If the unstated goal of these rotation post-mortems was to summarize what I had learned, a single post may not be enough for breast cancer. Six weeks ago, I knew that it was common, maybe overdiagnosed, possibly overtreated, and beating all other cancers for research funding by a vast margin. All this was a vague sense of being informed—like a NYT reader may feel after reading the Sunday Magazine feature—rather than actual knowledge.
Having talked to a good number of women with breast cancer, and worked with a few attendings dedicated to the field, I know it enough to know that I need to know more; but also enough to keep me interested. What from the outside looks like cookbook this-marker-means-she's-getting-that-treatment medicine is in fact an intricate work of knowing your patient, figuring out where she stands in the heaps of data generated by decades-long studies following thousands of women on different protocols, discussing the options, and coming to a mutualy agreed decision1. Hard work, all of it.
Harder still is working on those data-generating trials. Anyone can think of a clinicaly relevant question, but can they make it into a feasable protocol? Can they gather a team to manage all the patients in the center, and all the different centers? Can they manage that team? Looking at a recent set of trials you will hear more about soon, the scale boggles the mind.
Side note: "We don't have a crystal ball" is common oncspeak for "I don't know what your prognosis is"2, but if a person has breast cancer what are the Gail model or Oncotype DX if not (developing, imperfect) tellers of fortune? And wouldn't it be great to have a similar set of tools and statistics for all cancers?
So, not going into the field, but thoroughly impressed.