Recent advances in the diagnosis and treatment of lung cancer

Recent advances in the diagnosis and treatment of lung cancer

Recent advances in lung cancer diagnosis and treatment have significantly improved patient outcomes and survival rates. Artificial intelligence (AI) and deep learning techniques have shown promising results in pulmonary nodule detection, segmentation, and classification, potentially enhancing early diagnosis of lung cancer [1]. Low-dose computed tomography (CT) screening has demonstrated mortality benefits and is being implemented in clinical settings for high-risk individuals [2].

In treatment, targeted therapies based on specific genomic abnormalities have revolutionized lung cancer care, particularly for non-small cell lung cancer (NSCLC) (Garg et al., 2024; Nishino et al., 2014). Immunotherapies have also shown significant improvements in patient outcomes, especially for advanced NSCLC [3]. For extensive-stage small-cell lung cancer (ES-SCLC), immune checkpoint inhibitors combined with platinum-based chemotherapy have established a new standard for first-line treatment [4].

Interestingly, while these advancements are promising, challenges remain in accessing advanced treatments and managing their associated costs [3]. Additionally, despite improvements in targeted treatments, early detection remains crucial for optimal outcomes, as diagnosis at the earliest stage is strongly associated with improved survival [5].

In conclusion, the field of lung cancer diagnosis and treatment is rapidly evolving. Emerging technologies such as circulating biomarkers (CTCs, ctDNA, microRNA) show potential for non-invasive monitoring of treatment response and disease progression [6]. Ongoing research into new therapies like CAR-T cell therapy and oncolytic viruses, along with efforts to identify molecular subtypes of lung cancer, may further revolutionize treatment approaches and pave the way for more personalized and effective care (Araghi et al., 2023; Garg et al., 2024; Yu et al., 2022).

References

1. Tandon YK, Bartholmai BJ, Koo CW. Putting artificial intelligence (AI) on the spot: machine learning evaluation of pulmonary nodules. Journal of thoracic disease. Pioneer Bioscience Publishing Company (Pbpc); 2020;12:6954–65.

2. New M, Keith R. Early Detection and Chemoprevention of Lung Cancer. F1000Research. Taylor & Francis; 2018;7:61.

3. Garg P, Singhal S, Kulkarni P, Horne D, Malhotra J, Salgia R, et al. Advances in Non-Small Cell Lung Cancer: Current Insights and Future Directions. Journal of clinical medicine. Mdpi; 2024;13:4189.

4. Yu Y, Fan Y, Chen K. Extensive-stage small-cell lung cancer: Current management and future directions. International Journal of Cancer. Wiley-Blackwell; 2022;152:2243–56.

5. Bradley SH, Neal RD, Kennedy MPT. Recognising Lung Cancer in Primary Care. Advances in Therapy. Springer Nature; 2018;36:19–30.

6. Xu-Welliver M, Carbone DP. Blood-based biomarkers in lung cancer: prognosis and treatment decisions. Translational lung cancer research. Ame Publishing Company; 2017;6:708–12.