Thank you, mishka, for your thoughtful response. You’ve given me a lot to chew on, particularly regarding the potential of focusing on chemotherapy treatment timing. While I’ve explored AI-driven health research, I hadn’t fully appreciated how important treatment timing, diet, exercise, and other factors may be for people in my situation.
There’s a mountain of data in this area, and using AI to predict salient data could potentially lead to improvements in how we approach chemotherapy. This seems like a practical and timely research direction, assuming it is still somewhat niche.
the potential of focusing on chemotherapy treatment timing
More concretely (this is someone’s else old idea), what I think is still not done is the following. Chemo kills dividing cells, this is why the rapidly renewing tissues and cell populations are particularly vulnerable.
If one wants to spare one of those cell types (say, a particular population of immune cells), one should take the typical period of its renewal, and use that as a period of chemo sessions (time between chemo sessions, a “resonance” of sorts between that and the period of the cell population renewal for the selected cell type). Then one should expect to spare most of that population (and might potentially be able to use higher doses for better effect, if the spared population is the most critical one; this does need some precision, not a typical today’s “relaxed logistics” approach where a few days this or that way in the schedule is nothing to worry about).
I don’t know if that ever progressed beyond the initial idea...
(That’s just one example, of course, there is a lot of things which can be considered and, perhaps, tried.)
Thank you, mishka, for your thoughtful response. You’ve given me a lot to chew on, particularly regarding the potential of focusing on chemotherapy treatment timing. While I’ve explored AI-driven health research, I hadn’t fully appreciated how important treatment timing, diet, exercise, and other factors may be for people in my situation.
There’s a mountain of data in this area, and using AI to predict salient data could potentially lead to improvements in how we approach chemotherapy. This seems like a practical and timely research direction, assuming it is still somewhat niche.
I appreciate your input.
More concretely (this is someone’s else old idea), what I think is still not done is the following. Chemo kills dividing cells, this is why the rapidly renewing tissues and cell populations are particularly vulnerable.
If one wants to spare one of those cell types (say, a particular population of immune cells), one should take the typical period of its renewal, and use that as a period of chemo sessions (time between chemo sessions, a “resonance” of sorts between that and the period of the cell population renewal for the selected cell type). Then one should expect to spare most of that population (and might potentially be able to use higher doses for better effect, if the spared population is the most critical one; this does need some precision, not a typical today’s “relaxed logistics” approach where a few days this or that way in the schedule is nothing to worry about).
I don’t know if that ever progressed beyond the initial idea...
(That’s just one example, of course, there is a lot of things which can be considered and, perhaps, tried.)