Frontiers in Nuclear Medicine (Sep 2023)

Multicentric 68Ga-PSMA PET radiomics for treatment response assessment of 177Lu-PSMA-617 radioligand therapy in patients with metastatic castration-resistant prostate cancer

  • Robin Gutsche,
  • Robin Gutsche,
  • Gizem Gülmüs,
  • Felix M. Mottaghy,
  • Florian Gärtner,
  • Markus Essler,
  • Dirk von Mallek,
  • Hojjat Ahmadzadehfar,
  • Philipp Lohmann,
  • Alexander Heinzel,
  • Alexander Heinzel,
  • Alexander Heinzel

DOI
https://doi.org/10.3389/fnume.2023.1234853
Journal volume & issue
Vol. 3

Abstract

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ObjectiveThe treatment with 177Lutetium PSMA (177Lu-PSMA) in patients with metastatic castration-resistant prostate cancer (mCRPC) has recently been approved by the FDA and EMA. Since treatment success is highly variable between patients, the prediction of treatment response and identification of short- and long-term survivors after treatment could help tailor mCRPC diagnosis and treatment accordingly. The aim of this study is to investigate the value of radiomic parameters extracted from pretreatment 68Ga-PSMA PET images for the prediction of treatment response.MethodsA total of 45 mCRPC patients treated with 177Lu-PSMA-617 from two university hospital centers were retrospectively reviewed for this study. Radiomic features were extracted from the volumetric segmentations of metastases in the bone. A random forest model was trained and validated to predict treatment response based on age and conventionally used PET parameters, radiomic features and combinations thereof. Further, overall survival was predicted by using the identified radiomic signature and compared to a Cox regression model based on age and PET parameters.ResultsThe machine learning model based on a combined radiomic signature of three features and patient age achieved an AUC of 0.82 in 5-fold cross-validation and outperformed models based on age and PET parameters or radiomic features (AUC, 0.75 and 0.76, respectively). A Cox regression model based on this radiomic signature showed the best performance to predict overall survival (C-index, 0.67).ConclusionOur results demonstrate that a machine learning model to predict response to 177Lu-PSMA treatment based on a combination of radiomics and patient age outperforms a model based on age and PET parameters. Moreover, the identified radiomic signature based on pretreatment 68Ga-PSMA PET images might be able to identify patients with an improved outcome and serve as a supportive tool in clinical decision making.

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