MO‐AB‐BRA‐10: Cancer Therapy Outcome Prediction Based On Dempster‐Shafer Theory and PET Imaging

Abstract

Purpose:In cancer therapy, utilizing FDG‐18 PET image‐based features for accurate outcome prediction is challenging because of 1) limited discriminative information within a small number of PET image sets, and 2) fluctuant feature characteristics caused by the inferior spatial resolution and system noise of PET imaging. In this study, we proposed a new Dempster‐Shafer theory (DST) based approach, evidential low‐dimensional transformation with feature selection (ELT‐FS), to accurately predict cancer therapy outcome with both PET imaging features and clinical characteristics.Methods:First, a specific loss function with sparse penalty was developed to learn an adaptive low‐rank distance metric for representing the dissimilarity between different patients’ feature vectors. By minimizing this loss function, a linear low‐dimensional transformation of input features was achieved. Also, imprecise features were excluded simultaneously by applying a l2,1‐norm regularization of the learnt dissimilarity metric in the loss function. Finally, the learnt dissimilarity metric was applied in an evidential K‐nearest‐neighbor (EK‐ NN) classifier to predict treatment outcome.Results:Twenty‐five patients with stage II–III non‐small‐cell lung cancer and thirty‐six patients with esophageal squamous cell carcinomas treated with chemo‐radiotherapy were collected. For the two groups of patients, 52 and 29 features, respectively, were utilized. The leave‐one‐out cross‐validation (LOOCV) protocol was used for evaluation. Compared to three existing linear transformation methods (PCA, LDA, NCA), the proposed ELT‐FS leads to higher prediction accuracy for the training and testing sets both for lung‐cancer patients (100+/−0.0, 88.0+/−33.17) and for esophageal‐cancer patients (97.46+/−1.64, 83.33+/−37.8). The ELT‐FS also provides superior class separation in both test data sets.Conclusion:A novel DST‐ based approach has been proposed to predict cancer treatment outcome using PET image features and clinical characteristics. A specific loss function has been designed for robust accommodation of feature set incertitude and imprecision, facilitating adaptive learning of the dissimilarity metric for the EK‐NN classifier.