung cancer, also for determining the severity of those cancer. This study is aimed at employing pre-trained deep neural networks for classification of lung cancer images. A gaussian-based approach is used to segment CT scan images. In this research exploits a transfer learning-based classification method for the chest CT images acquired from Cancer Image Archive and available in the Kaggle platform. Pre- trained models VGG and RESNET were trained using segmented chest CT images, and their performance was evaluated using different optimization algorithms were analyzed. Keywords: Computer Aided Diagnosis, lung cancer, deep learning, CT image, gaussian, transfer learning, pre-trained models, optimization algorithms.

MeVs-deep CNN: optimized deep learning model for efficient lung cancer classification

MeVs-deep CNN: optimized deep learning model for efficient lung cancer classification

Human lung cancer classification and comprehensive analysis using different machine learning techniques.

Lung cancer is the most common causes of death among all cancer-related diseases. A lung scan examination of the patient is the primary diagnostic technique. This scan analysis pertains to an MRI, CT, or X-ray. The automated classification of lung cancer is difficult due to the involvement of multiple steps in imaging patients' lungs. In this manuscript, human lung cancer classification and comprehensive analysis using different machine learning techniques is proposed. Initially, the input images are gathered using lung cancer dataset. The proposed method processes these images using image-processing techniques, and further machine learning techniques are utilized for categorization. Seven different classifiers including the k-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), multinomial naive Bayes (MNB), stochastic gradient descent (SGD), random forest (RF), and multi-layer perceptron (MLP) classifier are used, which classifies the lung cancer as malignant and benign. The performance of the proposed approach is examined using performances metrics, like positive predictive value, accuracy, sensitivity, and f-score are evaluated. Among them, the performance of the MLP classifier provides 25.34%, 45.39%, 15.39%, 41.28%, 22.17%, and 12.12% higher accuracy than other KNN, SVM, DT, MNB, SGD, and RF respectively. RESEARCH HIGHLIGHTS: Lung cancer is a leading cause of cancer-related death. Imaging (MRI, CT, and X-ray) aids diagnosis. Automated classification of lung cancer faces challenges due to complex imaging steps. This study proposes human lung cancer classification using diverse machine learning techniques. Input images from lung cancer dataset undergo image processing and machine learning. Classifiers like k-nearest neighbors, support vector machine, decision tree, multinomial naive Bayes, stochastic gradient descent, random forest, and multi-layer perceptron (MLP) classify cancer types; MLP excels in accuracy.

Lung cancer classification model using convolutional neural network with feature ranking process

Lung cancer classification model using convolutional neural network with feature ranking process

An enhanced Garter Snake Optimization-assisted deep learning model for lung cancer segmentation and classification using CT images

An early detection of lung tumors is critical for better treatment results, and CT scans can reveal lumps in the lungs which are too small to be picked up by conventional X-rays. CT imaging has advantages, but it also exposes a person to radiation from ions, which raises the possibility of malignancy, particularly when the imaging procedure is done. Access to expensive-quality CT scans and the related sophisticated analytic tools might be restricted in environments with fewer resources due to their high cost and limited availability. It will need an array of creative technological innovations to overcome such weaknesses. This paper aims to design a heuristic and deep learning-aided lung cancer classification using CT images. The collected images are undergone for segmentation, which is performed by Shuffling Atrous Convolutional (SAC) based ResUnet++ (SACRUnet++). Finally, the lung cancer classification is performed by the Adaptive Residual Attention Network (ARAN) by inputting the segmented images. Here the parameters of ARAN are optimally tuned using the Improved Garter Snake Optimization Algorithm (IGSOA). The developed lung cancer classification performance is compared to conventional lung cancer classification models and it showed high accuracy.

Evolving therapeutic landscape of EGFR-TKIs in NSCLC

Lung cancer is one of the most common cancers worldwide and the leading cause of cancer-related death. Over the past two decades, the classification of lung cancer has significantly evolved. Today, non-small cell lung cancer (NSCLC) consists of various molecular oncogenic subsets that impact both prognosis and disease management. EGFR is the first targeted oncogenic alteration identified in 2004. Since then, nearly two decades of research have enabled scientists to understand its biological function and to identify and often overcome the molecular basis of acquired resistance mechanisms to EGFR-TKIs. This article reviews the role of EGFR in NSCLC and the research progress of EGFR-TKIs in patients with EGFR mutant lung cancer, discussing potential treatment strategies for drug resistance to improve survival and achieve precision drug use.

