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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 Stief
  • , Steffen Löck
  • , Alex Zwanenburg
  • , Christian Trapp
  • , Denise Bernhardt
  • Stephan G. Nekolla, Minglun Li, Claus Belka, Stephanie E. Combs, Matthias Eiber, Lena Unterrainer, Marcus Unterrainer, Peter Bartenstein, Anca L. Grosu, Constantinos Zamboglou, Jan C. Peeken
  • Albert-Ludwigs-Universität Freiburg
  • German Cancer Research Center
  • University Medical Center
  • Ludwig-Maximilians-Universität München
  • Technical University of Munich
  • Universitätsklinikum Carl Gustav Carus Dresden
  • National Center for Tumor Diseases (NCT/UCC) Dresden
  • Technischen Universität Dresden
  • HelmholtzZentrum Dresden-Rossendorf
  • Helmholtz Zentrum München German Research Center for Environmental Health
  • European University of Cyprus

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

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 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.

Original languageEnglish
Pages (from-to)2537-2547
Number of pages11
JournalEuropean Journal of Nuclear Medicine and Molecular Imaging
Volume50
Issue number8
DOIs
StatePublished - Jul 2023

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Outcome prediction
  • PSMA-PET/CT
  • Personalization
  • Prostate cancer
  • Radiomics
  • Salvage radiotherapy

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