Improving diagnosing performance for malignant parotid gland tumors using machine learning with multifeatures based on diffusion‐weighted magnetic resonance imaging

2021 ◽  
Author(s):  
Chun‐Jung Juan ◽  
Teng‐Yi Huang ◽  
Yi‐Jui Liu ◽  
Wu‐Chung Shen ◽  
Chih‐Wei Wang ◽  
...  
2015 ◽  
Vol 32 (1) ◽  
pp. 22-32 ◽  
Author(s):  
Yasemin Karaman ◽  
Anıl Özgür ◽  
Demir Apaydın ◽  
Cengiz Özcan ◽  
Rabia Arpacı ◽  
...  

QJM ◽  
2020 ◽  
Vol 113 (Supplement_1) ◽  
Author(s):  
S M Elgaafary ◽  
E A Darwish ◽  
D Y Hussein

Abstract Introduction The parotid gland is the largest salivary gland and parotid cancer accounts for 5% of the incidence rate of all head and neck tumors and 80% the salivary gland tumors. Benign tumors account for 80–85% and malignant tumors account for 15–20%. About 25% of untreated parotid pleomorphic adenoma shows malignant degeneration after a long history of disease, especially in multiple pleomorphic adenoma, but Warthin’s tumors are rarely malignant, with a rate of less than 1%. Objectives This review aim to assess the role of magnetic resonance imaging and diffusion weighted MR Imaging in diagnosis and characterization of parotid gland tumors. Data Sources Medline databases (PubMed, Medscape, Science Direct. EMF-Portal) and all materials available in the Internet till 2018. Study Selection This search presented 70 articles. The articles studied the role of MRI and diffusion weighted image in parotid tumors and to purify the most recent studies in this field. Data Extraction If the studies did not fulfill the inclusion criteria, they were excluded. Study quality assessment included whether ethical approval was gained, eligibility criteria specified, appropriate controls, and adequate information and defined assessment measures. Data Synthesis Comparisons were made by structured review with the results tabulated. Conclusion The combination of conventional MR imaging with advanced multiparametric MR assessment can enable accurate characterization of parotid gland tumors non –invasively.


2019 ◽  
Vol 84 ◽  
pp. 142-146 ◽  
Author(s):  
Ahmed Razek ◽  
El-hadidy Mohamed El-Hadidy ◽  
Mohamed El-Said Moawad ◽  
Nader El-Metwaly ◽  
Amr Abd El-hamid El-Said

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