Advantages in Lung Cancer Diagnosis: a Recent Review
DOI:
https://doi.org/10.71321/cvmbxc42Keywords:
Lung cancer diagnosis, Imaging technologies, Biopsy, AI in radiology, biomarker discoveryAbstract
Lung cancer remains a leading cause of cancer-related mortality worldwide, underscoring the critical need for advancements in early and accurate diagnostic strategies. This review synthesizes recent progress in lung cancer diagnostics, focusing on innovations in imaging technologies, biomarker discovery, liquid biopsy, and artificial intelligence (AI)-driven analytical tools. Low-dose computed tomography (LDCT) has solidified its role in screening high-risk populations, reducing mortality through early detection, yet challenges persist regarding false positives and overdiagnosis. Emerging imaging modalities, including spectral CT and PET-MRI hybrid systems, demonstrate enhanced specificity in characterizing pulmonary nodules. AI and machine learning algorithms are increasingly deployed to refine radiological interpretation, predict malignancy risk, and correlate imaging features with molecular profiles. Despite these breakthroughs, challenges such as standardization, cost, and integration into clinical workflows remain. Future research would emphasize the synergy of multimodal diagnostics, leveraging AI to harmonize imaging, liquid biopsy, and clinical data for precision medicine.
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