3237 - Radiomic Analysis of Pre-Treatment PSMA PET/CT for Prognostic Stratification in Patients with Oligometastatic Castration-Sensitive Prostate Cancer
Presenter(s)
Y. Cao1, P. Sutera2, W. Mendes3, Z. Jiang4, A. Sawant5, L. Marchionni6, N. L. Simone7, P. T. Tran8, C. Onal9, and L. Ren5; 1Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, 2Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins Medicine, Baltimore, MD, 3University of Maryland School of Medicine, Baltimore, MD, USA, Baltimore, MD, 4Duke University, Durham, NC, 5University of Maryland, School of Medicine, Radiation Oncology, Baltimore, MD, 6Department of Pathology and Laboratory Medicine, NewYork-Presbyterian/Weill Cornell Medical Center, New York, NY, 7Dept. of Radiation Oncology, Sidney Kimmel Medical College and Comprehensive Cancer Center, Thomas Jefferson University, Philadelphia, PA, 8Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 9Baskent University Faculty of Medicine, Department of Radiation Oncology, Ankara, Turkey
Purpose/Objective(s): Emerging data suggest metastasis-directed therapy (MDT) improves outcomes in patients with a unique subset of CSPC called oligometastatic castration-sensitive prostate cancer (omCSPC). However, there remains a critical need for robust biomarkers to guide patient selection and assess treatment response. Molecular imaging modalities, such as PET and CT, have shown promise in providing biologically relevant information that could support risk stratification and personalized management strategies in this population.To predict metastasis-free survival (MFS), this study explored imaging biomarkers obtained from baseline multi-modality imaging (PET and CT) before metastasis-directed therapy (MDT). These biomarkers could offer early response prediction before MDT treatment, optimizing patient management and improving outcomes.
Materials/Methods: The study analyzed a multi-institutional cohort of 118 patients with oligometastatic castration-sensitive prostate cancer (omCSPC), including 34 from Johns Hopkins Hospital (JHH) and 84 from Baskent University (BU), all treated with stereotactic ablative radiation therapy (SABR) MDT. Before MDT, all patients underwent PSMA PET and CT imaging. For radiomics analysis, the gross tumor volume (GTV) was defined as zone 1, with an additional 5 mm peritumoral expansion designated as zone 2. From these regions, 1308 radiomics features were extracted. Feature selection was performed using a mutual information function, identifying the five most informative radiomics features from PSMA PET and CT. These were combined with five key clinical parameters—age, Gleason score, total number of lesions, number of untreated lesions, and pre-MDT prostate-specific antigen (PSA)—as model inputs. Multiple machines learning algorithms, including random forest, decision tree, support vector machine, and naïve Bayes, were applied to predict 2-year metastasis-free survival (MFS). Model performance was evaluated using both leave-one-out and cross-institution validation.
Results: In a leave-one-out test with 93 patients, random forest achieved 78% accuracy and an AUC of 0.80 in predicting 2-year MFS. In cross-institution validation with 61 BU and 32 JHH patients, random forest correctly predicted 2-year MFS for 69% and 71% of patients, with AUC values of 0.71 and 0.73, respectively. Kaplan Meier curve comparison shows statistically significant separation between “rapid progressors” and “non-rapid progressors” patients stratified by the model in both leave one out and cross-institution validation tests.
Conclusion: This study provides evidence that pre-treatment multi-modality imaging biomarkers derived from PSMA PET and CT can serve as valuable predictors of metastasis-free survival (MFS) in patients with omCSPC.