What is the most efficient diagnostic test to determine metastasis in lung cancer?
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In conclusion, our results showed the utility of the new bone markers in detecting bone metastasis and suggested that measurement of urinary NTx was valid diagnostic method of bone metastasis from lung cancer. | |
35 Citations | In conclusion, an OSNA assay using CK19 could be effective for molecular diagnosis of lymph node metastasis in lung cancer. |
48 Citations | (18)FDG PET-CT has excellent diagnostic performance for diagnosis of distant metastases in patients with lung cancer. |
EUS-FNA as a first test (after CT) has high diagnostic yield and accuracy for detecting lung cancer metastases to the mediastinum and distant sites. | |
21 Citations | This study provides an efficient and noninvasive strategy to detect tumor metastasis in LNs with greatly enhanced diagnostic accuracy. |
53 Citations | In conclusion, we have developed an efficient and unbiased method to determine the metastasis burden in mouse lungs. |
18 Citations | We describe an algorithm for the diagnosis and tumor-node-metastasis staging of lung cancer that uses procedures with the least invasiveness and cost with the highest diagnostic yields. |
32 Citations | There is an impetus to find a screening test that can detect non-small cell lung cancer in its early preclinical stages, when surgical resection is most likely to reduce lung cancer mortality. |
91 Citations | Further studies with larger cohorts, including also more participants with early‐stage tumors, should be performed to increase the robustness of this noninvasive diagnostic tool and to determine its added value in the diagnostic chain for lung cancer. |
Our results provide the potential to detect early breast cancer lung metastasis, monitor its progression and differentiate it from the same cancer cells grown at other locations. |
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How accurate are diagnostic methods for detecting lung cancer at an early stage?5 answersDiagnostic methods for detecting lung cancer at an early stage have shown varying levels of accuracy. Recent advancements in AI and deep learning algorithms, such as convolutional neural networks (CNNs), have shown promise in improving the accuracy and efficiency of lung cancer detection. One study utilized a deep learning-based CNN algorithm and found that the system showed a significant improvement in accuracy and efficiency compared to traditional detection methods. Another study focused on DNA methylation biomarkers and developed a diagnostic model that achieved high accuracy in distinguishing lung cancers from benign diseases, both in tissue samples and plasma samples. A different approach using cfDNA fragmentomics and machine learning models also demonstrated superior sensitivity for detecting early-stage lung cancer. Additionally, a study evaluating tumor-associated autoantibodies (TAABs) found that a 7-TAAB panel showed promising sensitivity and specificity in detecting lung cancer. These findings suggest that these advanced diagnostic methods have the potential to improve early detection of lung cancer.
What are the most effective lung cancer treatment?4 answersThe most effective treatments for lung cancer include surgical resection, chemotherapy, immunotherapy, radiation therapy, and targeted therapy. Surgical resection is the preferred treatment for early-stage non-small cell lung carcinoma (NSCLC), while stereotactic body radiation therapy is increasingly used for curative intent. Chemotherapy and/or immunotherapy are the standard treatments for patients with distant metastasis who do not have driver mutations, while patients with driver mutations are initially treated with targeted agents. Combination therapy using different anticancer drugs has shown promise in treating lung cancer, with nanocarriers like liposomes being used for targeted treatment. In terms of first-line chemotherapy regimens, nab-paclitaxel–carboplatin has shown longer overall survival compared to gemcitabine–platinum and sb-paclitaxel–carboplatin. Personalized tumor profiling and the development of novel pharmacologic agents targeting specific mutations have also improved outcomes for lung cancer patients.
What are the most effective methods for lung cancer detection?4 answersThe most effective methods for lung cancer detection include image processing and machine learning techniques. Classification algorithms such as support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), and decision tree (DT) methods have been used to classify lung images as malignant or normal based on extracted features. Additionally, deep learning models, such as convolutional neural networks (CNN), have been employed for tumor identification on CT scan images. Hybrid methods combining different features, such as Gray-level co-occurrence (GLCM), autoencoder, and Haralick texture features, have shown high detection performance using SVM with different kernel functions. Biosensor technology has also been utilized for the detection of lung cancer tumor markers, including DNA biosensors, RNA biosensors, and protein biosensors. These methods provide important insights for the early diagnosis of lung cancer.
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