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Spohn, S.K.B.* ; Schmidt-Hegemann, N.S.* ; Ruf, J.* ; Mix, M.* ; Benndorf, M.* ; Bamberg, F.* ; Makowski, M.R.* ; Kirste, S.* ; Ruhle, A.* ; Nouvel, J.* ; Sprave, T.* ; Vogel, M.M.E.* ; Galitsnaya, P.* ; Gschwend, J.E.* ; Gratzke, C.* ; Stief, C.* ; Löck, S.* ; Zwanenburg, A.* ; Trapp, C.* ; Bernhardt, D.* ; Nekolla, S.G.* ; Li, M.* ; Belka, C.* ; Combs, S.E. ; Eiber, M.* ; Unterrainer, L.* ; Unterrainer, M.* ; Bartenstein, P.* ; Grosu, A.L.* ; Zamboglou, C.* ; Peeken, J.C.

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

Eur. J. Nucl. Med. Mol. Imaging 50, 2537-2547 (2023)
Verlagsversion DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
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.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Outcome Prediction ; Psma-pet/ct ; Personalization ; Prostate Cancer ; Radiomics ; Salvage Radiotherapy; Prostate-cancer; Validation
Sprache englisch
Veröffentlichungsjahr 2023
HGF-Berichtsjahr 2023
ISSN (print) / ISBN 1619-7070
e-ISSN 1432-105X
Zeitschrift European Journal of Nuclear Medicine and Molecular Imaging
Quellenangaben Band: 50, Heft: 8, Seiten: 2537-2547 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort One New York Plaza, Suite 4600, New York, Ny, United States
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Radiation Medicine (IRM)
POF Topic(s) 30203 - Molecular Targets and Therapies
Forschungsfeld(er) Radiation Sciences
PSP-Element(e) G-501300-001
Förderungen Projekt DEAL
DOI 10.1007/s00259-023-06195-3
WOS ID 000950456600002
Scopus ID 85150341900
PubMed ID 36929180
Erfassungsdatum 2023-10-06
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