The Laryngoscope · 2025
Parotid Pleomorphic Adenoma and Apparent Diffusion Coefficient: A Novel Clinical Prediction Tool.
СтатьяНазвание и аннотация: Перевод в очереди
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Abend Audrey, Keir Graham, Krishnan Karthik, Kim Jessica, Strauss Sara, Kutler David
Аннотация
OBJECTIVES: Pleomorphic adenoma (PA) is the most common benign salivary gland tumor. MRI is a non-invasive diagnostic tool, but its sensitivity and specificity varies. Incorporating diffusion-weighted imaging (DWI) and quantitative apparent diffusion coefficient (ADC) assessment may enhance preoperative diagnosis. Our primary objective was to determine if including these variables in a clinical tool would enhance the diagnostic utility of MRI.
METHODS: A retrospective study was conducted at Weill Cornell Medical Center, including patients with parotid masses who underwent surgical resection and preoperative MRI (2006-2022). Patient and MRI characteristics, including ADC values, were analyzed to identify predictors of PA. A logistic regression model was developed and internally validated.
RESULTS: Among 157 patients, 86 (55%) had PA. MRI sensitivity and specificity for PA were 56% and 96%, respectively. Key predictors of PA included higher ADC values (p < 0.001), T2 hyperintense signal (p = 0.006), lobulated tumor contour (p = 0.021), and absence of dumbbell shape (p = 0.047). The final model achieved a sensitivity of 83% and specificity of 82%, with an area under the curve of 0.87. False negatives were significantly associated with lower ADC values, qualitative diffusion restriction, non-uniform tumor enhancement pattern, and intermediate/hypointense T2 signal.
CONCLUSION: Qualitative MRI features combined with quantitative ADC values offer a non-invasive, accurate approach for diagnosing PA. The proposed clinical calculator enhances preoperative planning, though external validation is warranted.
METHODS: A retrospective study was conducted at Weill Cornell Medical Center, including patients with parotid masses who underwent surgical resection and preoperative MRI (2006-2022). Patient and MRI characteristics, including ADC values, were analyzed to identify predictors of PA. A logistic regression model was developed and internally validated.
RESULTS: Among 157 patients, 86 (55%) had PA. MRI sensitivity and specificity for PA were 56% and 96%, respectively. Key predictors of PA included higher ADC values (p < 0.001), T2 hyperintense signal (p = 0.006), lobulated tumor contour (p = 0.021), and absence of dumbbell shape (p = 0.047). The final model achieved a sensitivity of 83% and specificity of 82%, with an area under the curve of 0.87. False negatives were significantly associated with lower ADC values, qualitative diffusion restriction, non-uniform tumor enhancement pattern, and intermediate/hypointense T2 signal.
CONCLUSION: Qualitative MRI features combined with quantitative ADC values offer a non-invasive, accurate approach for diagnosing PA. The proposed clinical calculator enhances preoperative planning, though external validation is warranted.