Development of PSMA-PET-guided CT-based radiomic signature to predict biochemical recurrence after salvage radiotherapy
Simon K. B. Spohn, Nina-Sophie Schmidt-Hegemann, Juri Ruf, Michael Mix, Matthias Benndorf, Fabian Bamberg, Marcus R. Makowski, Simon Kirste, Alexander Rühle, Jerome Nouvel, Tanja Sprave, Marco M. E. Vogel, Polina Galitsnaya, Jürgen E. Gschwend, Christian Gratzke, Christian G. Stief, Steffen Löck, Alex Zwanenburg, Christian Trapp, Denise Bernhardt, Stephan G. Nekolla, Minglun Li, Claus Belka, Stephanie E. Combs, Matthias Eiber, Lena M. Unterrainer, Marcus Unterrainer, Peter Bartenstein, Anca‐L. Grosu, Constantinos Zamboglou, Jan C. Peeken
Abstract
PURPOSE: To develop a CT-based radiomic signature to predict biochemical recurrence (BCR) in prostate cancer patients after sRT guided by positron-emission tomography targeting prostate-specific membrane antigen (PSMA-PET). MATERIAL AND METHODS: Ga-PSMA11-PET/CT-guided sRT from three high-volume centers in Germany, were included in this retrospective multicenter study. Patients had PET-positive local recurrences and were treated with intensity-modulated sRT. Radiomic features were extracted from volumes of interests on CT guided by focal PSMA-PET uptakes. After preprocessing, clinical, radiomics, and combined clinical-radiomic models were developed combining different feature reduction techniques and Cox proportional hazard models within a nested cross validation approach. RESULTS: Among 99 patients, median interval until BCR was the radiomic models outperformed clinical models and combined clinical-radiomic models for prediction of BCR with a C-index of 0.71 compared to 0.53 and 0.63 in the test sets, respectively. In contrast to the other models, the radiomic model achieved significantly improved patient stratification in Kaplan-Meier analysis. The radiomic and clinical-radiomic model achieved a significantly better time-dependent net reclassification improvement index (0.392 and 0.762, respectively) compared to the clinical model. Decision curve analysis demonstrated a clinical net benefit for both models. Mean intensity was the most predictive radiomic feature. CONCLUSION: This is the first study to develop a PSMA-PET-guided CT-based radiomic model to predict BCR after sRT. The radiomic models outperformed clinical models and might contribute to guide personalized treatment decisions.