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 guideline for clinical diagnosis and treatment of lung cancer (2024 edition). This consensus resulted in several updates from the 2023 edition. 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.

The prognosis and metabolite changes of NSCLC patients receiving first-line immunotherapy combined chemotherapy in different M1c categories according to 9th edition of TNM classification.

The 9th edition of the TNM Classification for lung cancer delineates M1c into two subcategories: M1c1 (Multiple extrathoracic lesions within a single organ system) and M1c2 (Multiple extrathoracic lesions involving multiple organ systems). Existing research indicates that patients with lung cancer in stage M1c1 exhibit superior overall survival compared to those in stage M1c2. The primary frontline therapy for patients with advanced non-small cell lung cancer (NSCLC), lacking driver gene mutations, involves the use of immune checkpoint inhibitors (ICIs) combined with chemotherapy. Nevertheless, a dearth of evidence exists regarding potential survival disparities between NSCLC patients with M1c1 and M1c2 undergoing first-line immune-chemotherapy, and reliable biomarkers for predicting treatment outcomes are elusive. Serum metabolic profiles may elucidate distinct prognostic mechanisms, necessitating the identification of divergent metabolites in M1c1 and M1c2 undergoing combination therapy. This study seeks to scrutinize survival discrepancies between various metastatic patterns (M1c1 and M1c2) and pinpoint metabolites associated with treatment outcomes in NSCLC patients undergoing first-line ICIs combined with chemotherapy. In this study, 33 NSCLC patients lacking driver gene mutations diagnosed with M1c1, and 22 similarly diagnosed with M1c2 according to the 9th edition of TNM Classification, were enrolled. These patients received first-line PD-1 inhibitor plus chemotherapy. The relationship between metastatic patterns and progression-free survival (PFS) in patients undergoing combination therapy was analyzed using univariate and multivariate Cox regression models. Serum samples were obtained from all patients before treatment initiation for untargeted metabolomics analysis, aiming to identify differential metabolites. In the univariate analysis of PFS, NSCLC patients in M1c1 receiving first-line PD-1 inhibitor plus chemotherapy exhibited an extended PFS (HR = 0.49, 95% CI, 0.27-0.88, p = 0.017). In multivariate PFS analyses, these M1c1 patients receiving first-line PD-1 inhibitor plus chemotherapy also demonstrated prolonged PFS (HR = 0.45, 95% CI, 0.22-0.92, p = 0.028). The serum metabolic profiles of M1c1 and M1c2 undergoing first-line PD-1 inhibitors plus chemotherapy displayed notable distinctions. In comparison to M1c1 patients, M1c2 patients exhibited alterations in various pathways pretreatment, including platelet activation, linoleic acid metabolism, and the VEGF signaling pathway. Diminished levels of lipid-associated metabolites (diacylglycerol, sphingomyelin) were correlated with adverse outcomes. NSCLC patients in M1c1, devoid of driver gene mutations, receiving first-line PD-1 inhibitors combined with chemotherapy, experienced superior outcomes compared to M1c2 patients. Moreover, metabolomic profiles strongly correlated with the prognosis of these patients, and M1c2 patients with unfavorable outcomes manifested distinct changes in metabolic pathways before treatment. These changes predominantly involved alterations in lipid metabolism, such as decreased diacylglycerol and sphingomyelin, which may impact tumor migration and invasion.

