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Spohn, Simon K. B.; Schmidt-Hegemann, Nina-Sophie; Ruf, Juri; Mix, Michael; Benndorf, Matthias; Bamberg, Fabian; Makowski, Marcus R.; Kirste, Simon; Rühle, Alexander; Nouvel, Jerome; Sprave, Tanja; Vogel, Marco M. E.; Galitsnaya, Polina; Gschwend, Jürgen E.; Gratzke, Christian; Stief, Christian; Löck, Steffen; Zwanenburg, Alex; Trapp, Christian; Bernhardt, Denise; Nekolla, Stephan G.; Li, Minglun; Belka, Claus; Combs, Stephanie E.; [...]

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

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  • Media type: E-Article
  • Title: Development of PSMA-PET-guided CT-based radiomic signature to predict biochemical recurrence after salvage radiotherapy
  • Contributor: Spohn, Simon K. B.; Schmidt-Hegemann, Nina-Sophie; Ruf, Juri; Mix, Michael; Benndorf, Matthias; Bamberg, Fabian; Makowski, Marcus R.; Kirste, Simon; Rühle, Alexander; Nouvel, Jerome; Sprave, Tanja; Vogel, Marco M. E.; Galitsnaya, Polina; Gschwend, Jürgen E.; Gratzke, Christian; Stief, Christian; Löck, Steffen; Zwanenburg, Alex; Trapp, Christian; Bernhardt, Denise; Nekolla, Stephan G.; Li, Minglun; Belka, Claus; Combs, Stephanie E.; [...]
  • imprint: Springer Science and Business Media LLC, 2023
  • Published in: European Journal of Nuclear Medicine and Molecular Imaging
  • Language: English
  • DOI: 10.1007/s00259-023-06195-3
  • ISSN: 1619-7089; 1619-7070
  • Keywords: Radiology, Nuclear Medicine and imaging ; General Medicine ; Radiology, Nuclear Medicine and imaging ; General Medicine
  • Origination:
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  • Description: <jats:title>Abstract </jats:title><jats:sec> <jats:title>Purpose </jats:title> <jats:p>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).</jats:p> </jats:sec><jats:sec> <jats:title>Material and methods</jats:title> <jats:p>Consecutive patients, who underwent <jats:sup>68</jats:sup>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.</jats:p> </jats:sec><jats:sec> <jats:title>Results</jats:title> <jats:p>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.</jats:p> </jats:sec><jats:sec> <jats:title>Conclusion</jats:title> <jats:p>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. </jats:p> </jats:sec>