Arthur Braat

UMC Utrecht

In short, Arthur Braat's research entails the following:

TowArds IndividuaLized PSMA PET/CT-guided Treatment in Metastastatic PrOstate CanceR Using Machine Learning-Derived Risk Stratification (TAILOR-MADE)

Hormone sensitive metastatic prostate cancer (mHSPC) can present itself in various forms regarding aggressiveness, metastatic burden and prognosis. Accurate disease classification at diagnosis is crucial for accurate treatment selection. Nowadays, PSMA PET/CT is the gold standard, with significantly higher sensitivity to detect metastasis compared to conventional CT and bone scan.Use of PSMA PET/CT instead of bone scintigraphy and CT, results in detection of additional lesions in almost two-thirds of the patients. The current dichotomous classification system, based on the location and number of metastasis diagnosed on bone- and CT scan, classifies patients to “low-volume” [LVD] or “high-volume” [HVD]. Classification to either LVD or HVD has major clinical implications, as different treatment strategies have been shown to be effective in both risk groups.

In today’s clinical routine, CT and bone scanning has been completely replaced by PSMA PET/CT.

As a consequence, the currently available risk stratification system may not be generalizable to the contemporary patient population. In addition, as the mHSPC includes a very heterogeneous patient population, it is attainable that the currently available dichotomous stratification system insufficiently accounts for the heterogeneity in disease prognosis.

Therefore, a new PSMA PET/CT based risk stratification system and contemporary prognostic biomarkers are urgently needed. Not only to improve our current clinical care of men diagnosed with metastatic prostate cancer, but also to ensure the results of future trials remain generalizable to the contemporary state of practice. In addition, availability of a more robust stratification system will improve comparability of in-between trial results and assessment of possible reasons why one trial is positive but the other not.

In this project we will use artificial intelligence (AI) for automatic disease segmentation in PSMA PET/CT’s, image-derived radiomics and identification of predictive features. Together with machine learning and clinical data, we intend to develop a novel AI-based risk classification for mHSPC.