Cancers, Free Full-Text
Por um escritor misterioso
Last updated 06 julho 2024
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Lung cancer remains one of the leading causes of cancer-related deaths worldwide, emphasizing the need for improved diagnostic and treatment approaches. In recent years, the emergence of artificial intelligence (AI) has sparked considerable interest in its potential role in lung cancer. This review aims to provide an overview of the current state of AI applications in lung cancer screening, diagnosis, and treatment. AI algorithms like machine learning, deep learning, and radiomics have shown remarkable capabilities in the detection and characterization of lung nodules, thereby aiding in accurate lung cancer screening and diagnosis. These systems can analyze various imaging modalities, such as low-dose CT scans, PET-CT imaging, and even chest radiographs, accurately identifying suspicious nodules and facilitating timely intervention. AI models have exhibited promise in utilizing biomarkers and tumor markers as supplementary screening tools, effectively enhancing the specificity and accuracy of early detection. These models can accurately distinguish between benign and malignant lung nodules, assisting radiologists in making more accurate and informed diagnostic decisions. Additionally, AI algorithms hold the potential to integrate multiple imaging modalities and clinical data, providing a more comprehensive diagnostic assessment. By utilizing high-quality data, including patient demographics, clinical history, and genetic profiles, AI models can predict treatment responses and guide the selection of optimal therapies. Notably, these models have shown considerable success in predicting the likelihood of response and recurrence following targeted therapies and optimizing radiation therapy for lung cancer patients. Implementing these AI tools in clinical practice can aid in the early diagnosis and timely management of lung cancer and potentially improve outcomes, including the mortality and morbidity of the patients.
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Cancer Awareness Word Search – Free Printable
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O Amar Mota Bou Get File - Colaboratory
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Cancer
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Cancer Facts & Figures 2023
![Cancers, Free Full-Text](https://www.thelancet.com/cms/attachment/8cf7bcae-110d-453d-8cfb-e61eba935d6e/gr1.jpg)
Multi-omics integrated circulating cell-free DNA genomic signatures enhanced the diagnostic performance of early-stage lung cancer and postoperative minimal residual disease - eBioMedicine
![Cancers, Free Full-Text](https://www.thelancet.com/cms/attachment/2059ef4d-75ee-40cc-b912-9b0d536a1edc/gr1_lrg.jpg)
The global burden of cancer attributable to risk factors, 2010–19: a systematic analysis for the Global Burden of Disease Study 2019 - The Lancet
from Flow Cytometry to Cytomics, Page 2
Mother find out dying child is cancer free ❤️, cancer free
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Mortality after second malignancy in breast cancer survivors compared to a first primary cancer: a nationwide longitudinal cohort study
![Cancers, Free Full-Text](https://www.mdanderson.org/cancerwise/2021/05/remission--cancer-free--no-evidence-of-disease--what-is-the-difference-when-talking-about-cancer-treatment-effectiveness-and-results/jcr:content/blog/adaptiveimage.resize.702.404.jpg)
Remission, cancer-free, no evidence of disease: What's the difference?
What is cancer of unknown primary (CUP)?
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Risk of cancer in regular and low meat-eaters, fish-eaters, and vegetarians: a prospective analysis of UK Biobank participants, BMC Medicine
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