A Comprehensive Review on Radiomics and Deep Learning for Nasopharyngeal Carcinoma Imaging

Abstract

Nasopharyngeal carcinoma (NPC) is one of the most common malignant tumours of the head and neck, and improving the efficiency of its diagnosis and treatment strategies is an important goal. With the development of the combination of artificial intelligence (AI) technology and medical imaging in recent years, an increasing number of studies have been conducted on image analysis of NPC using AI tools, especially radiomics and artificial neural network methods. In this review, we present a comprehensive overview of NPC imaging research based on radiomics and deep learning. These studies depict a promising prospect for the diagnosis and treatment of NPC. The deficiencies of the current studies and the potential of radiomics and deep learning for NPC imaging are discussed. We conclude that future research should establish a large-scale labelled dataset of NPC images and that studies focused on screening for NPC using AI are necessary.

Machine Learning Based on MRI DWI Radiomics Features for Prognostic Prediction in Nasopharyngeal Carcinoma

“…Radiomics models based on MRI features in nasopharyngeal carcinoma (NPC) can predict the prognosis and therapeutic responses [28], but these models were constructed based on basic MR sequences (e.g., T2WI, T1WI, and CE-T1WI). Studies with a radiomics approach based on DWI images in nasopharyngeal carcinoma remain to be explored.…”

Prognostic value of PET/CT and MR-based baseline radiomics among patients with Nasopharyngeal Carcinoma

“…Our study had some limitations, predominantly in terms of methodology. This was a retrospective study with a moderate number of patients (124) (reported sample sizes in the literature range from 85-737 [3]), with mixed clinical stages of NPC (I-IV). Other prognostic molecular biomarkers, such as Haemoglobin, LDH, neutrophil-lymphocyte ration, c-Met, ERBB3 and MTDH were not available for inclusion in the study [21], as these were not routinely obtained at this time at our institute.…”

“…NPC affects less than 1 person per 100 000 in North America [2], however is endemic in Southern China, the Middle East and North Africa [2]. Although the prognosis of NPC is largely good, with 5 year survival rates reaching up to 80% [3], 20-30% of patients experience treatment failure from locoregional recurrence or distant metastasis [4].…”

“…MR assessment is performed due to superior soft tissue contrast resolution compared with CT, and 18 Fluoride-Fluorodeoxyglucose-Position Emission Tomography/Computed Tomography (PET/CT) is utilized to evaluate for both the presence of a primary lesion in cases of diagnostic uncertainty, and for the presence of local lymph node and distant metastatic disease. Increasing stage has been demonstrated to be associated with poorer prognosis [3,6], however if these patients are identi ed early, escalated therapy strategies can be employed.…”

Baseline MRI-based radiomics model assisted predicting disease progression in nasopharyngeal carcinoma patients with complete response after treatment

“…However, these early reports either mainly focused on advanced NPC [ 11 ], or only predicted local-regional recurrence without metastasis in some studies [ 12 , 13 ], or just focused on distant metastasis [ 5 ]. The same as radiomics, deep learning (DL) has also become one of the most important artificial intelligence (AI) tools, whose application in NPC have been gradually increasing since 2017 [ 14 ]. Jing [ 15 ] established an end-to-end multi-modality deep survival network (MDSN) to predict the risk of disease progression of NPC patients, but the best performance was a C-index of 0.651.…”