WE-C-WAB-02: Joint FDG-PET/MR Imaging for the Early Prediction of Tumor Outcomes

Purpose: To investigate the potential of joint FDG‐PET/MR imaging features for the prediction of lung metastases at diagnosis of soft‐tissue sarcomas (STS). Methods: A cohort of 35 patients with histologically proven STS was used in this study. All patients underwent pre‐treatment FDG‐PET and MR scans that comprised T1 and T2‐fat suppression weighted (T2FS) sequences. The cohort had a median follow‐up period of 29 months (range: 4–85) during which 13 patients developed lung metastases. An SUV feature (SUVmax) from the FDG‐PET scans and 6 texture features (energy, entropy, contrast, homogeneity, sum‐mean and variance) from the co‐occurrence matrix of the separate (FDG‐PET, T1 and T2FS) and fused (FDG‐PET/T1 and FDG‐PET/T2FS) scans were extracted from the tumor region. Fusion of the scans was implemented using the wavelet transform. Multivariable modeling was performed using logistic regression (LR) and the corresponding performance for lung metastases prediction was assessed using receiver operating characteristic (ROC) metrics on bootstrapping resampling. Optimal texture extraction was carried out through the optimization of intensity quantization, spatial resolution and wavelet band‐pass filtering. Results: Overall, textures extracted from fused scans outperformed those from separate scans for the prediction of lung metastases. The best performance was found using an LR model with the following 4 parameters: SUVmax, FDG‐PET/T1‐‐contrast, FDG‐PET/T1‐‐homogeneity and FDG‐PET/T2FS‐‐variance. The average performance of this model in 10000 bootstrapping testing sets was: AUC=0.956, sensitivity=0.909, specificity=0.925, accuracy=0.916. However, the upper limit on the uncertainty of the texture model due to contouring variations was evaluated to be 15%. Conclusion: Our results demonstrate that fused FDG‐PET/MR texture features can be used to evaluate lung metastasis risk at diagnosis of STS. Accurate risk assessment could improve patient outcomes by allowing better adapted treatments. The methodology developed in this study could be tested on other cancer types and clinical endpoints such as treatment response.