Korean J Radiol.  2018 Apr;19(2):358-365. 10.3348/kjr.2018.19.2.358.

A Whole-Tumor Histogram Analysis of Apparent Diffusion Coefficient Maps for Differentiating Thymic Carcinoma from Lymphoma

• Zhang W
• Zhou Y
• Xu XQ
• Kong LY
• Xu H
• Yu TF
• Shi HB
• Feng Q

• KMID: 2404934
• DOI: http://doi.org/10.3348/kjr.2018.19.2.358

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.

Keyword

Histogram analysis; Diffusion weighted imaging; Apparent diffusion coefficient; Thymic carcinoma; Lymphoma; Mediastinal mass

MeSH Terms

Area Under Curve
Diffusion*
Humans
Lymphoma*
Retrospective Studies
ROC Curve
Sensitivity and Specificity
Thymoma*

Figure

• 

  Fig. 1 Schematic diagram of placements of ROIs.In terms of hot spot ROI based mean ADC measurements, slice on which tumor showed biggest diameter was chosen. Three circular ROIs were drawn on tumor's area which demonstrated mostly increased signal intensity on diffusion weighted image (b1000 map) (A). In terms of histogram analysis, ROIs were manually drawn on each slice that encompassed much of tumor area (B). ADC = apparent diffusion coefficient, ROI = region of interest

• 

  Fig. 2 Box plots show comparison of histogram-based and hot-spot-ROI-based ADC measurements between thymic carcinoma and lymphoma.

• 

  Fig. 3 Images of 39-year-old man with diffuse large B cell lymphoma.Axial T1-weighted image (A) shows mass in anterior mediastinal region. After ROI was placed (B), pixel-by-pixel colored ADC map (C) was generated and embedded in axial diffusion-weighted imaging. Histogram analysis of whole lesion shows lower cumulative ADC value but higher relative frequency (D). ADCmedian and ADC10 were 0.502 and 0.146 × 10−3 mm2/s, respectively. Typical histopathologic appearance on hematoxylin-eosin stain (E, × 10) and CD20 positive on immunohistochemistry (F, × 10) confirmed diagnosis of lymphoma.

• 

  Fig. 4 Images of 42-year-old man with thymic squamous cell carcinoma.Axial T1-weighted image shows mass in anterior mediastinal region (A). After ROI was placed (B), pixel-by-pixel colored ADC map (C) was generated and embedded in axial diffusion-weighted imaging. Compared with lymphoma, histogram analysis of whole lesion showed higher cumulative ADC value but lower relative frequency (D). ADCmedian and ADC10 were 0.895 and 0.395 × 10−3 mm2/s, respectively. Typical histopathologic appearance on hematoxylin-eosin stain (E, × 10) and P63 positive on immunohistochemistry (F, × 10) confirmed diagnosis of squamous cell carcinoma.

• 

  Fig. 5 Receiver operating characteristic curves of histogram-based and hot-spot-ROI-based ADC measurements for differentiating thymic carcinoma from lymphoma.

Reference

1. Azizad S, Sannananja B, Restrepo CS. Solid tumors of the mediastinum in adults. Semin Ultrasound CT MR. 2016; 37:196–211. PMID: 27261345.

2. Shepherd A, Riely G, Detterbeck F, Simone CB 2nd, Ahmad U, Huang J, et al. Thymic Carcinoma Management Patterns among International Thymic Malignancy Interest Group (ITMIG) physicians with consensus from the Thymic Carcinoma Working Group. J Thorac Oncol. 2017; 12:745–751. PMID: 27876674.

3. Piña-Oviedo S, Moran CA. Primary mediastinal classical hodgkin lymphoma. Adv Anat Pathol. 2016; 23:285–309. PMID: 27441757.

4. Abdel Razek AA, Soliman N, Elashery R. Apparent diffusion coefficient values of mediastinal masses in children. Eur J Radiol. 2012; 81:1311–1314. PMID: 21439745.

5. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988; 44:837–845. PMID: 3203132.

6. Abdel Razek AA, Khairy M, Nada N. Diffusion-weighted MR imaging in thymic epithelial tumors: correlation with World Health Organization classification and clinical staging. Radiology. 2014; 273:268–275. PMID: 24877982.

7. Gümüştaş S, Inan N, Sarisoy HT, Anik Y, Arslan A, Ciftçi E, et al. Malignant versus benign mediastinal lesions: quantitative assessment with diffusion weighted MR imaging. Eur Radiol. 2011; 21:2255–2260. PMID: 21698463.

8. Shin KE, Yi CA, Kim TS, Lee HY, Choi YS, Kim HK, et al. Diffusion-weighted MRI for distinguishing non-neoplastic cysts from solid masses in the mediastinum: problem-solving in mediastinal masses of indeterminate internal characteristics on CT. Eur Radiol. 2014; 24:677–684. PMID: 24177751.

