Prediction of nasopharyngeal carcinoma recurrence by neuro-fuzzy techniques

Kumdee, Orrawan; Bhongmakapat, Thongchai; Ritthipravat, Panrasee

doi:10.1016/j.fss.2012.03.004

Cited by 11 publications

(15 citation statements)

References 21 publications

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“…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%

“… 10 , 12 , 15 , 26–28 , 35 , 43 , 44 , 49 , 54 , 56 , 60–63 Sensitivity was the most common studied parameter for diagnostic performance: 15 out of 23 papers. 10 , 15 , 16 , 23 , 26 , 27 , 49 , 52 , 54 , 56 , 59–63 The specificity was only reported for prognosis (n=7) 12 , 14 , 28 , 34 , 39 , 40 , 43 and diagnosis (n=15). 10 , 15 , 16 , 23 , 26 , 27 , 49 , 52 , 54 , 56 , 59–63 In addition, the DSC (n=20) 15 , 18 , 22 , 24 , 30–32 , 45–53 , 55 , 65 , 67 , 69 and ASSD (n=10) 18 , 22 , 24 , 31 , 32 , 45 , 46 , 48 , 51 , 69 were the primary metrics reported in studies on auto-contouring ( Figure 2B ).…”

Section: Resultsmentioning

confidence: 99%

“…ANN is the backbone of DL, as DL algorithms are ANNs with multiple (2 or more) hidden layers. In the development of AI applications for NPC, 80% of the studied articles incorporated either ANN or DL technique in their models 12 , 13 , 15–19 , 21–26 , 28–34 , 36 , 38 , 39 , 42–56 , 60–69 because neural networks are generally better for image recognition. However, one study cautioned that ANNs were not necessarily better than other ML models in NPC identification.…”

Section: Discussionmentioning

confidence: 99%

“…Empirical performance - Accuracy: 0.654 - Euclidean distance a The proposed model had a better performance in recognizing risk factors associated with NPC than other algorithms. The proposed techniques can integrate experts’ knowledge and information extracted from data to analyze epidemiologic data N/A Kumdee, Bhongmakapat, and Ritthipravat (2012) 12 (Thailand) NPC 1257 (Clinicopathological and serology data) Machine learning (Prognosis) 1. Prediction: Generalized neural network-type SIRM (G-NN-SIRM) To predict NPC recurrence 1.…”

Section: Methodsmentioning

confidence: 99%

“… ✓ ✓ ✓ ✓ ✓ ✓ Yes No Aussem et al (2012) 11 ? ✓ ✓ ✓ ✓ ✓ ✓ Yes No Kumdee, Bhongmakapat and Ritthipravat (2012) 12 ✓ ✓ ✓ ✓ ✓ ✓ ✓ No No Ritthipravat, Kumdee, and Bhongmakapat (2013) 13 ✓ ✓ ✓ ✓ ✓ ✓ ✓ No No Zhu and Kan (2014) 44 ✓ ✓ ✓ ✓ ✓ ✓ ✓ No No Jiang et al (2016) 14 ? ✓ ✓ ✓ ✓ ✓ ✓ Yes No Liu et al (2016) 43 ✓ ✓ ✓ ?…”

Section: Methodsmentioning

confidence: 99%

See 4 more Smart Citations

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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