Development and validation of an MRI-based radiomics nomogram for distinguishing Warthin’s tumour from pleomorphic adenomas of the parotid gland

Zheng, Ying-Mei; Chen, Jiao; Xu, Qi; Zhao, Wen-hui; Wang, Xinfeng; Yuan, Ming-Gang; Liu, Zong-Jing; Wu, Zhen; Dong, Cheng

doi:10.1259/dmfr.20210023

Cited by 21 publications

(20 citation statements)

References 38 publications

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“…For differentiation of PA from WT, our study found that the radiomics nomogram incorporating radiomics features, age, and sex exhibited slightly better results than the radiomics nomogram used by Zheng et al (36). Radiomics feature-based ADC also plays an important role in the final radiomics model.…”

Section: Discussionmentioning

confidence: 60%

Differentiation of Benign From Malignant Parotid Gland Tumors Using Conventional MRI Based on Radiomics Nomogram

Gao

Song

et al. 2022

Front. Oncol.

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ObjectivesWe aimed to develop and validate radiomic nomograms to allow preoperative differentiation between benign- and malignant parotid gland tumors (BPGT and MPGT, respectively), as well as between pleomorphic adenomas (PAs) and Warthin tumors (WTs).Materials and MethodsThis retrospective study enrolled 183 parotid gland tumors (68 PAs, 62 WTs, and 53 MPGTs) and divided them into training (n = 128) and testing (n = 55) cohorts. In total, 2553 radiomics features were extracted from fat-saturated T2-weighted images, apparent diffusion coefficient maps, and contrast-enhanced T1-weighted images to construct single-, double-, and multi-sequence combined radiomics models, respectively. The radiomics score (Rad-score) was calculated using the best radiomics model and clinical features to develop the radiomics nomogram. The receiver operating characteristic curve and area under the curve (AUC) were used to assess these models, and their performances were compared using DeLong’s test. Calibration curves and decision curve analysis were used to assess the clinical usefulness of these models.ResultsThe multi-sequence combined radiomics model exhibited better differentiation performance (BPGT vs. MPGT, AUC=0.863; PA vs. MPGT, AUC=0.929; WT vs. MPGT, AUC=0.825; PA vs. WT, AUC=0.927) than the single- and double sequence radiomics models. The nomogram based on the multi-sequence combined radiomics model and clinical features attained an improved classification performance (BPGT vs. MPGT, AUC=0.907; PA vs. MPGT, AUC=0.961; WT vs. MPGT, AUC=0.879; PA vs. WT, AUC=0.967).ConclusionsRadiomics nomogram yielded excellent diagnostic performance in differentiating BPGT from MPGT, PA from MPGT, and PA from WT.

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Section: Discussionmentioning

confidence: 60%

Differentiation of Benign From Malignant Parotid Gland Tumors Using Conventional MRI Based on Radiomics Nomogram

Gao

Song

et al. 2022

Front. Oncol.

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“…It can also contribute to predict the prognosis in some instances [15][16][17]. Several recent articles have explored the value of radiomics, clinical, and their combined models for the classification of PA, WT, and malignancy [18][19][20][21][22]. However, the selection of clinical factors is not uniform.…”

Section: Introductionmentioning

confidence: 99%

Value of T2-weighted-based radiomics model in distinguishing Warthin tumor from pleomorphic adenoma of the parotid

Guo

Feng

et al. 2022

Eur Radiol

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Objectives The differentiation of Warthin tumor and pleomorphic adenoma before treatment is crucial for clinical strategies. The aim of this study was to develop and test a T2-weighted-based radiomics model for differentiating pleomorphic adenoma from Warthin tumor of the parotid gland. Methods A total of 117 patients, including 61 cases of Warthin tumor and 56 cases of pleomorphic adenoma, were retrospectively enrolled from two centers between January 2010 and June 2022. The training set included 82 cases, and the validation set included 35 cases. From T2-weighted images, 971 radiomics features were extracted. Seven radiomics features remained after a two-step selection process. We used the seven radiomics features and clinical factors through multivariable logistic regression to build radiomics and clinical models, respectively. A radiomics–clinical model was also built that combined the independent clinical predictors with the radiomics features. Through ROC curves, the three models were evaluated and compared. Results In the radiomics model, AUCs were 0.826 and 0.796 in training and validation sets, respectively. In the clinical model, the AUCs were 0.923 and 0.926 in the training and validation sets, respectively. Decision curve analysis revealed that the radiomics–clinical model had the best diagnostic performance for distinguishing Warthin tumor from pleomorphic adenoma of the parotid gland (AUC = 0.962 and 0.934 for the training and validation sets, respectively). Conclusion The radiomics–clinical model performed well in differentiating pleomorphic adenoma from Warthin tumor of the parotid gland. Key points • The clinical model outperformed the radiomics model in distinguishing pleomorphic adenoma from Warthin tumor of the parotid gland. • The radiomics features extracted from T2-weighted images could help differentiate pleomorphic adenoma from Warthin tumor of the parotid gland. • The radiomics–clinical model was superior to the radiomics and the clinical models for differentiating pleomorphic adenoma from Warthin tumor of the parotid gland.

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“…Radiomics features were extracted from axial T1W and T2W sequences. The authors found that the radiomics signature had a notable predictive value in differentiating parotid Warthin tumors from PMA, with an AUC of 0.953 and 0.918 for the training and test sets, respectively [36 ▪ ]. In another study, Zheng et al [37 ▪ ] applied clinical factors and radiomics signatures to logistic regression analysis for differential diagnosis of 60 benign and 55 malignant PGTs by T1W and T2W MRI sequences.…”

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

confidence: 99%

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

Gündüz

Alçın

Kızılay

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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Development and validation of an MRI-based radiomics nomogram for distinguishing Warthin’s tumour from pleomorphic adenomas of the parotid gland

Cited by 21 publications

References 38 publications

Differentiation of Benign From Malignant Parotid Gland Tumors Using Conventional MRI Based on Radiomics Nomogram

Differentiation of Benign From Malignant Parotid Gland Tumors Using Conventional MRI Based on Radiomics Nomogram

Value of T2-weighted-based radiomics model in distinguishing Warthin tumor from pleomorphic adenoma of the parotid

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

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