  Lymph node metastasis (LNM) is a significant prognostic factor in patients
with head and neck cancer, and the ability to predict it accurately is
essential for treatment optimization. PET and CT imaging are routinely used for
LNM identification. However, uncertainties of LNM always exist especially for
small size or reactive nodes. Radiomics and deep learning are the two preferred
imaging-based strategies for node malignancy prediction. Radiomics models are
built based on handcrafted features, and deep learning can learn the features
automatically. We proposed a hybrid predictive model that combines
many-objective radiomics (MO-radiomics) and 3-dimensional convolutional neural
network (3D-CNN) through evidential reasoning (ER) approach. To build a more
reliable model, we proposed a new many-objective radiomics model. Meanwhile, we
designed a 3D-CNN that fully utilizes spatial contextual information. Finally,
the outputs were fused through the ER approach. To study the predictability of
the two modalities, three models were built for PET, CT, and PET&CT. The
results showed that the model performed best when the two modalities were
combined. Moreover, we showed that the quantitative results obtained from the
hybrid model were better than those obtained from MO-radiomics and 3D-CNN.

Medical Physics