Lung cancer
6 articles · search the full text for this term
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Non-Small Cell Lung Cancer: Types, Pathogenesis, Diagnosis, and Novel Therapeutic Strategies
Abstract: Non-small cell lung cancer (NSCLC) is the most prevalent type of lung cancer, accounting for over 85% of all cases globally. It remains one of the primary causes of cancer-related death due to its rapid progression, few early symptoms, and late detection. The three main forms of non-small cell lung cancer (NSCLC) are adenocarcinoma, squamous cell carcinoma, and giant cell carcinoma; each has a unique histology, prognosis, and response to …
Published in Research and Reviews: A Journal of Pharmacology · Vol. 16, Issue 2, 2026 Read article
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Modern Approaches in Lung Cancer Management from Herbal Nanomedicine to Artificial Intelligence
Abstract: Lung cancer continues to be a major global health concern and one of the leading causes of cancer-related mortality worldwide. Despite significant advancements in therapy, there is still a pressing need for safer and more effective therapeutic alternatives; problems such drug resistance, side effects, metastasis, and recurrence continue to impact patient outcomes and quality of life. Examining current advancements in the use of herbal drug-loaded nanoparticles as a novel approach …
Published in Research and Reviews: A Journal of Pharmacology · Vol. 16, Issue 2, 2026 Read article
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A Comprehensive Review of Ethnobotanical Studies on Traditional Indian Herbal Plants for Lung Cancer Management
Abstract: Lung cancer is a leading cause of cancer-related deaths in the world. Current therapies for lung cancer are often limited by many side effects. This review compiles and evaluates ethnobotanical evidence and modern scientific findings of Indian herbal plants traditionally used for respiratory and tumor-related disorders, with a focus on their relevance to lung cancer management. Herbal phytochemicals and how they show their potent activities were also listed in this …
Published in Research & Reviews : Journal of Herbal Science · Vol. 15, Issue 1, 2026 · pp. 1–7 Read article
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A Review on Lung Cancer Prediction Using Machine Learning
Abstract: Lung cancer continues to be a major contributor to cancer-related mortality across the globe. Timely diagnosis and reliable prediction models play a crucial role in enhancing treatment outcomes and survival rates for patients. The present study focuses on the utilization of machine learning (ML) methods for the prediction of lung cancer. Using datasets that incorporate clinical records, imaging modalities, and genetic profiles, the research assesses the predictive capabilities of multiple …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 14, Issue 3, 2025 · pp. 1–11 Read article
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Air Pollution and Lung Cancer: A Deep Dive into the Impact in Southern India
Abstract: Lung cancer incidence is rising in Southern India, with air pollution, particularly PM2.5, emerging as a significant risk factor alongside smoking. A retrospective cohort study analyzed data from 1,500 lung cancer patients diagnosed between 2010–2020 across Chennai, Bangalore, and Hyderabad, using hospital records, cancer registries, and environmental monitoring stations. The study found that 73.3% of patients were male, 80.7% were smokers, and 42.7% were aged 60+. Chennai had the highest …
Published in International Journal of Toxins and Toxics · Vol. 2, Issue 2, 2025 · pp. 24–30 Read article
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Lung Cancer Detection and Classification Using Deep Learning
Abstract: Lung cancer is a disease that can be effectively treated if detected early. Various technologies, such as magnetic resonance imaging, isotopes, X-rays, and computed tomography scans, are employed for diagnosis. One of the most crucial strategies in combating cancer is early detection, which greatly enhances a patient’s likelihood of survival; this is where artificial intelligence plays a significant role. The approach proposed in this study leverages historical medical data to …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 13, Issue 3, 2024 · pp. 11–17 Read article