Cancer Disscussion Practice

Discussion 3 Guide: Cancer NAME: Wesley Beals Directions:  Read the articles BEFORE answering the questions.  This assignment should be a “deep dive” into the material. It will take time to do. Part 1: Article Reading and Analysis 1. Ferlay et al. 2021: a. Read the methods section. How could the method for collecting cancer data affect the results of the study? Explain your reasoning. If the point of the study was to try to figure out how cancer is affecting different areas of the world, then that would require a petty extensive data collection and analysis. If the data was not sampled from a worldwide database about cancer and deaths, then that limits the number of extensive conclusions about cancer trends across the world. It would only be a trend from the regions you look at. Also, since the world has 8 billion people, your sample size for the study would require one that matches that. It also requires significant statical analysis so that the sample size is big enough to be representative of the entire population. b. Which type of cancer represented the most new cases for men? For women? The type of cancer that represented the most new cases for men was lung cancer followed by prostate cancer, NMSC, lip and oral cancer, and lastly liver cancer. For females, breast cancer represented the most common type of diagnosed cancer followed by lung, cervical, and NMSC cancer. c. Which types of cancer represented the most deaths for men? For women? For deaths in men, lung cancer was the greatest, and for women, breast cancer was the main cause of the cancer deaths. d. What cancers represent the greatest mortality for men and women across different regions of the world? What factors do you think contribute to this? The cancer that represents the most deaths across the regions is lung cancer among men and breast cancer among women. I think lung cancer makes sense that it can cause the most deaths among men because men are more likely to have a smoking habit compared to women and smoking is the leading cause of lung cancer. I would also think that men are more likely across the world than women to work outside and air pollution can also lead to lung cancer. I would also assume that it is difficult to diagnose the cancer until it is in its later stages. Breast cancer for women makes sense considering that there is such a thing as Breast Cancer Awareness Month. Breast cancer, from what I understand, is hard to treat because each patient is different from the rest. In addition, breast cancer I’ve heard is an aggressive cancer and if not caught early it can be deadly. e. Figure 2 shows the estimated number of new cases and deaths. What factors contribute to Eastern Asia having some many cases? Explain your reasoning. In Eastern Asia, which includes countries such as China, Mongolia, Korea, and Japan, there are a couple of possible factors that could lead to the most cancer in the world. One such factor is the fact that this region of the world has about 1/8 of the population of the world and up until recently had the largest population in the world. When you have more people in an area, there is a higher chance that someone in that population will have cancer and it also represents a larger population in the world compared to other areas. In addition, you have places like China and North Korea that struggle with pollution and governments that don’t really care about their people then that’s how you can end up in this region having more cancer incidences and deaths. f. Pick another region and list factors that could affect the percent of new cases and deaths. Explain your reasoning. I thought about the factors that could lead to increased cancer rates and deaths in North America. The most common cancer death in North America is lung cancer. A factor that could lead to this is the fact that there is a high percentage of older Americans who smoke which is one of the leading causes of lung cancer. Some other auxiliary factors could be pollution. As for things like incidents of breast cancer and NMSC, it could be that we are exposed to a lot of processed food and products that could also lead to increased cancer. Also, North America is a genetic melting pot in which different genetic profiles come together that really had not in the past so those mixed genetics could lead to increased susceptibility for those cancers. 2. National Cancer Institute: a. How is AI being used to detect cancer? AI is starting to be used to recognize tumors with different body scans and help doctors sift through all the data that comes with medicine and body scans. It is also being considered to help train new radiologists so that they can gain skills that are necessary for accurate diagnosis. b. What issues may arise from using AI to detect cancer? There are actually a few issues that are brought up by the article about using AI. One of the main things is that the AI program is still in testing and as a result, there are still a couple of kinks in the testing. Another challenge is that AI programs are not good with information or images they were not trained with. Scientists are also struggling to figure out how the AI comes to certain conclusions and even in one round of testing, the AI only marked images as cancer from a certain hospital. Researchers wonder how the AI would handle different machines and the quality of images. One last challenge is the fact that this might contribute to healthcare inequality as AI is primarily being train on white patients and not very many minorities' patients c. What benefits might there be from using AI to detect cancer? The benefits of using AI to help detect cancer are numerous. One of the benefits is that it can be used to detect things faster and more accurately than a human can and go through an overwhelming amount of information and images that a human cannot. They can also be used to train new radiologists so they can gain experience and even hospital staff who do not have a specialist to identify cancer. AI can notice details that humans cannot notice and eventually do it for a multitude of different scans and types of images. 3. Zang et al. 2023: a. What are the differences between artificial intelligence, machine learning, and deep learning? AI is a general field of study while the other two seem to be specializations of AI. AI uses algorithms and programs to try and analyze data and make predictions. Machine learning, which is a specialization of AI, focuses around using algorithms to learn and improve performance without being expressly coded to do a task. Deep learning, is an even deeper specialization of machine learning and AI, focuses on being and developing a neural network that works very similarly to the human brain and analyzes large and complex data such as language, image recognition, and others. b. What ways are AI and machine learning being used in the medical field? In addition to helping in the fight against cancer, AI is currently being used in detecting patients at risk of certain diseases, used in imaging other things that rely on images, and it is even being used in disease detection using very minimal information and leading to quicker diagnosis. AI is helping in the diagnosis and treatment process in many different way that help lead to better and more personalized patient care so that paitents can do better faster. c. Are there differences in which types of cancers can be detected with AI? Explain your reasoning. There does not seem to be much difference between the types of cancer that can be detected. A lot of the ways that AI is labeling things as cancerous stems from an increased emphasis on focusing on gene expression and mutation which leads to just needing to analyze the cells to notice mutations in the genome and the cascade effect from the mutation that leads to cancer. A difference is that for some cancers that rely on images or slides can lead to an entirely different process to reach the conclusion that there is cancer. d. What are some concerns with using AI to detect cancer? Currently concerns are about the data that is used to train the AI programs and the actual process the AI uses to try and reach its conclusions. The AI programs need lots of data to learn and become more accurate with their outcomes. Getting that data requires actual patient data which is protected and not given out willingly. This led to data being incomplete and fragmented which leads to an underdeveloped AI. In addition, not all clinical institutions use the same systems to store their data leading to some data being uncompilable in a neat and orderly fashion. Even once that data is received by the AI there is the concern of storage and the security of that storage. Also, that data from all the different intuitions needs to be normalized and standardized for the AI to be able to do its work. Lastly there is a lack of knowledge about how the AI reaches certain conclusions which adds to the problem of trying to improve it. Each of these challenges are being studied to help better the AI programs and increase advancements in medical care.