Vision Transformer Based Effective Model for Early Detection and Classification of Lung Cancer

Vision Transformer Based Effective Model for Early Detection and Classification of Lung Cancer

Machine learning enabled classification of lung cancer cell lines co-cultured with fibroblasts with lightweight convolutional neural network for initial diagnosis

BackgroundIdentification of lung cancer subtypes is critical for successful treatment in patients, especially those in advanced stages. Many advanced and personal treatments require knowledge of specific mutations, as well as up- and down-regulations of genes, for effective targeting of the cancer cells. While many studies focus on individual cell structures and delve deeper into gene sequencing, the present study proposes a machine learning method for lung cancer classification based on low-magnification cancer outgrowth patterns in a 2D co-culture environment.MethodsUsing a magnetic well plate holder, circular pattern lung cancer cell clusters were generated among fibroblasts, and daily images were captured to monitor cancer outgrowth over a 9-day period. These outgrowth images were then augmented and used to train a convolutional neural network (CNN) model based on the lightweight TinyVGG architecture. The model was trained with pairs of classes representing three subtypes of NSCLC: A549 (adenocarcinoma), H520 (squamous cell carcinoma), and H460 (large cell carcinoma). The objective was to assess whether this lightweight machine learning model could accurately classify the three lung cancer cell lines at different stages of cancer outgrowth. Additionally, cancer outgrowth images of two patient-derived lung cancer cells, one with the KRAS oncogene and the other with the EGFR oncogene, were captured and classified using the CNN model. This demonstration aimed to investigate the translational potential of machine learning-enabled lung cancer classification.ResultsThe lightweight CNN model achieved over 93% classification accuracy at 1 day of outgrowth among A549, H460, and H520, and reached 100% classification accuracy at 7 days of outgrowth. Additionally, the model achieved 100% classification accuracy at 4 days for patient-derived lung cancer cells. Although these cells are classified as Adenocarcinoma, their outgrowth patterns vary depending on their oncogene expressions (KRAS or EGFR).ConclusionsThese results demonstrate that the lightweight CNN architecture, operating locally on a laptop without network or cloud connectivity, can effectively create a machine learning-enabled model capable of accurately classifying lung cancer cell subtypes, including those derived from patients, based upon their outgrowth patterns in the presence of surrounding fibroblasts. This advancement underscores the potential of machine learning to enhance early lung cancer subtyping, offering promising avenues for improving treatment outcomes in advanced stage-patients.

Performance analysis of lung cancer detection and classification using efficientNet: a deep learning model

Performance analysis of lung cancer detection and classification using efficientNet: a deep learning model

Optimal Deep Belief Network with Opposition Based Pity Beetle Algorithm for Lung Cancer Classification: A DBNOPBA Approach – Short Review

Optimal Deep Belief Network with Opposition Based Pity Beetle Algorithm for Lung Cancer Classification: A DBNOPBA Approach – Short Review

Breaking Barriers in Cancer Diagnosis: Super‐Light Compact Convolution Transformer for Colon and Lung Cancer Detection

ABSTRACTAccording to the World Health Organization, lung and colon cancers are known for their high mortality rates which necessitate the diagnosis of these cancers at an early stage. However, the limited availability of data such as histopathology images used for diagnosis of these cancers, poses a significant challenge while developing computer‐aided detection system. This makes it necessary to keep a check on the number of parameters in the artificial intelligence (AI) model used for the detection of these cancers considering the limited availability of the data. In this work, a customised compact and efficient convolution transformer architecture, termed, C3‐Transformer has been proposed for the diagnosis of colon and lung cancers using histopathological images. The proposed C3‐Transformer relies on convolutional tokenisation and sequence pooling approach to keep a check on the number of parameters and to combine the advantage of convolution neural network with the advantages of transformer model. The novelty of the proposed method lies in efficient classification of colon and lung cancers using the proposed C3‐Transformer architecture. The performance of the proposed method has been evaluated on the ‘LC25000’ dataset. Experimental results shows that the proposed method has been able to achieve average classification accuracy, precision and recall value of 99.30%, 0.9941 and 0.9950, in classifying the five different classes of colon and lung cancer with only 0.0316 million parameters. Thus, the present computer‐aided detection system developed using proposed C3‐Transformer can efficiently detect the colon and lung cancers using histopathology images with high detection accuracy.

