Translating New RBE Modeling to the Clinic
A Concept...
Note: Today is a complex topic really focused within the singular realm of proton therapy. My goal is to present this as simply as I can to reach a broader audience, yet still make the scientific concept.
Premise:
We cannot/should not move protons from 1.1 RBE compared to photons to some “better” modeling of this effect without clinical validation. Protons have been using a basic conversion (1.1) since they went clinically live. We are on the cusp of “thinking” we have better calculations. These new approaches demand clinical validation.
A recent article was published evaluating Mayo Clinic’s variable RBE model. As many people know, when we deal with particles there is a bit more to consider when trying to model dose. To me, this is a very detailed and complex area - quite foreign to many of us who have lived in the photon world, so today, we’ll try to keep it rather simple.
Just a few quotes from the paper for flavor.
Main results: Marked differences were observed between the results of the phenomenological proton RBE models, as reported in previous studies…
Significance: The study highlights the importance of considering cell-specific characteristics and detailed radiation quality information for accurate RBE calculations in proton therapy…
As mentioned above, we have traditionally converted dose with an RBE of 1.1 but computer planning systems keep getting more advanced and we are beginning to better factor in additional factors of LET and/or variable RBE modeling. This paper discusses their approach. I won’t dive into the paper, we’ll stay at a higher / broader level today.
I think it is safe to say, that we are certain that our traditional modeling is too simplistic - that by integrating LET and better RBE modeling that we can further optimize dose distributions and better define tumor control and risk to any adjacent organs.
There are, in fact, a number of different models and a number of approaches to this issue (this study evaluated 6 different approaches to give you a feel for the complexity), but as a physician on the pure clinical side of the equation, I always return to the basic question of:
How do we make the jump to the clinical implementation of these models?
Maybe it is simple and we just use lab and physics studies without any clinical data and simply jump?
That would be similar to the jump from homogeneous calcs to heterogeneous calcs. We “thought we knew” enough to make the math / physics jump and we did it, but here, I think the physical science pairs with cellular RBE type effects. Together, those interactions make this transition have at least one extra layer of uncertainty (likely far more).
Today, we’ll try to propose a safe, practical jump within the clinic that I believe could be used to validate any transition between models with rather limited patients and rather quickly (say 50-100 patients, within 2 years).
Background Context:
In simple terms, the components to this conversation can be highly technical. We’ll try to really simplify.
Three components come into play:
1) Uncertainty.
Particles have uncertainty in their behavior - we model them with Monte Carlo calculations for this reason (thousands of calcs to estimate the likely landing spot for the individual particles). And where they ultimately stop and deposit energy affects RBE for that tissue on a cellular level.
2) LET.
Linear Energy Transfer describes the transfer of energy based on things we know pretty well - for example, the energy of the protons, the density of the tissue, the atomic composition of the tissue, and beam characteristics like width and shape. Since we know most of these things, we’re better at modeling this one than…
3) RBE.
Relative Biological Effectiveness is the relative effective difference between particles and photons - ie, when they deposit dose (which they do differently in part due to the charge), there is a relative difference in cellular effect. It varies based on a number of factors - again exactly how the treatment damages the DNA.
This one is affected by the particle, your biological endpoint (cell survival vs tissue damage), tissue sensitivity due to cell type, fractionation scheme and dose rate. So this one corresponds to LET but is harder to calculate.
All of that said, today’s models simply use 1.1 to convert between proton and photon doses. Protons have 1.1x the impact so we “give” ~10% less dose for the same effect.
That is as simple as I can get it. They are all intertwined and there are thousands of people working on “resolving” as much of this science as we can in the lab and translating that information into planning systems. But ultimately, what I believe we should care about is:
Does an improvement in the model - moving from non-LET optimized, uniform 1.1 RBE to more complex models result in better outcomes? - ie a verification of the work to outcomes.
As a reference document, I recommend this AAPM TG-256 document. As a physics document goes, it is quite readable and is my personal go-to refresher.
My analogy: A Spaceship
Consider all the crazy math that goes into rocket ships. It is amazing what we can do, but seeing the rocket take off and now land is what truly validates the work. While there is complex math and physic principles underlying the lunar landing, seeing it happen drove it all home. Here to, I believe we must validate the work.
My Simple Idea:
Treat intermediate risk prostate cancer. Treat men to 6000 cGy(RBE)/20 fractions with either our current “traditional model” or a new “LET/RBE optimized model”.
Monitor PSA kinetics and toxicity. My thinking is 60/20 is the “low end” of the appropriate dose curve - shorter than traditional, but still fractionated to where I trust the kinetics data.
