Lung Cancer Diagnosis, Treatment, and Prognosis Using Machine Learning
DOI:
https://doi.org/10.62760/iteecs.4.2.2025.133Keywords:
imaging and sequencing technologies, intricate datasets, immunotherapy, prediction, detectionAbstract
Recent advances in imaging and sequencing technology have enabled a systematic advancement in the medical treatment of carcinoma of the lungs. Meanwhile, the human mind's ability to comprehend and make optimal use of the collection for this enormous amounts of knowledge is limited.. The integration and analysis of these vast and intricate datasets, which have thoroughly described lung cancer by utilizing various viewpoints from the accumulated data, are made possible in great part by machine learning-based methodologies. We give a summary of machine learning-based methods in this review that support the various facets of lung cancer diagnosis and treatment, such as immunotherapy practice, prognosis prediction, auxiliary diagnosis, and early detection. We also highlight the challenges and opportunities for additional artificial intelligence applications in lung disease.
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