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

TL;DR

This paper presents a hybrid model combining advanced radiomics and deep learning techniques, fused via evidential reasoning, to improve the prediction of lymph node metastasis in head and neck cancer using PET and CT imaging.

Contribution

It introduces a novel hybrid model that integrates many-objective radiomics and 3D-CNN with evidential reasoning, enhancing predictive accuracy for lymph node metastasis.

Findings

Hybrid model outperforms individual radiomics and deep learning models.

Combining PET and CT data yields the best prediction results.

Quantitative results demonstrate improved reliability of the hybrid approach.

Abstract

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.