Accuracy assessment of combined diffusion weighed and dynamic gadolinium MR sequences in characterization of salivary gland tumors

Eissa, Lamya; Seif, S. Abou; Desooky, Salah El; Eid, Mohamed; Koraitim, Tarek

doi:10.1016/j.ejrnm.2015.11.011

Cited by 12 publications

(3 citation statements)

References 19 publications

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“…On the other hand, a type C curve was shown by the majority of the malignant tumours, achieving high sensitivity (87.5%), specificity (100%) and accuracy (93.75%) in differentiating malignant tumours from benign ones. The results were consistent with previous studies, all of which demonstrated that type A and type B curve are seen in benign tumours and type C curve in malignant tumours [9][10][11][12].Only 2 malignant tumours (one adenoid cystic carcinoma and another epithelial myoepithelial carcinoma) did not show type C TIC pattern. Adenoid cystic carcinoma demonstrated a type A curve, probably due to abundant my xoid component [13].…”

Section: Benign Versus Malignant Tumourssupporting

confidence: 92%

Differentiation of various salivary gland tumours using diffusion-weighted MRI and dynamic contrast-enhanced MRI

Kushwaha¹,

Kumar²,

Garg³

et al. 2023

Pol J Radiol

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Purpose To determine the role of functional magnetic resonance imaging techniques (diffusion-weighted magnetic resonance imaging [DW-MRI] and dynamic contrast-enhanced magnetic resonance imaging [DCE-MRI]) in the differentiation of various salivary gland tumours. Material and methods In this prospective study, we evaluated 32 patients with salivary gland tumours using functional MRI. Diffusion parameters (mean apparent diffusion coefficient [ADC], normalized ADC and homogeneity index [HI]),semiquantitative DCE parameters (time signal intensity curves [TICs]) and quantitative DCE parameters (K ep , K trans and V e ) were analysed. Diagnostic efficiencies of all these parameters were determined to differentiate benign and malignant tumours as well as to characterize 3 major subgroups of salivary gland tumours, namely pleomorphic adenoma, Warthin tumour, and malignant tumours. Results Mean ADC, normalized ADC and HI were insignificant in differentiating benign and malignant tumours but were significant in differentiating pleomorphic adenomas, Warthin tumours, and malignant tumours. Mean ADC was the best parameter in predicting both pleomorphic adenomas and Warthin tumours (AUC: 0.95 and 0.89, respectively). Amongst DCE parameters, only TIC pattern could differentiate between benign and malignant tumours, with an accuracy of 93.75% (AUC: 0.94). The quantitative perfusion parameters aided greatly in characterizing pleomorphic adenomas, Warthin tumours and malignant tumours. For predicting pleomorphic adenomas, the accuracy of K ep and K trans was 96.77% (AUC: 0.98) and 93.55% (AUC: 0.95), respectively and for predicting Warthin tumours, the accuracy of both K ep and K trans was 96.77% (AUC: 0.97). Conclusions DCE parameters (particularly TIC, K ep and K trans ) had higher accuracy in characterizing various tumour subgroups (pleomorphic adenomas, Warthin tumours, and malignant tumours) than DWI parameters. Hence, dynamic contrast-enhanced imaging adds immense value with only a minimum time penalty to the examination.

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Section: Benign Versus Malignant Tumourssupporting

confidence: 92%

Differentiation of various salivary gland tumours using diffusion-weighted MRI and dynamic contrast-enhanced MRI

Kushwaha¹,

Kumar²,

Garg³

et al. 2023

Pol J Radiol

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“…The characterization of PTs is important for preoperative treatment planning, as well as assessment of therapeutic responsiveness and prognosis (1). In the present study, LDA-based ML with selected clinical data and CT radiomics were applied to discriminate between benign (PA and WT) and malignant (PCa) PTs preoperatively, with satisfactory results.…”

Section: Discussionmentioning

confidence: 93%

Machine learning‑based multiparametric traditional multislice computed tomography radiomics for improving the discrimination of parotid neoplasms

Jin

et al. 2021

Mol Clin Oncol

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Characterization of parotid tumors is important for treatment planning and prognosis, and parotid tumor discrimination has recently been developed at the molecular level. The aim of the present study was to establish a machine learning (ML) predictive model based on multiparametric traditional multislice CT (MSCT) radiomic and clinical data analysis to improve the accuracy of differentiation among pleomorphic adenoma (PA), Warthin tumor (WT) and parotid carcinoma (PCa). A total of 345 patients (200 with WT, 91 with PA and 54 with PCa) with pathologically confirmed parotid tumors were retrospectively enrolled from five independent institutions between January 2010 and May 2019. A total of 273 patients recruited from institutions 1, 2 and 3 were randomly assigned to the training model; the independent validation set consisted of 72 patients treated at institutions 1, 4 and 5. Data were investigated using a linear discriminant analysis-based ML classifier. Feature selection and dimension reduction were conducted using reproducibility testing and a wrapper method. The diagnostic accuracy of the predictive model was compared with histopathological findings as reference results. This classifier achieved a satisfactory performance for the discrimination of PA, WT and PCa, with a total accuracy of 82.1% in the training cohort and 80.5% in the validation cohort. In conclusion, ML-based multiparametric traditional MSCT radiomics can improve the accuracy of differentiation among PA, WT and PCa. The findings of the present study should be validated by multicenter prospective studies using completely independent external data.

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“…DWI quantifies the diffusional mobility of water protons with apparent diffusion coefficient (ADC); DCE-MRI provides information related to the physiology of the microcirculation and vascularity. Eissa et al 8 reported the combined of DWI and dynamic contrast enhancement curves could provide valuable data in characterization of salivary glandular tumors. Nakamura et al 9 reported that the ADC of DWI may provide pre-operative tissue characterization of the salivary gland tumors.…”

Section: Introductionmentioning

confidence: 99%

The diagnostic value of combining conventional, diffusion-weighted imaging and dynamic contrast-enhanced MRI for salivary gland tumors

Zheng¹,

Li²,

Liu³

et al. 2018

BJR

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Accuracy assessment of combined diffusion weighed and dynamic gadolinium MR sequences in characterization of salivary gland tumors

Cited by 12 publications

References 19 publications

Differentiation of various salivary gland tumours using diffusion-weighted MRI and dynamic contrast-enhanced MRI

Differentiation of various salivary gland tumours using diffusion-weighted MRI and dynamic contrast-enhanced MRI

Machine learning‑based multiparametric traditional multislice computed tomography radiomics for improving the discrimination of parotid neoplasms

The diagnostic value of combining conventional, diffusion-weighted imaging and dynamic contrast-enhanced MRI for salivary gland tumors

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