%0 Journal Article
%A Spohn, Simon K B
%A Schmidt-Hegemann, Nina-Sophie
%A Ruf, Juri
%A Mix, Michael
%A Benndorf, Matthias
%A Bamberg, Fabian
%A Makowski, Marcus R
%A Kirste, Simon
%A Rühle, Alexander
%A Nouvel, Jerome
%A Sprave, Tanja
%A Vogel, Marco M E
%A Galitsnaya, Polina
%A Gschwend, Jürgen E
%A Gratzke, Christian
%A Stief, Christian
%A Löck, Steffen
%A Zwanenburg, Alex
%A Trapp, Christian
%A Bernhardt, Denise
%A Nekolla, Stephan G
%A Li, Minglun
%A Belka, Claus
%A Combs, Stephanie E
%A Eiber, Matthias
%A Unterrainer, Lena
%A Unterrainer, Marcus
%A Bartenstein, Peter
%A Grosu, Anca-L
%A Zamboglou, Constantinos
%A Peeken, Jan C
%T Development of PSMA-PET-guided CT-based radiomic signature to predict biochemical recurrence after salvage radiotherapy.
%J European journal of nuclear medicine and molecular imaging
%V 50
%N 8
%@ 1619-7070
%C Heidelberg [u.a.]
%I Springer-Verl.
%M DKFZ-2023-00549
%P 2537-2547
%D 2023
%Z 2023 Jul;50(8):2537-2547
%X 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).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.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.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.
%K Outcome prediction (Other)
%K PSMA-PET/CT (Other)
%K Personalization (Other)
%K Prostate cancer (Other)
%K Radiomics (Other)
%K Salvage radiotherapy (Other)
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:36929180
%R 10.1007/s00259-023-06195-3
%U https://inrepo02.dkfz.de/record/274347