Usefulness of MRI-based radiomic features for distinguishing Warthin tumor from pleomorphic adenoma: performance assessment using T2-weighted and post-contrast T1-weighted MR images

Faggioni, Lorenzo; Gabelloni, Michela; Vietro, Fabrizio De; Frey, Jessica; Mendola, Vincenzo; Cavallero, Diletta; Borgheresi, Rita; Tumminello, Lorenzo; Shortrede, Jorge Eduardo; Morganti, Riccardo; Seccia, Veronica; Coppola, Francesca; Cioni, Dania; Neri, Emanuele

doi:10.1016/j.ejro.2022.100429

Cited by 6 publications

(3 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Subsequently, the full texts of the remaining 18 records were obtained and thoroughly reviewed. Four additional records were excluded for specific reasons: one study did not report AUC [30]; three studies reported an overlapping cohort [26,31,32]. Finally, 14 studies were included in the analysis.…”

Section: Literature Selectionmentioning

confidence: 99%

“…MRI has also demonstrated comparable efficacy to FNAC in characterizing SGTs [9][10][11]. More recently, the introduction of radiomics analysis to medical imaging has brought new approaches for quantitative imaging analysis [12], so it is not surprising that researchers have investigated the performance of radiomics analysis in characterizing SGTs on MRI [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]. Although these studies showed that radiomics analysis offers great potential for characterizing SGTs on MRI, variations in the proposed radiomics models limit its application in clinical practice.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Radiomics Analysis in Characterization of Salivary Gland Tumors on MRI: A Systematic Review

Mao,

Wong,

Zhang

et al. 2023

Cancers

View full text Add to dashboard Cite

Radiomics analysis can potentially characterize salivary gland tumors (SGTs) on magnetic resonance imaging (MRI). The procedures for radiomics analysis were various, and no consistent performances were reported. This review evaluated the methodologies and performances of studies using radiomics analysis to characterize SGTs on MRI. We systematically reviewed studies published until July 2023, which employed radiomics analysis to characterize SGTs on MRI. In total, 14 of 98 studies were eligible. Each study examined 23–334 benign and 8–56 malignant SGTs. Least absolute shrinkage and selection operator (LASSO) was the most common feature selection method (in eight studies). Eleven studies confirmed the stability of selected features using cross-validation or bootstrap. Nine classifiers were used to build models that achieved area under the curves (AUCs) of 0.74 to 1.00 for characterizing benign and malignant SGTs and 0.80 to 0.96 for characterizing pleomorphic adenomas and Warthin’s tumors. Performances were validated using cross-validation, internal, and external datasets in four, six, and two studies, respectively. No single feature consistently appeared in the final models across the studies. No standardized procedure was used for radiomics analysis in characterizing SGTs on MRIs, and various models were proposed. The need for a standard procedure for radiomics analysis is emphasized.

show abstract

Section: Literature Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Radiomics Analysis in Characterization of Salivary Gland Tumors on MRI: A Systematic Review

Mao,

Wong,

Zhang

et al. 2023

Cancers

View full text Add to dashboard Cite

show abstract

“…In recent years, radiomics has been widely used for preoperative diagnosis of parotid tumors ( 16 – 18 ). Some previous studies have tried to discriminate benign and malignant parotid tumors using radiomics ( 19 , 20 ), but only a few of them have analyzed the differentiation of PA from WT ( 21 , 22 ). However, there is no research focused on differentiating misdiagnosed or ambiguous PA and WT using radiomics.…”

Section: Introductionmentioning

confidence: 99%

Different MRI-based radiomics models for differentiating misdiagnosed or ambiguous pleomorphic adenoma and Warthin tumor of the parotid gland: a multicenter study

Yang,

Bi,

Jin

et al. 2024

Front. Oncol.

View full text Add to dashboard Cite

PurposeTo evaluate the effectiveness of MRI-based radiomics models in distinguishing between Warthin tumors (WT) and misdiagnosed or ambiguous pleomorphic adenoma (PA).MethodsData of patients with PA and WT from two centers were collected. MR images were used to extract radiomic features. The optimal radiomics model was found by running nine machine learning algorithms after feature reduction and selection. To create a clinical model, univariate logistic regression (LR) analysis and multivariate LR were used. The independent clinical predictors and radiomics were combined to create a nomogram. Two integrated models were constructed by the ensemble and stacking algorithms respectively based on the clinical model and the optimal radiomics model. The models’ performance was evaluated using the area under the curve (AUC).ResultsThere were 149 patients included in all. Gender, age, and smoking of patients were independent clinical predictors. With the greatest average AUC (0.896) and accuracy (0.839) in validation groups, the LR model was the optimal radiomics model. In the average validation group, the radiomics model based on LR did not have a higher AUC (0.795) than the clinical model (AUC = 0.909). The nomogram (AUC = 0.953) outperformed the radiomics model in terms of discrimination performance. The nomogram in the average validation group had a highest AUC than the stacking model (0.914) or ensemble model (0.798).ConclusionMisdiagnosed or ambiguous PA and WT can be non-invasively distinguished using MRI-based radiomics models. The nomogram exhibited excellent and stable diagnostic performance. In daily work, it is necessary to combine with clinical parameters for distinguishing between PA and WT.

show abstract

Preoperative prediction of vasculogenic mimicry in lung adenocarcinoma using a CT radiomics model

Li,

Yang,

et al. 2024

Clinical Radiology

View full text Add to dashboard Cite

Usefulness of MRI-based radiomic features for distinguishing Warthin tumor from pleomorphic adenoma: performance assessment using T2-weighted and post-contrast T1-weighted MR images

Cited by 6 publications

References 29 publications

Radiomics Analysis in Characterization of Salivary Gland Tumors on MRI: A Systematic Review

Radiomics Analysis in Characterization of Salivary Gland Tumors on MRI: A Systematic Review

Different MRI-based radiomics models for differentiating misdiagnosed or ambiguous pleomorphic adenoma and Warthin tumor of the parotid gland: a multicenter study

Preoperative prediction of vasculogenic mimicry in lung adenocarcinoma using a CT radiomics model

Contact Info

Product

Resources

About