A deep learning approach to segmentation of nasopharyngeal carcinoma using computed tomography

Bai, Xiaoyu; Hu, Yan; Gong, Guanzhong; Yin, Yong; Xia, Yong

doi:10.1016/j.bspc.2020.102246

Cited by 17 publications

(17 citation statements)

References 16 publications

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“…66.7% (n=40) only used imaging data such as magnetic resonance imaging, computed tomography or endoscopic images. 15 , 16 , 18 , 19 , 21–24 , 26–28 , 30 , 32 , 34 , 37–39 , 41–43 , 45–56 , 58–63 , 67 , 69 There were also four studies that included clinicopathological data as well as images for training models, 25 , 31 , 36 , 40 while three other studies developed models using images, clinicopathological data, and plasma Epstein-Barr virus (EBV) DNA. 29 , 33 , 35 Furthermore, 4 studies used treatment plans, 64–66 , 68 while proteins and microRNA expressions data were each extracted by one study.…”

Section: Resultsmentioning

confidence: 99%

“…The studies could be categorized into 4 domains, which were auto-contouring (n=21), 15 , 16 , 18 , 22 , 24 , 30–32 , 45–55 , 67 , 69 diagnosis (n=17), 10 , 15 , 16 , 23 , 26 , 27 , 49 , 52 , 54 , 56–63 prognosis (n=20) 12–14 , 17 , 19 , 25 , 28 , 29 , 33–44 and miscellaneous applications (n=7), 11 , 20 , 21 , 64–66 , 68 which included risk factor identification, image registration and radiotherapy planning ( Figure 2A ). Five studies examined both diagnosis and auto-contouring simultaneously.…”

Section: Resultsmentioning

confidence: 99%

“… 2. Only T3N1M0 patients were considered Bai et al (2021) 30 (China) NPC 60 (CT images) Deep learning (Auto-contouring) 1. Segmentation: ResNeXt U-net To use computed tomography for the segmentation of NPC 1.…”

Section: Methodsmentioning

confidence: 99%

“… ✓ ✓ ✓ ✓ ✓ ✓ Yes No Zhang et al (2020) 58 ✓ ✓ ✓ ✓ ✓ ✓ ✓ No No Zhang et al (2020) 36 ✓ ✓ ✓ ✓ ✓ ✓ ✓ No No Zhao et al (2020) 35 ✓ ✓ ✓ ? ✓ ✓ ✓ Yes No Zhong et al (2020) 29 ✓ ✓ ✓ ✓ ✓ ✓ ✓ No No Bai et al (2021) 30 ✓ ✓ ✓ ? ✓ ✓ ✓ Yes No Cai et al (2021) 31 ✓ ✓ ✘ ✓ ✓ ✓ ...…”

Section: Methodsmentioning

confidence: 99%

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Application of Artificial Intelligence for Nasopharyngeal Carcinoma Management – A Systematic Review

Ng¹,

But²,

Choi

et al. 2022

CMAR

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Introduction Nasopharyngeal carcinoma (NPC) is endemic to Eastern and South-Eastern Asia, and, in 2020, 77% of global cases were diagnosed in these regions. Apart from its distinct epidemiology, the natural behavior, treatment, and prognosis are different from other head and neck cancers. With the growing trend of artificial intelligence (AI), especially deep learning (DL), in head and neck cancer care, we sought to explore the unique clinical application and implementation direction of AI in the management of NPC. Methods The search protocol was performed to collect publications using AI, machine learning (ML) and DL in NPC management from PubMed, Scopus and Embase. The articles were filtered using inclusion and exclusion criteria, and the quality of the papers was assessed. Data were extracted from the finalized articles. Results A total of 78 articles were reviewed after removing duplicates and papers that did not meet the inclusion and exclusion criteria. After quality assessment, 60 papers were included in the current study. There were four main types of applications, which were auto-contouring, diagnosis, prognosis, and miscellaneous applications (especially on radiotherapy planning). The different forms of convolutional neural networks (CNNs) accounted for the majority of DL algorithms used, while the artificial neural network (ANN) was the most frequent ML model implemented. Conclusion There is an overall positive impact identified from AI implementation in the management of NPC. With improving AI algorithms, we envisage AI will be available as a routine application in a clinical setting soon.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Application of Artificial Intelligence for Nasopharyngeal Carcinoma Management – A Systematic Review

Ng¹,

But²,

Choi

et al. 2022

CMAR

View full text Add to dashboard Cite

show abstract

“…Bai [ 147 ] fine-tuned a pre-trained ResNeXt-50 U-Net, which uses the recall preserved loss to produce a rough segmentation of the gross tumour volume of NPC. Then, the well-trained ResNeXt-50 U-Net was applied to the fine-grained gross tumour volume boundary minute.…”

Section: Studies Based On DLmentioning

confidence: 99%

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

et al. 2021

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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.

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TG-Net: Combining transformer and GAN for nasopharyngeal carcinoma tumor segmentation based on total-body uEXPLORER PET/CT scanner

Huang

Tang

Chen

et al. 2022

Computers in Biology and Medicine

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A deep learning approach to segmentation of nasopharyngeal carcinoma using computed tomography

Cited by 17 publications

References 16 publications

Application of Artificial Intelligence for Nasopharyngeal Carcinoma Management – A Systematic Review

Application of Artificial Intelligence for Nasopharyngeal Carcinoma Management – A Systematic Review

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

TG-Net: Combining transformer and GAN for nasopharyngeal carcinoma tumor segmentation based on total-body uEXPLORER PET/CT scanner

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