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.

  1. Which is—truth be told—what we should do for any cancer, or illness. Alas, most diseases lack data, options, or both. 

  2. Or in the paternalistic dialect of the language, “I don’t want to tell you that you prognosis is poor”.