TTFields electrode optimization for a glioblastoma volume

This example lives at examples/medicine_examples/tumor_treatment/. It models the Tumor Treating Fields (TTFields) optimization problem on a glioblastoma volume: given a 3D grid of tumor voxels with directional cell-division axes, find the electrode angle that maximizes disruption.

The optimizer is Metropolis-style. Each iteration:

1. Proposes a new (theta, phi).
2. Evaluates the proposed configuration’s efficacy as a causal effect chain.
3. Accepts the proposal if the score improves, rejects otherwise.

The interesting move is step 2: the physics simulation is wrapped in PropagatingEffect::pure(new_params).bind(...) so the optimizer composes against the same effect-propagation machinery the rest of the library uses.

The chain

use deep_causality_core::{CausalityError, EffectValue, PropagatingEffect};

let effect = PropagatingEffect::pure(new_params).bind(|params, _, _| {
    let p = match params {
        EffectValue::Value(v) => v,
        _ => (0.0, 0.0),
    };
    let score = match model::evaluate_efficacy(&tumor, p) {
        Ok(s) => s,
        Err(e) => return PropagatingEffect::from_error(/* ... */),
    };
    PropagatingEffect::pure(score)
});

if let EffectValue::Value(new_score) = effect.value() {
    if *new_score > current_score {
        current_params = new_params;
        current_score = *new_score;
    }
}

The bind is a pure function: it takes the proposed (theta, phi), runs the physics simulation, and returns a new PropagatingEffect carrying the score. The optimizer reads the score off the effect’s value.

What to look at in the source

• model.rs: evaluate_efficacy, build_mock_tumor, the physics that turns an electrode angle into a disruption score.
• main.rs: the optimization loop, the Metropolis accept/reject, and the temperature schedule.

The crate ships sibling examples for protein folding, tissue classification, aneurysm hemodynamics, decompression, and epilepsy. They live next to this one under examples/medicine_examples/.

Run it

git clone https://github.com/deepcausality-rs/deep_causality
cd deep_causality
cargo run --release -p medicine_examples --example tumor_treatment

The output prints one line per optimization step with the current score and the proposed angle.

Why this is a good fit

The optimization loop is glue; the physics is heavy. Wrapping the physics in a PropagatingEffect keeps the loop indifferent to the implementation. Swap evaluate_efficacy for a different model, the loop does not change. Add an audit log of every proposal, the EffectLog is already collecting it.