ESTRO 2023 - Abstract Book

S448

Sunday 14 May 2023

ESTRO 2023

Figure 1: Biochemical relapse-free survival in patient undergoing MRI (green) and not undergoing MRI (blue) before salvage radiotherapy (p<0.001). Conclusion An unexpectedly and surprisingly high rate of macroscopic relapses was recorded in patients undergoing MRI prior to salvage RT. This made it possible to adapt the treatment by delivering a boost to the disease site. This strategy produced a significant improvement in the biochemical outcome of these patients. Our findings challenge current guidelines on the pre-treatment restaging in this setting. PD-0567 CT-based radiomic signatures to predict biochemical recurrence after salvage radiotherapy S. Spohn 1 , N. Schmidt-Hegemann 2 , J. Ruf 3 , M. Mix 3 , M. Benndorf 4 , M.R. Makowski 5 , S. Kirste 1 , M.M. Vogel 6 , J.E. Gschwend 7 , C. Gratzke 8 , C. Stief 9 , S. Löck 10 , A. Zwanenburrg 10 , C. Trapp 2 , P. Galitsnaya 6 , S.G. Nekolla 11 , C. Belka 2 , S.E. Combs 6 , M. Eiber 11 , L. Unterrainer 12 , M. Unterrainer 12 , P. Bartenstein 12 , A.L. Grosu 1 , C. Zamboglou 1 , J.C. Peeken 6 1 University Medical Center Freiburg, Department of Radiation Oncology, Freiburg, Germany; 2 University Hospital, LMU Munich, Department of Radiation Oncology, Munich, Germany; 3 University Medical Center Freiburg, Department of Nuclear Medicine, Freiburg, Germany; 4 University Medical Center Freiburg, Department of Radiology, Freiburg, Germany; 5 Klinikum rechts der Isar, Technical University of Munich, Department of Radiology, Munich, Germany; 6 Klinikum rechts der Isar, Technical University of Munich, Department of Radiation Oncology, Munich, Germany; 7 Department of Urology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; 8 University Medical Center Freiburg, Department of Urology, Munich, Germany; 9 University Hospital, LMU Munich, Department of Urology, Munich, Germany; 10 Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, OncoRay - National Center for Radiation Research in Oncology, Dresden, Germany; 11 Klinikum rechts der Isar, Technical University of Munich, Department of Nuclear Medicine, Munich, Germany; 12 University Hospital, LMU Munich, Department of Nuclear Medicine, Munich, Germany Purpose or Objective Prostate cancer (PCa) patients, who receive salvage radiotherapy (sRT) due to biochemical recurrence (BCR) after surgery experience heterogeneous response rates. In search of novel non-invasive biomarkers, this study aims to develop a CT based radiomic signature to predict BCR after sRT guided by positron-emission tomography targeting prostate-specific membrane antigen (PSMA). Materials and Methods Ninety-nine 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. Patients with evidence of nodal or distant metastases in PSMA-PET or received ADT prior to PSMA imaging were not eligible. PSMA-PET/CT were performed with iodine-based contrast-enhanced CTs using 120 kVp and exposure of 100–400 mAs (dose modulation) for attenuation correction. Images were acquired at portal venous phases approximately 80 seconds after injection of contrast agents. Median slice thickness of CT images was 3 mm (range 1.5 – 5) with a median voxel of 0.97 x 0.97 mm (range 0.68 x 0-68 – 1.17 x 1.17x). Radiomic features were extracted from volumes of interests on CT, guided by focal PSMA PET uptakes. After pre-processing, clinical-, radiomics- and combined clinical-radiomics models were developed combining different feature reduction techniques and Cox proportional hazard models within a nested cross validation approach. Results Median follow-up (FU) was 29 (range 3-79) months, in which 26% of patients experienced BCR. The radiomic models outperformed clinical models and combined clinical-radiomics 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 and improved receiver operator characteristics analysis (ROC) at 24 months of FU. 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.