If we “optimize” the dose delivery and actually bump it up 5%-10% within the prostate due to improvements in modeling - it has a real chance to show up in improved PSA kinetics at the 1 yr and 2 yr mark - using the simple benchmark of “lower to faster”. (I’ve discussed kinetics and the data supporting this simple concept extensively on this site - suffice it to say, there is a plethora of data).
What we should not see with the change is worse kinetics or increases in toxicity. If we see either, we need to stop and pause.
Why this approach?
First, I believe that we require validation.
Secondly, it is an amazingly low risk approach - nearly a guarantee of no survival difference or an likely no DM risk difference.
Third, it has real potential to demonstrate not just a safe transition of basic proton dose modeling calculation but an ability to document via kinetics that we have IMPROVED our dose representation.
Forth, I can’t think of another setting where this type of difference is even feasible within the next 2 years.
We have a large volume daily patient base of prostate cancer for accrual.
Tear it up / break it down, or let’s get it done.
We need something easy to complete, relatively quick and accruable, and repeatable:
These models are coming. There are multiple approaches out there. Maybe they are all just simply better but somehow, I think they should be validated. At least to some extent.
We likely can’t validate all of the components - like RBE based on tissue and cell line type and cure vs. complication rate, but I do think it is wise for us to demonstrate some method to validate the jump from the lab to the clinic.
The consideration of moving to a new dose model for the treatment of a child, while pushing dose constraints and OARs limits, makes me convinced that clinical validation in a lower risk cohort is a required pre-requisite.
And using this approach, every model should be able to acquire 100 prostate cases - easily. Maybe it isn’t perfect and maybe the kinetics can’t really show “improvement” in the model but AT LEAST there will be some validation of the jump to the new model. I think the proton industry nearly requires that of us.
Granted, I believe we largely jumped in photons from homogeneous to non-homogeneous plans without this type of validation but here are some stats showing the massive scale that favors photons in picking up warning / safety issues:
As of 2021, less than 280,000 patients had been treated with protons in the history of the technology. Compared to 2020 when 1.06 million patients were treated with photon radiation in the US alone.
The scale on the photon side adds safety and the jump to heterogeneous was rather straightforward and “obvious” - photons transmit through bone and lung differently. Here, the issue is far more complex and difficult to comprehend. And even in the prior scenario where we used to treat lung like bone, I ran duplicate plans slowly transitioning my clinical practice for years - literally. I think we should treat this transition of modeling with an additional level of caution and respect due to complex interactions within radiobiological effect and linear energy transfer. Today, in many sites, we push dose closer to the edge than we have ever done in the history of our field. I believe we owe this type of validation to this approach - to both our past and future patients.
I’m stopping this one here - not much else to say. The lack of randomized data hurt the field now 30 years into our history. We need to avoid running various different models at different institutions. That approach will harm the technology moving forward in the long-run. I think it is wise for us to consolidate around mechanisms where we can validate clinical outcomes for these laboratory based improvements.
If you look at carbon data, even the basic prescriptions out of Japan and the EU have historically been different. It would be a shame for our proton modeling to de-coalesce into a similar non-uniform structure.
As always, a concept of one. If this ain’t it, please comment or reach out - I believe we need good discussion about how we move forward from world-wide leadership as we work to implement these newer approaches into clinical application.
And yes, I see the irony that prostate cancer might be used to validate progress in proton therapy dosimetry, but maybe, that is why it is so appropriate. After all, they aren’t going away and we have plenty of them under beam - if they can help push the dose calculations forward, that would be a path towards better.
did you mean 6000 cGy(RBE)/20 Fractions?
If it's still 6000 cGy (absorbed/physical), nothing's different in the patient, just what you *think* the resulting BED is (6600 classical, something higher, probably with "modern modelling").
With modern modelling (non-constant RBE), and a fixed BED prescription, you're delivering less physical/absorbed dose than you would have with a constant RBE model.
But you mention bumping up by 5% to 10% (increase physical dose for classical model, probably close to previous physical dose for modern model because the RBE would be higher in the modern model). What about the OAR? Part of the idea of variable RBE is that the plan would then put the higher RBE in the tumour and the lower RBE in the OAR (LET optimisation).
Not arguing the value of clinical evidence. Just trying to understand your proposal.
What about going back and recalculating effective dose on already treated patients (especially for PBS) given the physical dose that was used (more or less in-silico trial) and review outcome vs. "modern model" RBE?
FYI - a more complete version of this concept is being written. I tried to put down more of my thinking and data supporting the path into a much more developed document based on comments and feedback. Likely in the next week it will be pushed.