Biomarkers and their Role in Screening for Lung Cancer

Two of the principal components of treating lung cancer are screening and detection.  In this blog post, I will be discussing how AI can be used to identify tumor progression, and the role that biomarkers play in lung cancer diagnosis.


Histopathological images can provide scientists with information about the environment surrounding a tumor, also known as the microenvironment, which can have an impact on the tumor itself. The microenvironment of lung cancer consists of the tumor cells, lymphocytes, stromal cells, macrophages, and blood vessels. These components are all important factors for tumor progression and have differing effects on the outcome of the lung cancer patient. Researchers of one study created a type of deep learning model, a convolutional neural network, that had the ability to identify the differences between these components of the microenvironment of lung cancer using pathological images. This study was completed by removing small images of the cell nuclei which were then classified by the CNN model and another model was developed that could validate the data sets using the detected cells in the training set (Wang et al., 2019). Such screening and detection cannot be completed however without the presence of biomarkers that help scientists identify molecular changes in the body that result from the formation of a tumor.


Strong biomarkers are able to clarify criteria for lung cancer screening to limit costs, provide analysis to help doctors make decisions on how to manage cancerous cells, and follow up on treated patients who were previously screened for lung cancer or those with a high chance of recurrence. It is also important for biomarkers not to be influenced by changes in temperature and pH, along with enzyme and oxygen levels (Seijo et al., 2019). If a biomarker surpasses the standard of care, is cost effective, and has high performance accuracy, then it is most likely considered successful.


Scientists have established a few promising biomarkers, such as urine and saliva, that are currently used. Blood is another biomarker that can be used for lung cancer screening as it can help to identify and study the tumor and the space surrounding it, any metastases, and the patient's immune response. Sputum, which comes from the airway epithelium can be used for lung cancer diagnosis as well and can supply data about any changes in molecular structure that are close to the tumor cells. Autoantibodies are another form of biomarker which develop as a result of the formation of a tumor, before any symptoms appear on images. These autoantibodies have been discovered in all types of lung cancer, meaning in the future they could be indicators of lung cancer. Further studies are examining the rise of newer biomarkers that can slo be used alongside artificial intelligence to decrease lung cancer patient mortality rates. The unstable  fragments of exhaled breath, which contains cells and DNA fragments, can actually act as biomarkers and be used for cancer detection. A specific nano-array sensor which runs off of artificial intelligence, and has the potential to distinguish benign tumors from malignant ones, was used in a study to diagnose 17 different diseases from exhaled breath samples and there was an accuracy rate of over 85% (Seijo et al., 2019).


Like any new field of research, there are challenges with studying biomarkers and their relation to artificial intelligence. Samples of data need to be collected precisely and on a large scale from medical records, and need to be treated with great amounts of care. These records also need to be kept anonymous a majority of the time so those transferring data need to remain cautious about genetic fingerprints that can reveal a patient's identity (Seijo et al., 2019). Aside from screening and detection, AI can be used to predict the chance of survival in survival cancer patients similar to the way it can make predictions for brain cancer patients. This predictive side of AI will be covered in my upcoming blog post.



References

Seijo, L., Peled, N., Ajona, D., Boeri, M., Field, J., Sozzi, G., . . . Montuenga, L. (2019, March). Biomarkers in Lung Cancer Screening: Achievements, Promises, and Challenges. Retrieved September 15, 2020, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6494979/

Wang, S., Yang, D., Rong, R., Zhan, X., Fujimoto, J., Liu, H., . . . Xiao, G. (2019, October 28). Artificial Intelligence in Lung Cancer Pathology Image Analysis. Retrieved September 15, 2020, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6895901/

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