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

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

Deep learning ensemble approach with explainable AI for lung and colon cancer classification using advanced hyperparameter tuning

Lung and colon cancers are leading contributors to cancer-related fatalities globally, distinguished by unique histopathological traits discernible through medical imaging. Effective classification of these cancers is critical for accurate diagnosis and treatment. This study addresses critical challenges in the diagnostic imaging of lung and colon cancers, which are among the leading causes of cancer-related deaths worldwide. Recognizing the limitations of existing diagnostic methods, which often suffer from overfitting and poor generalizability, our research introduces a novel deep learning framework that synergistically combines the Xception and MobileNet architectures. This innovative ensemble model aims to enhance feature extraction, improve model robustness, and reduce overfitting.Our methodology involves training the hybrid model on a comprehensive dataset of histopathological images, followed by validation against a balanced test set. The results demonstrate an impressive classification accuracy of 99.44%, with perfect precision and recall in identifying certain cancerous and non-cancerous tissues, marking a significant improvement over traditional approach.The practical implications of these findings are profound. By integrating Gradient-weighted Class Activation Mapping (Grad-CAM), the model offers enhanced interpretability, allowing clinicians to visualize the diagnostic reasoning process. This transparency is vital for clinical acceptance and enables more personalized, accurate treatment planning. Our study not only pushes the boundaries of medical imaging technology but also sets the stage for future research aimed at expanding these techniques to other types of cancer diagnostics.

Lung Cancer Classification Using Combination Of Efficientnet And Visual Geometry Group Algorithm

Lung cancer is one of the leading causes of mortality All around the world. It is classified into three main types: Adenocarcinoma of the lung (ACA), Non-small cell lung cancer (N), and Squamous Cell Carcinoma of the lung (SCC). Lung Cancer Classification is crucial on development of effective treatments. This study aims to improve the accuracy of lung cancer classification through the integration of a hybrid model, which combines two Convolutional Neural Networks architectures, namely EfficientNet-B7 and VGG-16. A set of histopathology images was subjected to testing, with the data split into three categories: 60% for training, 30% for validation, and 10% for testing. Prior to use, each image underwent a preprocessing process, wherein it was resized to 256x256 pixels. The model test results achieved an accuracy, precision, recall, and F1-score of 98.73%, which is superior to the EfficientNet-B7 base model. The findings of this study demonstrate the potential of hybrid models to improve accuracy in lung cancer classification. The utilization of hybrid models has the potential to contribute significantly to the beginning diagnosis and appropriate Lung Cancer Therapies. Future research will focus on improving the model through the application of image segmentation techniques and expanding the scope of classification to other types of lung cancer. Optimization of the hybrid model architecture using novel techniques such as the attention mechanism or transfer learning will be conducted to improve the efficiency and accuracy of the model. Additionally, a system that can be integrated into clinical practice will be developed

Application of computed tomography-based radiomics analysis combined with lung cancer serum tumor markers in the identification of lung squamous cell carcinoma and lung adenocarcinoma.

To establish a prediction model of lung cancer classification by computed tomography (CT) radiomics with the serum tumor markers (STM) of lung cancer. Two-hundred NSCLC patients were enrolled in our study. Clinical data including age, sex, and STM (squamous cell carcinoma [SCC], neuron-specific enolase [NSE], carcinoembryonic antigen [CEA], pro-gastrin-releasing peptide [PRO-GRP], and cytokeratin 19 fragment [cYFRA21-1]) were collected. A radiomics signature was generated from the training set using the least absolute shrinkage and selection operator (LASSO) algorithm. The risk factors were identified using multivariate logistic regression analysis, and a radiomics nomogram based on the radiomics signature and clinical features was constructed. The capability of the nomogram was evaluated using the training set and validated using the validation set. A correction curve and the Hosmer-Lemeshow test were used to evaluate the predictive performance of the radiomics model for the training and test sets. Twenty-nine of 1234 radiomics parameters were screened as important factors for establishing the radiomics model. The training (area under the curve [AUC] = 0.925; 95% confidence interval [CI]: 0.885-0.966) and validation sets (AUC = 0.921; 95% CI: 0.854-0.989) showed that the CT radiomics signature, combined with STM, accurately predicted lung squamous cell carcinoma and lung adenocarcinoma. Moreover, the logistic regression model showed good performance based on the Hosmer-Lemeshow test in the training (P = 0.954) and test sets (P = 0.340). Good calibration curve consistency also indicated the good performance of the nomogram. The combination of the CT radiomics signature and lung cancer STM performed well for the pathological classification of NSCLC. Compared with the radiomics signature method, the nomogram based on the radiomics signature and clinical factors had better performance for the differential diagnosis of NSCLC.

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 workup 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.