A Whole-Tumor Histogram Analysis of Apparent Diffusion Coefficient Maps for Differentiating Thymic Carcinoma from Lymphoma.
10.3348/kjr.2018.19.2.358
- Author:
Wei ZHANG
1
;
Yue ZHOU
;
Xiao Quan XU
;
Ling Yan KONG
;
Hai XU
;
Tong Fu YU
;
Hai Bin SHI
;
Qing FENG
Author Information
1. Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210000, China.
- Publication Type:Original Article
- Keywords:
Histogram analysis;
Diffusion weighted imaging;
Apparent diffusion coefficient;
Thymic carcinoma;
Lymphoma;
Mediastinal mass
- MeSH:
Area Under Curve;
Diffusion*;
Humans;
Lymphoma*;
Retrospective Studies;
ROC Curve;
Sensitivity and Specificity;
Thymoma*
- From:Korean Journal of Radiology
2018;19(2):358-365
- CountryRepublic of Korea
- Language:English
-
Abstract:
OBJECTIVE: To assess the performance of a whole-tumor histogram analysis of apparent diffusion coefficient (ADC) maps in differentiating thymic carcinoma from lymphoma, and compare it with that of a commonly used hot-spot region-of-interest (ROI)-based ADC measurement. MATERIALS AND METHODS: Diffusion weighted imaging data of 15 patients with thymic carcinoma and 13 patients with lymphoma were retrospectively collected and processed with a mono-exponential model. ADC measurements were performed by using a histogram-based and hot-spot-ROI-based approach. In the histogram-based approach, the following parameters were generated: mean ADC (ADCmean), median ADC (ADCmedian), 10th and 90th percentile of ADC (ADC10 and ADC90), kurtosis, and skewness. The difference in ADCs between thymic carcinoma and lymphoma was compared using a t test. Receiver operating characteristic analyses were conducted to determine and compare the differentiating performance of ADCs. RESULTS: Lymphoma demonstrated significantly lower ADCmean, ADCmedian, ADC10, ADC90, and hot-spot-ROI-based mean ADC than those found in thymic carcinoma (all p values < 0.05). There were no differences found in the kurtosis (p = 0.412) and skewness (p = 0.273). The ADC10 demonstrated optimal differentiating performance (cut-off value, 0.403 × 10−3 mm2/s; area under the receiver operating characteristic curve [AUC], 0.977; sensitivity, 92.3%; specificity, 93.3%), followed by the ADCmean, ADCmedian, ADC90, and hot-spot-ROI-based mean ADC. The AUC of ADC10 was significantly higher than that of the hot spot ROI based ADC (0.977 vs. 0.797, p = 0.036). CONCLUSION: Compared with the commonly used hot spot ROI based ADC measurement, a histogram analysis of ADC maps can improve the differentiating performance between thymic carcinoma and lymphoma.
