Apparent Diffusion Coefficient (ADC) Histogram Analysis in Parotid Gland Tumors: Evaluating a Novel Approach for Differentiation between Benign and Malignant Parotid Lesions Based on Full Histogram Distributions

Abstract

The aim of this study was to assess the diagnostic value of ADC distribution curves for differentiation between benign and malignant parotid gland tumors and to compare with mean ADC values. 73 patients with parotid gland tumors underwent head-and-neck MRI on a 1.5 Tesla scanner prior to surgery and histograms of ADC values were extracted. Histopathological results served as a reference standard for further analysis. ADC histograms were evaluated by comparing their similarity to a reference distribution using Chi2-test-statistics. The assumed reference distribution for benign and malignant parotid gland lesions was calculated after pooling the entire ADC data. In addition, mean ADC values were determined. For both methods, we calculated and compared the sensitivity and specificity between benign and malignant parotid gland tumors and three subgroups (pleomorphic adenoma, Warthin tumor, and malignant lesions), respectively. Moreover, we performed cross-validation (CV) techniques to estimate the predictive performance between ADC distributions and mean values. Histopathological results revealed 30 pleomorphic adenomas, 22 Warthin tumors, and 21 malignant tumors. ADC histogram distribution yielded a better specificity for detection of benign parotid gland lesions (ADChistogram: 75.0% vs. ADCmean: 71.2%), but mean ADC values provided a higher sensitivity (ADCmean: 71.4% vs. ADChistogram: 61.9%). The discrepancies are most pronounced in the differentiation between malignant and Warthin tumors (sensitivity ADCmean: 76.2% vs. ADChistogram: 61.9%; specificity ADChistogram: 81.8% vs. ADCmean: 68.2%). Using CV techniques, ADC distribution revealed consistently better accuracy to differentiate benign from malignant lesions (“leave-one-out CV” accuracy ADChistogram: 71.2% vs. ADCmean: 67.1%). ADC histogram analysis using full distribution curves is a promising new approach for differentiation between primary benign and malignant parotid gland tumors, especially with respect to the advantage in predictive performance based on CV techniques.

Construction and Application of a Nomogram for Predicting Benign and Malignant Parotid Tumors

“…[10][11][12] Constructing an evidence-based predictive mathematical model for disease diagnosis represents a new trend. [6][7][8] Some models for predicting benign and malignant parotid tumors have been described [6][7][8]13 ; however, most of the previous studies did not provide an easy-to-use and effective nomogram other than building a predictive mathematical model. In the present study, we combined the MR image features of parotid tumors and the basic clinical data of patients.…”

Histograms of computed tomography values in differential diagnosis of benign and malignant osteogenic lesions

“…Many studies to date have used histogram analysis to distinguish between benign and malignant lesions (8)(9)(10)(11), but the use of histogram analysis to distinguish BOLs and MOLs has not been reported in the literature. This study showed significant differences in most of the above histogram parameters used to distinguish BOLs and MOLs, including the mean value, coefficient of variation, skewness, and 1st, 5th, 10th, 25th, 50th, 75th, 90th, 95th, and 99th percentiles.…”

“…Thus, we can quantitatively analyze lesion features at the pixel level and obtain more objective and accurate information on subtle differences. As an effective method of image data analysis, histogram analysis is widely used in the differentiation, classification, and qualitative diagnosis of benign and malignant lesions (8)(9)(10)(11)(12)(13)(14)(15). The aim of the present study was to evaluate the usefulness of CT histogram analysis for differentiating benign osteoblastic lesions (BOLs) and malignant osteoblastic lesions (MOLs).…”