TY  - JOUR
AU  - Spohn, Simon K B
AU  - Schmidt-Hegemann, Nina-Sophie
AU  - Ruf, Juri
AU  - Mix, Michael
AU  - Benndorf, Matthias
AU  - Bamberg, Fabian
AU  - Makowski, Marcus R
AU  - Kirste, Simon
AU  - Rühle, Alexander
AU  - Nouvel, Jerome
AU  - Sprave, Tanja
AU  - Vogel, Marco M E
AU  - Galitsnaya, Polina
AU  - Gschwend, Jürgen E
AU  - Gratzke, Christian
AU  - Stief, Christian
AU  - Löck, Steffen
AU  - Zwanenburg, Alex
AU  - Trapp, Christian
AU  - Bernhardt, Denise
AU  - Nekolla, Stephan G
AU  - Li, Minglun
AU  - Belka, Claus
AU  - Combs, Stephanie E
AU  - Eiber, Matthias
AU  - Unterrainer, Lena
AU  - Unterrainer, Marcus
AU  - Bartenstein, Peter
AU  - Grosu, Anca-L
AU  - Zamboglou, Constantinos
AU  - Peeken, Jan C
TI  - Development of PSMA-PET-guided CT-based radiomic signature to predict biochemical recurrence after salvage radiotherapy.
JO  - European journal of nuclear medicine and molecular imaging
VL  - 50
IS  - 8
SN  - 1619-7070
CY  - Heidelberg [u.a.]
PB  - Springer-Verl.
M1  - DKFZ-2023-00549
SP  - 2537-2547
PY  - 2023
N1  - 2023 Jul;50(8):2537-2547
AB  - 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.
KW  - Outcome prediction (Other)
KW  - PSMA-PET/CT (Other)
KW  - Personalization (Other)
KW  - Prostate cancer (Other)
KW  - Radiomics (Other)
KW  - Salvage radiotherapy (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:36929180
DO  - DOI:10.1007/s00259-023-06195-3
UR  - https://inrepo02.dkfz.de/record/274347
ER  -