PROMISE Criteria: Enhanced Survival Prediction in Prostate Cancer with PSMA PET – Wolfgang Fendler

Phillip Koo: Hi, I’m Phillip Koo and welcome to UroToday. Our special guest here at ASCO 2024 is Dr. Wolfgang Fendler, who’s a professor and vice chair of Nuclear Medicine at University of Essen. Thank you very much for joining us.

Wolfgang Fendler: My pleasure to be here. Thank you very much.

Phillip Koo: So you’ve done so much work in this space around PSMA PET and therapies and you’re continuing to sort of push the edge. I know we’ve talked in the past about the PROMISE criteria reporting, standardized reporting, but one of the promises of PROMISE was to use that as a prognostic tool and that’s what you’re presenting. And could you give us some highlights from what you’re presenting here at ASCO?

Wolfgang Fendler: Yeah, absolutely. So the background is that we defined a new language for standardized reporting of PSMA PET years ago. And at the time we did not have follow-up data available. Now 10 years after the introduction of this technology, in some areas, like in Germany, we have substantial follow-up information available. And so the central goal of this research project was to assess the prognostic value, so the association between PSMA PET using the language of PROMISE and overall survival, so the risk of a patient for dying, and this was assessed for all prostate cancer patients, early stages, mid-stages, and late stages. And so two of the highlights that we found were PSMA PET is associated with overall survival, both in early and late stages. We can combine metrics or findings from PSMA PET to predict the overall survival in patients. And there are also some interesting results from PSMA PET that we can include, such as total tumor volume that have not been looked at before.

Phillip Koo: So you compared that against other clinical criteria and I believe PSMA PET performed better, is that correct?

Wolfgang Fendler: That is correct. So our second goal was to compare the prognostic accuracy, so the association with overall survival with other clinical criteria. So to sort to get a reference from the current clinical standard. And what we found was if we use the most sophisticated way of analyzing PSMA PET images, so visual assessment and quantifying the total tumor load, then we have a better prediction of overall survival as compared to scores that are used in a clinical routine such as STAR-CAP, or the EAU score at initial prostate cancer and biochemical recurrence. And we have a very similar predictive accuracy as compared to other novel scores like the Gafita nomogram that have been proposed before. So in parts in early and also late stages of prostate cancer, PSMA PET using new metrics can really predict our survival better than the conventional and current tools.

Phillip Koo: So from what you’re seeing when you talk about PSMA PET, is it really the volume that seems to be driving that association with survival? What about intensity? We hear a lot about SUV. Was that not as important?

Wolfgang Fendler: Absolutely. The two factors that have been introduced, we analyzed multiple factors and two factors that stood out here were the total volume and the SUV mean, which is kind of an expression of the average PSMA expression, the average target expression. And these two factors that we can quantify on PET images, they have been associated with overall survival significantly and independently. And these have been included into the risk assessment for a PSMA PET to stratify patients into high versus low risk. And this is a very interesting finding in addition to our visual findings. So our staging information, whether lymph nodes or organs are included and are involved,

Phillip Koo: This is really exciting. There’s so much data and information in PSMA PET that we’re just starting to unlock a lot of this and I think this is a wonderful step forward. When you think about the future, obviously being able to combine clinical factors, clinical history with imaging, I think is kind of the holy grail where we need to be headed. Tell us a little bit more about where you’re going to go next with this project.

Wolfgang Fendler: Absolutely. This is one of the central goals. We want to have the highest accuracy for prediction, highest prognostic accuracy. And this can also be achieved by combining imaging and clinical factors. So this is certainly a way for us to go to combine imaging factors and head-to-head clinical factors to achieve a higher accuracy. And the other step that we will approach now is to open up this study to other sites. We have currently included more than 20 other sites to contribute their patients with long follow-up to reach… To name a number to reach more than 10,000 patients in total with a long follow-up, more than five to 10 years follow-up. So we have a very good analysis and a high accuracy, high statistical power to analyze the prognostic value of PSMA PET. This is also one of the next steps that we’re aiming at in a multi-center registry.

Phillip Koo: The last question is, getting physicians to change their behaviors and start using these standardized reporting tools is much easier said than done. And I think from personal experience, it’s really hard to get physicians, radiologists, nuclear medicine physicians to change how they report and score these exams. What advice do you have? Because obviously we need that baseline if we’re going to really use this as that predictive biomarker or prognostic biomarker.

Wolfgang Fendler: Absolutely, I agree. The clinical introduction always has several aspects that need to be taken into account. And this is now a scientific publication that looked at all these factors. And I think for the clinical applicability, we also have to take into account the simplicity and the reproducibility of findings. So the final goal, of course, is to come up with risk groups based on PSMA PETs, such as high risk, intermediate risk, and low risk, and to define these risk groups in a simple manner. So every department that has availability of PSMA PET can look at the images and make an assumption whether a patient is high risk or low risk by just looking at the images without any sophisticated software, without any specific tool. And this is the goal to really use our data to redefine prostate cancer risk groups in a simple way. So every department and every clinical study will be able to use those criteria in the future.

Phillip Koo: Great. I think simplicity is beautiful. So we really appreciate all the work you’ve done, and thank you for taking the time to join us today.

Wolfgang Fendler: Thank you very much. It’s been a pleasure.