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Cancer Therapy Outcome Prediction Based On Dempster-Shafer Theory and PET Imaging

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C Lian

C Lian1,4*, H Li2 , T Denoeux1 , H Chen2 , C. Robinson2 , P Vera3,4 , S Ruan4 , (1) Sorbonne University, University of Technology of Compiegne, CNRS, UMR 7253 Heudiasyc, 60205 Compiegne, France (2) Washington University School of Medicine, Saint Louis, MO, USA (3) Centre Henri-Becquerel, 76038 Rouen, France (4) University of Rouen, QuantIF - EA 4108 LITIS, 76000 Rouen, France

Presentations

MO-AB-BRA-10 (Monday, July 13, 2015) 7:30 AM - 9:30 AM Room: Ballroom A


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.


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