Binomial distribution - killing cells with x-rays

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Discussion Overview

The discussion revolves around the application of the binomial distribution to model the effects of x-ray radiation on tumor cells, specifically focusing on the probability of cell damage and death based on photon interactions. The scope includes mathematical modeling, theoretical considerations, and potential alternatives to the proposed model.

Discussion Character

  • Exploratory
  • Technical explanation
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • Wendy proposes that the chance of a cell being damaged by radiation can be modeled using a binomial distribution, suggesting that if 1000 cells are exposed to k photons, the distribution of hits could reflect cell damage.
  • Wendy's model assumes that a cell dies if it is hit 2 or more times and seeks to determine the number of photons required to achieve a specific distribution of hits among the cells.
  • One participant agrees that the approach is a reasonable approximation but questions the use of the term "binomial distribution," suggesting a "millenomial distribution" instead, due to the complexity of the model.
  • Another participant reflects on the dice analogy, considering the implications of modeling the system as a discrete array of random numbers and suggests exploring more complex models involving graphs or random fields.
  • A separate query is raised about the potential use of quantum wavefunctions in this context, indicating a shift towards more advanced theoretical considerations.

Areas of Agreement / Disagreement

Participants express differing views on the appropriateness of the binomial distribution for this scenario, with some supporting the idea while others propose alternative models or question the terminology. The discussion remains unresolved regarding the best modeling approach.

Contextual Notes

The discussion highlights limitations in the proposed model, including assumptions about photon absorption and the complexity of cellular geometry, which are not fully addressed. There is also uncertainty regarding the mathematical framework and terminology used in the modeling process.

Does the question make sense, and are there any other medical physicists out there?

  • It makes sense

    Votes: 2 100.0%
  • I don't understand the problem

    Votes: 0 0.0%
  • Yes, I'm a medical Physicist

    Votes: 0 0.0%
  • no, I'm not

    Votes: 1 50.0%

  • Total voters
    2
  • Poll closed .
wendy-medicalphysics
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Dear Fellow mathematicians and Physicists,I am doing some MC modelling on tumour growth and radiotherapy treatment modelling and would like to know:

Who out there would agree (or suggest alternatives) to the theroy that the chance of a cell being damaged/hit with radiation (and therefore perhaps dying depending on other parameters) may be described by the bionomial distribution?

Background:
1. Let's say that we have 1000 cells, and k photons will be fired at them
2. Let's also say that a cell will dye if hit 2 or more times (simplistic for now!)
3. I need the number of cells that are hit only 0 or 1 times to be 46% of the total

Can I use the bionomial distribution to work out how may photons that would take (integrating to find the area under the curve to obtain the number of phtotons necessary to achieve point 2.?)

I believe we can think about it as a dice with 1000 number sides.
If we roll the dice k times and take the histogram of the number of times each side came up, then the system is the same as the cell/photon set up...WHAT DO YOU THINK?

Thanks, and write back if you don't understand what I am trying to say

Wendy:rolleyes:
 
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Assuming that all photons, with certainty, are absorbed by one of the cells, the above approach seems to me like a reasonable approximation, given the difficulty of incorporating knowledge of the cellular geometry and the radiation source into a model.

I wouldn't call the result a binomial distribution though - perhaps a millenomial distribution ?
 
wendy-medicalphysics said:
I believe we can think about it as a dice with 1000 number sides.
If we roll the dice k times and take the histogram of the number of times each side came up, then the system is the same as the cell/photon set up...WHAT DO YOU THINK?

Thanks, and write back if you don't understand what I am trying to say

Wendy:rolleyes:

That would be interesting. And you look at P(X1>=2, X2>=2, X3...)? A very simple and elegant model but I have a feeling it's been superceded. I would look at graphs, random fields, anything with that sort of mapped network type of thingie. Haven't really looked at that kind of stuff in a while so I probably can't help you yet (and I have this ***** of an essay to write.) I suspect what you're looking for is a discrete array of continuous arrays of random numbers. Thus you could model with continuity the cell surface and then model discretely n numbers of cells.
 
Last edited:
Are we supposed to use a quantum wavefunction for this?
 

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