New Perspectives on Lung Cancer Screening and Artificial Intelligence
Lung cancer is the leading cause of cancer-related death worldwide, with 1.8 million deaths annually. Early detection is vital for improving patient outcomes; however, survival rates remain low due to late-stage diagnoses. Accumulating data supports the idea that screening methods are useful for imp...
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MDPI AG
2025-03-01
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| author | Leonardo Duranti Luca Tavecchio Luigi Rolli Piergiorgio Solli |
| author_facet | Leonardo Duranti Luca Tavecchio Luigi Rolli Piergiorgio Solli |
| author_sort | Leonardo Duranti |
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| description | Lung cancer is the leading cause of cancer-related death worldwide, with 1.8 million deaths annually. Early detection is vital for improving patient outcomes; however, survival rates remain low due to late-stage diagnoses. Accumulating data supports the idea that screening methods are useful for improving early diagnosis in high-risk patients. However, several barriers limit the application of lung cancer screening in real-world settings. The widespread diffusion of artificial intelligence (AI), radiomics, and machine learning has dramatically changed the current diagnostic landscape. This review explores the potential of AI and biomarker-driven methods, particularly liquid biopsy, in enhancing early lung cancer detection. We report the findings of major randomized controlled trials, cohort studies, and research on AI algorithms that use multi-modal imaging (e.g., CT and PET scans) and liquid biopsy to identify early molecular alterations. AI algorithms enhance diagnostic accuracy by automating image analysis and reducing inter-reader variability. Biomarker-driven methods identify molecular alterations in patients before imaging signs of cancer are evident. Both AI and liquid biopsy show the potential to improve sensitivity and specificity, enabling the detection of early-stage cancers that traditional methods, like low-dose CT (LDCT) scans, might miss. Integrating AI and biomarker-driven methods offers significant promise for transforming lung cancer screening. These technologies could enable earlier, more accurate detection, ultimately improving survival outcomes. AI-driven lung cancer screening can achieve over 90% sensitivity, compared to 70–80% with traditional methods, and can reduce false positives by up to 30%. AI also boosts specificity to 85–90%, with faster processing times (a few minutes vs. 30–60 min for radiologists). However, challenges remain in standardizing these approaches and integrating them into clinical practice. Ongoing research is essential to fully realize their clinical benefits and enhance timely interventions. |
| format | Article |
| id | doaj-art-e0300d120616466192a0c26da0d465b6 |
| institution | Kabale University |
| issn | 2075-1729 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Life |
| spelling | doaj-art-e0300d120616466192a0c26da0d465b62025-08-20T03:43:10ZengMDPI AGLife2075-17292025-03-0115349810.3390/life15030498New Perspectives on Lung Cancer Screening and Artificial IntelligenceLeonardo Duranti0Luca Tavecchio1Luigi Rolli2Piergiorgio Solli3Thoracic Surgery Unit, Fondazione IRCCS Istituto Nazionale Tumori, 20131 Milan, ItalyThoracic Surgery Unit, Fondazione IRCCS Istituto Nazionale Tumori, 20131 Milan, ItalyThoracic Surgery Unit, Fondazione IRCCS Istituto Nazionale Tumori, 20131 Milan, ItalyThoracic Surgery Unit, Fondazione IRCCS Istituto Nazionale Tumori, 20131 Milan, ItalyLung cancer is the leading cause of cancer-related death worldwide, with 1.8 million deaths annually. Early detection is vital for improving patient outcomes; however, survival rates remain low due to late-stage diagnoses. Accumulating data supports the idea that screening methods are useful for improving early diagnosis in high-risk patients. However, several barriers limit the application of lung cancer screening in real-world settings. The widespread diffusion of artificial intelligence (AI), radiomics, and machine learning has dramatically changed the current diagnostic landscape. This review explores the potential of AI and biomarker-driven methods, particularly liquid biopsy, in enhancing early lung cancer detection. We report the findings of major randomized controlled trials, cohort studies, and research on AI algorithms that use multi-modal imaging (e.g., CT and PET scans) and liquid biopsy to identify early molecular alterations. AI algorithms enhance diagnostic accuracy by automating image analysis and reducing inter-reader variability. Biomarker-driven methods identify molecular alterations in patients before imaging signs of cancer are evident. Both AI and liquid biopsy show the potential to improve sensitivity and specificity, enabling the detection of early-stage cancers that traditional methods, like low-dose CT (LDCT) scans, might miss. Integrating AI and biomarker-driven methods offers significant promise for transforming lung cancer screening. These technologies could enable earlier, more accurate detection, ultimately improving survival outcomes. AI-driven lung cancer screening can achieve over 90% sensitivity, compared to 70–80% with traditional methods, and can reduce false positives by up to 30%. AI also boosts specificity to 85–90%, with faster processing times (a few minutes vs. 30–60 min for radiologists). However, challenges remain in standardizing these approaches and integrating them into clinical practice. Ongoing research is essential to fully realize their clinical benefits and enhance timely interventions.https://www.mdpi.com/2075-1729/15/3/498AI in lung cancer screeningartificial intelligence in CT imagingbiomarker-driven screening for lung cancerliquid biopsy in lung cancer detectionartificial intelligence in PET imagingscreening |
| spellingShingle | Leonardo Duranti Luca Tavecchio Luigi Rolli Piergiorgio Solli New Perspectives on Lung Cancer Screening and Artificial Intelligence Life AI in lung cancer screening artificial intelligence in CT imaging biomarker-driven screening for lung cancer liquid biopsy in lung cancer detection artificial intelligence in PET imaging screening |
| title | New Perspectives on Lung Cancer Screening and Artificial Intelligence |
| title_full | New Perspectives on Lung Cancer Screening and Artificial Intelligence |
| title_fullStr | New Perspectives on Lung Cancer Screening and Artificial Intelligence |
| title_full_unstemmed | New Perspectives on Lung Cancer Screening and Artificial Intelligence |
| title_short | New Perspectives on Lung Cancer Screening and Artificial Intelligence |
| title_sort | new perspectives on lung cancer screening and artificial intelligence |
| topic | AI in lung cancer screening artificial intelligence in CT imaging biomarker-driven screening for lung cancer liquid biopsy in lung cancer detection artificial intelligence in PET imaging screening |
| url | https://www.mdpi.com/2075-1729/15/3/498 |
| work_keys_str_mv | AT leonardoduranti newperspectivesonlungcancerscreeningandartificialintelligence AT lucatavecchio newperspectivesonlungcancerscreeningandartificialintelligence AT luigirolli newperspectivesonlungcancerscreeningandartificialintelligence AT piergiorgiosolli newperspectivesonlungcancerscreeningandartificialintelligence |