Prediction of metastasis-free survival in patients with localized prostate adenocarcinoma using primary tumor and lymph node radiomics from pre-treatment PSMA-PET/CT scans

Purpose: To predict metastasis-free survival (MFS) for patients with prostate adenocarcinoma (PCa) treated with androgen deprivation therapy (ADT) and external radiotherapy using clinical factors and radiomics extracted from primary tumor and node volumes in pre-treatment PSMA PET/CT scans. Materials/Methods: Our cohort includes 134 PCa patients (nodal involvement in 28 patients). Gross tumor volumes of primary tumor (GTVp) and nodes (GTVn) on CT and PET scans were segmented. A 5 mm expansion ring area outside primary tumor was defined. Z-score normalization was applied to radiomics features extracted from tumor and ring; dimension reduction was performed using Principal Components Analysis (PCA). For patients with only primary tumor, we took 3 principal components (PCs) from GTVp and one ring PC as representative radiomics components from CT and PET scans. For patients with nodes, we calculated weighted average (by volume) of radiomics from primary tumor and nodes, computed first 3 PCs and combined it with 1st PC from the ring. Radiomics PCs and clinical variables (age, Gleason score, initial prostate specific antigen value (i PSA), PSA_relapse) formed the predictors. Due to MFS data imbalance (metastasis-24, no metastasis-110), we performed 70:30 train-test split and applied imbalance correction to training data. Univariate Cox-regression was used to select top predictors (logistic regression p < 0.05). Multivariate Cox-regression was performed on imbalance-corrected training data and fit on testing data (using predictors selected from training). Model 2 was built using clinical variables and radiomic PCs from primary tumors (GTVp, ring). Model 3 was built using clinical variables only. Binary classification analysis for prediction of five-year MFS was also performed. Results: Results of time-to-event analysis (MFS) were: Cox-regression c-scores: model1: train- 0.77 [0.72, 0.78]; test- 0.69 [0.64, 0.70]; model2: train- 0.72 [0.66, 0.73]; test- 0.63 [0.58, 0.64]; model3: train- 0.62 [0.57, 0.63]; test- 0.54 [0.51, 0.56]. The results of 5 year MFS classification analysis were [sensitivity, specificity, AUC]: model 1: train- [83.6 %, 91.3 %, 0.88]; test- [76.3 %, 82.5 %, 0.81]; model 2: train- [77.4 %, 85.1 %, 0.84]; test- [71.5 %, 78.2 %, 0.76]; model 3: train- [69.3 %, 78.2 %, 0.76]; test- [64.7 %, 72.6 %, 0.68]. The two cohorts of patients classified by model 1 showed statistically significant differences in their actual survival curves, demonstrating the efficacy of the classification. Integration of node with primary tumor-radiomics provides the best prognostic performance in MFS prediction. Conclusion: This is one of the first studies to explore the prognostic value of pre-treatment PSMA-PET, a relatively recent advancement in the care of prostate adenocarcinoma patients. Results demonstrated the potential of using imaging biomarkers from PSMA-PET/CT images for prognosis prediction before the treatment, which provides clinicians valuable information for customizing the treatment paradigm to improve the outcomes for primary prostate cancer patients.