Week Nine!

Hi all!

In my ninth week of my internship I decided that I would be extending my time at Mayo Clinic and doing research there outside the scope of SRP! My research questions delves into which scans are the most important in diagnosing prostate cancer and the research I will be doing outside of SRP will further explore the potential of these scans. My mentors and I will determine whether whether a shortened protocol with only these important scans is just as effective as the full length 45 minute protocol that is currently in use. Since the research I will be doing there still relates to my project, I will use any findings to support my SRP presentation.

So in the past week I did more personal research than I spent time at the clinic. My mentors assigned me to look at past articles on the effectiveness of a shortened protocol since we don’t want to be conducting research that’s already been done. As I’ve mentioned in my last blog post, the three most important scans are the T2, diffusion, and perfusion scans. Some of the articles I found compared the T2 and the diffusion with the T2 with the combined diffusion and T2 being more effective.

A phrase I saw come up often in all the reports I read was ROC curve and initially, naive me thought it was a radiology term. Coincidentally when I went to meet with Dr. Panda he gave me an article to read about Receiver Operating Characteristic (ROC) curves and how they relate to medicine. ROC curves basically weigh the pros and cons of a test such as its accuracy, amount of type 1 and 2 errors, etc. and essentially determines how good of a test it actually is. For example, breast cancer occurs around 3 out of 1000 people. A breast imager looking at 1000 cases could call all of them negative without ever really seeing them and have an accuracy of 99.7%. However this isn’t a reliable way to find the quality of a test. An ROC curve uses the number of true positives and true negatives, as well as false positives and false negatives and other variables to determine the proficiency of a test. Therefore, the larger the area under the ROC curve is, the “better” a test is.

Thank you all for reading and I hope to see you next week for my final blog post!