9. Seki S, Koyama H, Ohno Y, Nishio M, Takenaka D, Maniwa Y, et al. Diffusion-weighted MR imaging vs. multi-detector row CT: direct comparison of capability for assessment of management needs for anterior mediastinal solitary tumors. Eur J Radiol. 2014; 83:835–842. PMID: 24636535.

10. Yabuuchi H, Matsuo Y, Abe K, Baba S, Sunami S, Kamitani T, et al. Anterior mediastinal solid tumours in adults: characterisation using dynamic contrast-enhanced MRI, diffusion-weighted MRI, and FDG-PET/CT. Clin Radiol. 2015; 70:1289–1298. PMID: 26272529.

11. Xu X, Su G, Hu H, Wang Y, Hong X, Shi H, et al. Effects of regions of interest methods on apparent coefficient measurement of the parotid gland in early Sjögren's syndrome at 3T MRI. Acta Radiol. 2017; 58:27–33. PMID: 26987670.

12. Zhang YD, Wang Q, Wu CJ, Wang XN, Zhang J, Liu H, et al. The histogram analysis of diffusion-weighted intravoxel incoherent motion (IVIM) imaging for differentiating the gleason grade of prostate cancer. Eur Radiol. 2015; 25:994–1004. PMID: 25430007.

13. Lu SS, Kim SJ, Kim N, Kim HS, Choi CG, Lim YM. Histogram analysis of apparent diffusion coefficient maps for differentiating primary CNS lymphomas from tumefactive demyelinating lesions. AJR Am J Roentgenol. 2015; 204:827–834. PMID: 25794073.

14. Xu XQ, Hu H, Su GY, Zhang L, Liu H, Hong XN, et al. Orbital indeterminate lesions in adults: combined magnetic resonance morphometry and histogram analysis of apparent diffusion coefficient maps for predicting malignancy. Acad Radiol. 2016; 23:200–208. PMID: 26625705.

15. Choi MH, Oh SN, Rha SE, Choi JI, Lee SH, Jang HS, et al. Diffusion-weighted imaging: apparent diffusion coefficient histogram analysis for detecting pathologic complete response to chemoradiotherapy in locally advanced rectal cancer. J Magn Reson Imaging. 2016; 44:212–220. PMID: 26666560.

16. Nougaret S, Vargas HA, Lakhman Y, Sudre R, Do RK, Bibeau F, et al. Intravoxel incoherent motion-derived histogram metrics for assessment of response after combined chemotherapy and radiation therapy in rectal cancer: initial experience and comparison between single-section and volumetric analyses. Radiology. 2016; 280:446–454. PMID: 26919562.

17. Xu XQ, Hu H, Su GY, Liu H, Hong XN, Shi HB, et al. Utility of histogram analysis of ADC maps for differentiating orbital tumors. Diagn Interv Radiol. 2016; 22:161–167. PMID: 26829400.

18. Razek AA, Elkhamary S, Mousa A. Differentiation between benign and malignant orbital tumors at 3-T diffusion MR-imaging. Neuroradiology. 2011; 53:517–522. PMID: 21286695.

19. Takahashi K, Al-Janabi NJ. Computed tomography and magnetic resonance imaging of mediastinal tumors. J Magn Reson Imaging. 2010; 32:1325–1339. PMID: 21105138.

20. Donati OF, Mazaheri Y, Afaq A, Vargas HA, Zheng J, Moskowitz CS, et al. Prostate cancer aggressiveness: assessment with whole-lesion histogram analysis of the apparent diffusion coefficient. Radiology. 2014; 271:143–152. PMID: 24475824.

21. Suo S, Zhang K, Cao M, Suo X, Hua J, Geng X, et al. Characterization of breast masses as benign or malignant at 3.0T MRI with whole-lesion histogram analysis of the apparent diffusion coefficient. J Magn Reson Imaging. 2016; 43:894–902. PMID: 26343918.

22. Johnson PW, Davies AJ. Primary mediastinal B-cell lymphoma. Hematology Am Soc Hematol Educ Program. 2008; 349–358. PMID: 19074109.

23. Ströbel P, Bauer A, Puppe B, Kraushaar T, Krein A, Toyka K, et al. Tumor recurrence and survival in patients treated for thymomas and thymic squamous cell carcinomas: a retrospective analysis. J Clin Oncol. 2004; 22:1501–1509. PMID: 15084623.

24. Xu XQ, Choi YJ, Sung YS, Yoon RG, Jang SW, Park JE, et al. Intravoxel incoherent motion MR imaging in the head and neck: correlation with dynamic contrast-enhanced MR imaging and diffusion-weighted imaging. Korean J Radiol. 2016; 17:641–649. PMID: 27587952.