Korean Journal of Radiology

The Korean Society of Radiology (대한영상의학회)
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A Whole-Tumor Histogram Analysis of Apparent Diffusion Coefficient Maps for Differentiating Thymic Carcinoma from Lymphoma

  • Zhang, Wei (Department of Radiology, The First Affiliated Hospital of Nanjing Medical University) ;
  • Zhou, Yue (Department of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University) ;
  • Xu, Xiao-Quan (Department of Radiology, The First Affiliated Hospital of Nanjing Medical University) ;
  • Kong, Ling-Yan (Department of Radiology, The First Affiliated Hospital of Nanjing Medical University) ;
  • Xu, Hai (Department of Radiology, The First Affiliated Hospital of Nanjing Medical University) ;
  • Yu, Tong-Fu (Department of Radiology, The First Affiliated Hospital of Nanjing Medical University) ;
  • Shi, Hai-Bin (Department of Radiology, The First Affiliated Hospital of Nanjing Medical University) ;
  • Feng, Qing (Department of Nutrition and Food Hygiene, School of Public Health, Nanjing Medical University)
  • Received : 2017.05.09
  • Accepted : 2017.08.08
  • Published : 2018.04.01
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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 ($ADC_{mean}$), median ADC ($ADC_{median}$), 10th and 90th percentile of ADC ($ADC_{10}$ and $ADC_{90}$), 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 $ADC_{mean}$, $ADC_{median}$, $ADC_{10}$, $ADC_{90}$, 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 $ADC_{10}$ demonstrated optimal differentiating performance (cut-off value, $0.403{\times}10^{-3}mm^2/s$; area under the receiver operating characteristic curve [AUC], 0.977; sensitivity, 92.3%; specificity, 93.3%), followed by the $ADC_{mean}$, $ADC_{median}$, $ADC_{90}$, and hot-spot-ROI-based mean ADC. The AUC of $ADC_{10}$ 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.

Keywords

References

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