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ZENODO
Article . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Article . 2025
License: CC BY
Data sources: Datacite
ZENODO
Article . 2025
License: CC BY
Data sources: Datacite
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Lung Cancer: An Overview: Epidemiology, Pathophysiology, Diagnosis & Treatment

Authors: Supriya Hingane*, Dr. Ashwini Shewale, Sidra Riyaz Shaikh, Sanjay Vonkade, Shruti Sapate;

Lung Cancer: An Overview: Epidemiology, Pathophysiology, Diagnosis & Treatment

Abstract

Lung cancer remains one of the most common and deadly malignancies worldwide, with its prognosis largely dependent on the stage at diagnosis and the tumour’s molecular profile. Despite advances in imaging, biopsy techniques, and therapeutic options, overall survival rates continue to be disappointing. Key risk factors include long-term tobacco smoking, environmental exposures (such as air pollution and radon), and genetic predispositions. From a classification standpoint, lung cancers are broadly divided into non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC), the former accounting for the majority of cases and being further sub-typed into adenocarcinoma, squamous cell carcinoma and large-cell carcinoma. Recent years have seen significant progress in molecular characterisation — for example, actionable mutations in EGFR, ALK, ROS1 and others in NSCLC — which has enabled targeted therapies and immunotherapies to become part of standard care. Early detection through screening (such as low-dose CT) and delivering personalised treatment based on tumour biology are key strategies to improve outcomes. Nevertheless, major challenges remain: many patients still present at an advanced stage, access to advanced diagnostics and therapies is uneven globally, and treatment resistance often develops. Looking ahead, innovations in biomarkers, artificial intelligence for imaging and risk-stratification, and global efforts to improve equitable care offer hope for better management of lung cancer.

Keywords

Genetics; Lung Cancer; Pathogenesis; Treatment

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    popularity
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    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green
Related to Research communities
Cancer Research