Development of PSMA-PET-guided CT-based radiomic signature to predict biochemical recurrence after salvage radiotherapy

Author:

Spohn Simon KB1ORCID,Schmidt-Hegemann Nina-Sophie2,Ruf Juri1,Mix Michael1,Benndorf Matthias1,Bamberg Fabian1,Makowski Marcus R3,Kirste Simon1,Rühle Alexander1,Nouvel Jerome1,Sprave Tanja1,Vogel Marco ME3,Galitsnaya Polina3,Gschwend Juergen E3,Gratzke Christian1,Stief Christian2,Loeck Steffen4,Zwanenburg Alex5,Trapp Christian2,Bernhardt Denise6,Nekolla Stephan G3,Li Minglun2,Belka Claus2,Combs Stephanie E3,Eiber Matthias3,Unterrainer Lena2,Unterrainer Marcus2,Bartenstein Peter2,Grosu Anca L1,Zamboglou Constantinos1,Peeken Jan C3

Affiliation:

1. University Medical Center Freiburg: Universitatsklinikum Freiburg

2. University Hospital Munich: Klinikum der Universitat Munchen

3. Klinikum rechts der Isar der Technischen Universitat Munchen

4. TU Dresden: Technische Universitat Dresden

5. National Center for Tumor Diseases Dresden: Nationales Centrum fur Tumorerkrankungen Dresden

6. Klinikum rechts der Isar der Technischen Universität München: Klinikum rechts der Isar der Technischen Universitat Munchen

Abstract

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 Consecutive patients, who underwent 68Ga-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 pre-processing, clinical-, radiomics- and combined clinical-radiomics 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-radiomics 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.

Publisher

Research Square Platform LLC

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