Radiomics-based comparison of MRI and CT for differentiating pleomorphic adenomas and Warthin tumors of the parotid gland: a retrospective study

Liu, Yuebo; Zheng, Jiabao; Luo, Xiaoping; Wang, Yao; Meng, Fantai; Zhao, Juan; Guo, Chunhua; Yu, Lijiang; Zhu, Zhihui; Zhang, Tao

doi:10.1016/j.oooo.2021.01.014

Cited by 22 publications

(17 citation statements)

References 25 publications

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“…However, some of the included articles also specifically addressed the differential diagnosis between benign tumors, such as pleomorphic adenomas and Warthin tumors [ 16 , 20 , 22 , 24 , 26 ]. Indeed, although they are both benign tumors, the management and the therapy of these lesions are completely different; in pleomorphic adenomas, given the risk of transformation into carcinomas, parotidectomy is recommended [ 20 ].…”

Section: Discussionmentioning

confidence: 99%

The Role of Radiomics in Salivary Gland Imaging: A Systematic Review and Radiomics Quality Assessment

et al. 2022

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Background: Radiomics of salivary gland imaging can support clinical decisions in different clinical scenarios, such as tumors, radiation-induced xerostomia and sialadenitis. This review aims to evaluate the methodological quality of radiomics studies on salivary gland imaging. Material and Methods: A systematic search was performed, and the methodological quality was evaluated using the radiomics quality score (RQS). Subgroup analyses according to the first author’s professional role (medical or not medical), journal type (radiological journal or other) and the year of publication (2021 or before) were performed. The correlation of RQS with the number of patients was calculated. Results: Twenty-three articles were included (mean RQS 11.34 ± 3.68). Most studies well-documented the imaging protocol (87%), while neither prospective validations nor cost-effectiveness analyses were performed. None of the included studies provided open-source data. A statistically significant difference in RQS according to the year of publication was found (p = 0.009), with papers published in 2021 having slightly higher RQSs than older ones. No differences according to journal type or the first author’s professional role were demonstrated. A moderate relationship between the overall RQS and the number of patients was found. Conclusions: Radiomics application in salivary gland imaging is increasing. Although its current clinical applicability can be affected by the somewhat inadequate quality of the papers, a significant improvement in radiomics methodologies has been demonstrated in the last year.

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Section: Discussionmentioning

confidence: 99%

The Role of Radiomics in Salivary Gland Imaging: A Systematic Review and Radiomics Quality Assessment

et al. 2022

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“…The authors extracted 123 radiomics features from MRI and CT images of 626 PGTs, and found a diagnostic performance of rad-score and border index AUC for MRI of 0.911 and 0.716, respectively, whereas those of CT were 0.876 and 0.608, respectively. MRI and CT-based rad-score of both modalities showed no statistically significant differences but tumor border index and tumor margin examination properties on MRI had superior diagnostic performance over CT [41 ▪ ].…”

Section: Radiomics and Deep Learning Applications In Differential Dia...mentioning

confidence: 76%

Radiomics and deep learning approach to the differential diagnosis of parotid gland tumors

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et al. 2021

Current Opinion in Otolaryngology &Amp; Head &Amp; Neck Surgery

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Purpose of reviewAdvances in computer technology and growing expectations from computer-aided systems have led to the evolution of artificial intelligence into subsets, such as deep learning and radiomics, and the use of these systems is revolutionizing modern radiological diagnosis. In this review, artificial intelligence applications developed with radiomics and deep learning methods in the differential diagnosis of parotid gland tumors (PGTs) will be overviewed. Recent findingsThe development of artificial intelligence models has opened new scenarios owing to the possibility of assessing features of medical images that usually are not evaluated by physicians. Radiomics and deep learning models come to the forefront in computer-aided diagnosis of medical images, even though their applications in the differential diagnosis of PGTs have been limited because of the scarcity of data sets related to these rare neoplasms. Nevertheless, recent studies have shown that artificial intelligence tools can classify common PGTs with reasonable accuracy.

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“…Therefore, similar techniques might be extendible to MRI. Texture feature analysis of T 1 and T 2 -weighted images was used to distinguish parotid tumor PAs from WTs (26). But previous studies have shown that there is no significant difference in CT and MRI when distinguishing PAs and WTs (27).…”

Section: Discussionmentioning

confidence: 99%

Distinguishing Parotid Polymorphic Adenoma and Warthin Tumor Based on the CT Radiomics Nomogram: A Multicenter Study

Feng¹,

Wang²,

Cui³

et al. 2023

Academic Radiology

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Radiomics-based comparison of MRI and CT for differentiating pleomorphic adenomas and Warthin tumors of the parotid gland: a retrospective study

Cited by 22 publications

References 25 publications

The Role of Radiomics in Salivary Gland Imaging: A Systematic Review and Radiomics Quality Assessment

The Role of Radiomics in Salivary Gland Imaging: A Systematic Review and Radiomics Quality Assessment

Radiomics and deep learning approach to the differential diagnosis of parotid gland tumors

Distinguishing Parotid Polymorphic Adenoma and Warthin Tumor Based on the CT Radiomics Nomogram: A Multicenter Study

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