The Role of Artificial Intelligence in Detecting Pulmonary Cancer Early

Costello Velazquez^*
*Correspondence: Costello Velazquez, Department of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin 300052, China, Email:

Introduction

Pulmonary cancer, primarily referred to as lung cancer, remains one of the most lethal and prevalent forms of cancer worldwide. According to the World Health Organization (WHO), lung cancer accounts for nearly 18.4% of all cancer-related deaths globally, making early detection and treatment crucial in improving survival rates. However, early detection remains a significant challenge in pulmonary cancer diagnosis. Despite advances in medical imaging, such as chest X-rays, CT scans, and MRIs, many lung cancers are detected only in their later stages, when the prognosis is often poor. In recent years, artificial intelligence (AI) has shown immense promise in revolutionizing the early detection of various diseases, including cancer. AI systems have the potential to enhance medical imaging techniques, assist doctors in analyzing vast amounts of data, and improve diagnostic accuracy. Specifically, in the case of pulmonary cancer, AI tools can play a pivotal role in identifying subtle patterns and irregularities in imaging scans that might otherwise go unnoticed by the human eye [1].

Description

Pulmonary cancer, also known as lung cancer, typically refers to two major types, Non-Small Cell Lung Cancer (NSCLC) and Small Cell Lung Cancer (SCLC). NSCLC is the most common, accounting for approximately 85% of all cases. Both types of lung cancer develop primarily in the lungs but can spread (metastasize) to other organs over time, making early detection critical. Earlystage lung cancer often presents with few or no symptoms, and the initial signs may be easily overlooked. Lung cancer diagnosis traditionally involves imaging techniques, with the chest X-ray being one of the most common methods for detecting abnormalities in the lungs. However, chest X-rays have limitations in sensitivity and specificity, meaning they may miss small lesions or fail to differentiate between benign and malignant nodules. CT scans offer more detailed images, but the large volume of imaging data produced often makes manual interpretation by radiologists time-consuming and prone to error. Given these challenges, early-stage lung cancer is often diagnosed only after the tumor has grown large enough to cause noticeable symptoms or spread to other parts of the body. This delay in diagnosis can significantly reduce the chances of successful treatment, highlighting the importance of improving early detection methods [2].

Artificial intelligence, particularly deep learning, has the potential to revolutionize early cancer detection by assisting medical professionals in identifying patterns in imaging data. Deep learning, a subset of AI, uses neural networks with multiple layers to analyze and interpret complex data. These models can be trained on large datasets of medical images, allowing them to recognize patterns that may be too subtle for human radiologists to detect. Deep learning algorithms are particularly effective in analyzing medical images, including those obtained through CT scans, X-rays and MRI. By training on thousands of annotated images, AI models can learn to detect features of pulmonary cancer such as small nodules, unusual growth patterns, or irregularities in the lung tissue. One of the most significant advantages of AI in medical imaging is its ability to detect minute changes in images that might not be visible to the human eye. For example, AI systems can identify early-stage lung cancer tumors that are too small or too subtle for radiologists to spot. This ability to detect smaller lesions means that AI can potentially identify cancer at an earlier stage when treatment options are more effective and outcomes are better. AI-driven systems have been incorporated into several lung cancer screening programs and have shown promising results. In a study published by the American Journal of Respiratory and Critical Care Medicine, AI models trained on CT scans demonstrated an ability to accurately identify lung cancer nodules with high sensitivity, even in the early stages of development. In some cases, AI systems outperformed human radiologists in detecting malignancies [3].

AI's role in pulmonary cancer detection extends beyond mere image analysis. AI tools are also being used to assist radiologists in their decisionmaking process. Instead of replacing human professionals, AI complements their expertise by providing them with additional insights and recommendations. Radiologists are often faced with interpreting a large volume of images, and the pressure to detect and diagnose cancer accurately can be overwhelming. AI can streamline this process by automatically flagging suspicious areas in medical images, allowing radiologists to focus their attention on the most critical regions. In addition to increasing diagnostic efficiency, AI can also reduce the chances of human error, leading to more accurate diagnoses. AI systems are trained to recognize patterns in data that may be too complex for traditional image analysis techniques. For example, AI can integrate data from multiple imaging modalities (such as CT scans and PET scans) to provide a more comprehensive view of a patient's condition. This multi-modal approach allows AI systems to identify a broader range of abnormalities, including those that may have been missed using a single imaging modality.

In addition to helping radiologists interpret individual images, AI is also being integrated into large-scale lung cancer screening programs. The National Lung Screening Trial (NLST) in the United States, for example, showed that low-dose CT screening could reduce lung cancer mortality rates by 20% among high-risk individuals, such as smokers and former smokers. However, while the benefits of screening are clear, the volume of images generated from these screenings can be overwhelming, and false positives can lead to unnecessary follow-up tests and procedures. Recent developments in AI-based screening have demonstrated promising results. For instance, the use of AI models in conjunction with CT scans has been shown to increase the detection rate of small lung cancers while reducing the number of false positives. This combination of enhanced detection accuracy and reduced false positive rates is a significant step forward in lung cancer screening [4,5].

Conclusion

The role of artificial intelligence in detecting pulmonary cancer early holds immense promise. By harnessing the power of deep learning and advanced data analysis, AI systems can significantly improve the accuracy and efficiency of lung cancer detection, enabling earlier diagnoses and better outcomes for patients. AI models can assist radiologists in analyzing complex imaging data, improve screening programs, and provide personalized risk assessments for high-risk individuals. Despite the challenges, such as data quality and the need for transparent, interpretable models, AI is already demonstrating its potential in revolutionizing the way lung cancer is detected. As technology continues to advance and AI systems become more integrated into clinical workflows, we can expect significant improvements in the early detection of pulmonary cancer, ultimately saving lives and enhancing the quality of healthcare for patients worldwide.

Acknowledgment

None.

Conflict of Interest

None.

References

1. Wei, Shiyou, Chenglin Guo, Jintao He and Qunyou Tan, et al. "Effect of vein-first vs artery-first surgical technique on circulating tumor cells and survival in patients with non–small cell lung cancer: A randomized clinical trial and registry-based propensity score matching analysis."JAMA Surg 154 (2019): e190972-e190972.

   Google Scholar, Crossref, Indexed at

2. Szilágyi, Melinda, Ondrej Pös, Éva Márton and Gergely Buglyó, et al. "Circulating cell-free nucleic acids: Main characteristics and clinical application."Int J Mol Sci 21 (2020): 6827.

   Google Scholar, Crossref, Indexed at

3. Yamamoto, Shota, Ronald L. Korn, Rahmi Oklu and Christopher Migdal, et al. "ALK molecular phenotype in non-small cell lung cancer: CT radiogenomic characterization."Radiology272 (2014): 568-576.

   Google Scholar, Crossref, Indexed at

4. Aerts, Hugo JWL, Patrick Grossmann, Yongqiang Tan and Geoffrey R. Oxnard, et al. "Defining a radiomic response phenotype: A pilot study using targeted therapy in NSCLC."Sci Rep 6 (2016): 33860.

   Google Scholar, Crossref, Indexed at

5. Rizzo, Stefania, Francesco Petrella, Valentina Buscarino and Federica De Maria, et al. "CT radiogenomic characterization of EGFR, K-RAS and ALK mutations in non-small cell lung cancer."Eur Radiol 26 (2016): 32-42.

   Google Scholar, Crossref, Indexed at