Non-operative management of rectal cancer is becoming increasingly important - more and more patients are offered observation instead of surgery if they have a complete response after (chemo-)radiotherapy #radonc#ESTRO2020
Most published series are only reporting on that select group of patients - the ones who got a complete response. That's great if we want to understand if observation is a safe strategy for those patients #ESTRO2020 thelancet.com/journals/lance…
But it doesn't tell us how large a proportion of *all treated* patients can get long term control without surgery. Or really give us any robust information as to whether we can increase that number by altering our treatment up front #ESTRO2020#radonc
We are particularly interested in whether dose escalation has a role to play!
I've previously explained why dose escalation to the primary tumour probably doesn't help much in the neoadjuvant setting:
We systematically searched the literature for studies of non-operative management of rectal cancer; which: 1) Included the total patient cohort treated, not just those managed with observation 2) Reported the dose to tumour 3) Reported total proportion without surgery at 2 years
Now, out of the 15 papers that we found, 6 of them didn't systematically screen patients for complete response. So we couldn't trust that they actually allowed everybody to avoid surgery who could do so - i.e. their estimate of 'proportion managed without surgery' will be biased
One more tricky part: Some of the patient cohorts were treated with brachy or Papillon boosts - so estimating the tumour dose wasn't super easy ... #ESTRO2020#medphys
For one cohort, I actually had MRI-based dose plans, so getting exact EQD2 doses was possible. And for another (Papillon) cohort, I got information on tumour thickness and individual prescriptions + applicators - so I could estimate the average tumour EQD2 #ESTRO2020#medphys
For others, I did a lot of approximations, based on average cohort tumour size and application information ...
(Side note: We're improving on this! People have kindly shared individual patient dose data with me, so we'll do better for the final publication!) #ESTRO2020
But altogether, we were able to estimate a dose response for local control (without surgery) as a function of tumour EQD2, using a weighted and bounded logistic regression. Our best estimate:
D50 = 71Gy
gamma50 = 1.1 #ESTRO2020#radonc
One more important thing:
We know that smaller / early stage rectal cancers are more likely to respond to RT. There is an obvious risk that the observed dose-response relationship is purely due to cohorts with early cancers also receiving higher doses #ESTRO2020#radonc
So we controlled for this! We used a IPD meta analysis based measure of relative response probability, adjusted the data points accordingly, and allowed for different upper bounds for T1-2 and T3-4 cancers thelancet.com/journals/lanon… #ESTRO2020#radonc
Even when doing this, we still saw clear dose-response
- and we found different upper bounds for the maximum local control we can expect to achieve for early (~80%) and advanced (~65%) cancers.
D50 = 66Gy for T1-2 and 85Gy for T3-4 #ESTRO2020#radonc
If you think about it, we are actually unlikely to see a dose-response relationship for rectal cancer in the (neo-)adjuvant setting: Even if there exists a dose-response relationship, it must be very shallow
After reviewing 'predictive modelling & radiomics' abstracts for #ESTRO202, I had quite a few thoughts. I've finally found time to organise them in a semi-coherent manner
To follow: Some common pitfalls in modelling & radiomics abstracts for clinical conferences #radonc#medphys
First of all, the basic stuff:
Get somebody who’s never seen your study before to read through the abstract - to ensure fundamental information isn't missing.
(And no, you won't notice yourself, because you’re too concerned with whether you can squeeze in another AUC value ...)
If you are submitting to a radiotherapy conference, maybe make clear what the relevance is for radiotherapy? Several image analysis / radiomics / AI abstracts were probably technically excellent, but I scored them low due to lack of radiotherapy relevance
First, what characterises medical physicists?
- We're quantitative, systematic & analytical
- We're trained in modelling, data visualisation, & interpretation of evidence
(And sometimes we - by which I mean me - go exploring in caves, which is almost like running a trial 😅)
But importantly, we understand the opportunities and limitations in current technology & are uniquely placed to understand current gaps in knowledge.
We can ask
“How can we best utilise technology to improve outcomes?”
“Will this be achievable in daily practice?”