Differential Diagnosis of Oral Salivary Gland Carcinoma and Squamous Cell Carcinoma Using Quantitative Dynamic Contrast-Enhanced MRI

<b>Background/Objectives:</b> Preoperative differentiation between oral squamous cell carcinoma (SCC) and minor salivary gland carcinoma (SGC) remains clinically challenging due to overlapping imaging characteristics. This study aimed to develop a diagnostic model based on quantitative dynamic contrast-enhanced MRI (qDCE-MRI) parameters to distinguish SCC from SGC prior to surgery. <b>Methods:</b> Patients with histopathologic confirmed SCC or minor SGC who underwent preoperative 3.0T qDCE-MRI were recruited. Clinical characteristics and pharmacokinetic parameters, including volume transfer constant (K<sup>trans</sup>), reverse reflux rate constant (K<sub>ep</sub>), volume fraction of extravascular extracellular space (V<sub>e</sub>), plasma volume fraction (V<sub>p</sub>), time to peak (TTP), maximum concentration (MAXConc), maximal slope (MAXSlope), and area under the concentration-time curve (AUCt), along with the apparent diffusion coefficient (ADC), were extracted. Univariate and multivariable logistic regression analyses were performed to identify independent discriminators. Diagnostic performance was assessed using receiver operating characteristic analysis, and model comparisons were conducted with the DeLong test. Interobserver agreement was evaluated using intraclass correlation coefficients (ICC). <b>Results:</b> All qDCE-MRI parameters demonstrated excellent interobserver agreement (ICC range, 0.82-0.94). Multivariable analysis identified K<sub>ep</sub> (OR = 2620.172, <i>p</i> = 0.001), maximal slope (OR = 1.715, <i>p</i> = 0.024), and tumor location (OR = 5.561, <i>p</i> = 0.027) as independent predictors. The qDCE-MRI model achieved superior diagnostic performance compared with the clinical model (AUC: 0.945 vs. 0.747; <i>p</i> = 0.012). <b>Conclusions:</b> A qDCE-MRI-based model incorporating K<sub>ep</sub> and MAXSlope was shown to provide excellent accuracy for preoperative differentiation between oral SCC and minor SGC.

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Journal
Journal of Clinical Medicine
Published
2026-01-20
DOI
https://doi.org/10.3390/jcm15020822
Primary Topic
MRI in cancer diagnosis
Type
article
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article

Differential Diagnosis of Oral Salivary Gland Carcinoma and Squamous Cell Carcinoma Using Quantitative Dynamic Contrast-Enhanced MRI

Yan Zeng, Guoxiong Lu, Xi Wu, Yang Li et al.
Journal of Clinical Medicine
MRI in cancer diagnosis
article

Differential Diagnosis of Oral Salivary Gland Carcinoma and Squamous Cell Carcinoma Using Quantitative Dynamic Contrast-Enhanced MRI

Yan Zeng, Guoxiong Lu, Xi Wu, Yang Li, Jiale Zeng, Siya Shi, Kunjie Zeng, Y. Jiang, Zhaoqi Lai, Yun Su, Xinyin Chen
article en

Abstract

<b>Background/Objectives:</b> Preoperative differentiation between oral squamous cell carcinoma (SCC) and minor salivary gland carcinoma (SGC) remains clinically challenging due to overlapping imaging characteristics. This study aimed to develop a diagnostic model based on quantitative dynamic contrast-enhanced MRI (qDCE-MRI) parameters to distinguish SCC from SGC prior to surgery. <b>Methods:</b> Patients with histopathologic confirmed SCC or minor SGC who underwent preoperative 3.0T qDCE-MRI were recruited. Clinical characteristics and pharmacokinetic parameters, including volume transfer constant (K<sup>trans</sup>), reverse reflux rate constant (K<sub>ep</sub>), volume fraction of extravascular extracellular space (V<sub>e</sub>), plasma volume fraction (V<sub>p</sub>), time to peak (TTP), maximum concentration (MAXConc), maximal slope (MAXSlope), and area under the concentration-time curve (AUCt), along with the apparent diffusion coefficient (ADC), were extracted. Univariate and multivariable logistic regression analyses were performed to identify independent discriminators. Diagnostic performance was assessed using receiver operating characteristic analysis, and model comparisons were conducted with the DeLong test. Interobserver agreement was evaluated using intraclass correlation coefficients (ICC). <b>Results:</b> All qDCE-MRI parameters demonstrated excellent interobserver agreement (ICC range, 0.82-0.94). Multivariable analysis identified K<sub>ep</sub> (OR = 2620.172, <i>p</i> = 0.001), maximal slope (OR = 1.715, <i>p</i> = 0.024), and tumor location (OR = 5.561, <i>p</i> = 0.027) as independent predictors. The qDCE-MRI model achieved superior diagnostic performance compared with the clinical model (AUC: 0.945 vs. 0.747; <i>p</i> = 0.012). <b>Conclusions:</b> A qDCE-MRI-based model incorporating K<sub>ep</sub> and MAXSlope was shown to provide excellent accuracy for preoperative differentiation between oral SCC and minor SGC.

Journal of Clinical MedicineVol. 15(2)
Sun Yat-sen University (CN), Sun Yat-sen Memorial Hospital (CN), The First Affiliated Hospital, Sun Yat-sen University (CN)
Reduced inequalities
Openalex Percentile: Top 84%
MRI in cancer diagnosis
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