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Lymph node metastasis (LNM) is a significant prognostic factor in patientswith head and neck cancer, and the ability to predict it accurately isessential for treatment optimization. PET and CT imaging are routinely used forLNM identification. However, uncertainties of LNM always exist especially forsmall size or reactive nodes. Radiomics and deep learning are the two preferredimaging-based strategies for node malignancy prediction. Radiomics models arebuilt based on handcrafted features, and deep learning can learn the featuresautomatically. We proposed a hybrid predictive model that combinesmany-objective radiomics (MO-radiomics) and 3-dimensional convolutional neuralnetwork (3D-CNN) through evidential reasoning (ER) approach. To build a morereliable model, we proposed a new many-objective radiomics model. Meanwhile, wedesigned a 3D-CNN that fully utilizes spatial contextual information. Finally,the outputs were fused through the ER approach. To study the predictability ofthe two modalities, three models were built for PET, CT, and PET&CT. Theresults showed that the model performed best when the two modalities werecombined. Moreover, we showed that the quantitative results obtained from thehybrid model were better than those obtained from MO-radiomics and 3D-CNN.

physics in medicine and biology/physics in medicine and biology

Combining Many-objective Radiomics and 3-dimensional Convolutional  Neural Network through Evidential Reasoning to Predict Lymph Node Metastasis  in Head and Neck Cancer