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

Authors

  • Lian C,

    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
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  • Li H,

    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
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  • Denoeux T,

    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
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  • Chen H,

    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
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  • Robinson C.,

    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
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  • Vera P,

    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
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  • Ruan S

    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
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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.

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