Texture analysis in the characterization of parotid salivary gland lesions: A study on MR diffusion weighted imaging. Issue 136 (March 2021)

• Record Type: Journal Article
• Authors: Nardi, Cosimo; Tomei, Maddalena; Pietragalla, Michele; Calistri, Linda; Landini, Nicholas; Bonomo, Pierluigi; Mannelli, Giuditta; Mungai, Francesco; Bonasera, Luigi; Colagrande, Stefano
• Is Part Of: European journal of radiology. Issue 136(2021)
• Publication Date: 2021-03
• Subjects: MRI magnetic resonance imaging -- DWI diffusion weighted imaging -- ADC apparent diffusion coefficient -- GLRLM grey-level run-length matrix -- LZE long zone emphasis -- GLZLM grey-level zone length matrix -- LRE long run emphasis -- LRLGE long run low grey-level emphasis -- ICC intraclass correlation coefficient -- ROC receiver operating characteristic -- PPV positive predictive value -- NPV negative predictive value -- AUC area under the curve; Diffusion magnetic resonance imaging -- Parotid gland -- Lymphoma -- Head and neck neoplasms; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2021.109529
• Languages: English
• ISSNs: 0720-048X

Abstract

Highlights: Overlaps of morphological findings, ADC, and types of time/intensity curve on MRI are found among different parotid lesions. Texture analysis provides a quantitative assessment of tumor heterogeneity by adding precise structural information. LZE and LRE represent the texture parameters that enable the differentiation between malignant and benign lesions. Abstract: Background and Purpose: Parotid lesions show overlaps of morphological findings, apparent diffusion coefficient (ADC) values and types of time/intensity curve. This research aimed to evaluate the role of diffusion weighted imaging texture analysis in differentiating between benign and malignant parotid lesions and in characterizing pleomorphic adenoma (PA), Warthin tumor (WT), epithelial malignancy (EM), and lymphoma (LY). Methods: Texture analysis of 54 parotid lesions (19 PA, 14 WT, 14 EM, and 7 LY) was performed on ADC map images. An ANOVA test was used to estimate both the difference between benign and malignant lesions and the texture feature differences among PA, WT, EM, and LY. A P-value≤0.01 was considered to be statistically significant. A cut-off value defined by ROC curve analysis was found for each statistically significant texture parameter. The diagnostic accuracy was obtained for each texture parameter with AUC ≥ 0.5. The agreement between each texture parameter and histology was calculated using the Cohen's kappa coefficient. Results: The mean kappa values were 0.61, 0.34, 0.26, 0.17, and 0.48 for LY, EM, WT, PA, and benign vs. malignant lesions respectively. Long zone emphasis cut-off values >1.870 indicated EM with an accuracy of 81 % and values >2.630 revealed LY with an accuracy of 93 %. Long run emphasis values >1.050 and >1.070 indicated EM and LY with a diagnostic accuracy of 79% and 93% respectively. Conclusions: Long zone emphasis and long run emphasis texture parameters allowed the identification of LY and the differentiation between benign and malignant lesions. WT and PA were not accurately recognized.