Rationale and Objectives
A histologic grade in head and neck squamous cell carcinoma (HNSCC) is clinically important because of its association with prognosis. The purpose of this study was to investigate the efficacy of histographic analysis of apparent diffusion coefficient (ADC) maps on the basis of the entire tumor volume in differentiating histologic grades in HNSCC at standard (b = 1000 s/mm 2 ) and high (b = 2000 s/mm 2 ) b values.
Materials and Methods
Fifty-four patients with HNSCC, including well-differentiated (WD; n = 35), moderately differentiated (MD; n = 13) and poorly differentiated (PD; n = 6) carcinomas, were retrospectively evaluated. ADC maps were obtained at two different b values (1000 and 2000 s/mm 2 ) in each patient. Tumors were delineated on each slice of ADC maps, and data were collected to obtain a histogram for the entire tumor volume. Histographic parameters were calculated, including mean, standard deviation, kurtosis, skewness, and the ratio of the kurtosis measured at b values of 1000 and 2000 s/mm 2 . These parameters were correlated with histologic grades.
Results
There was no significant correlation between tumor grades and histographic parameters obtained from ADC maps at b = 1000 s/mm 2 . However, mean ADC at b = 2000 s/mm 2 was significantly higher in WD HNSCC (881 ± 131 × 10 −6 mm 2 /s) than in MD and PD HNSCC (770 ± 163 and 780 ± 158 × 10 −6 mm 2 /s, respectively) ( P < .05). Kurtosis ratio was significantly higher in PD HNSCC (115 ± 10%) compared to WD and MD HNSCC (91 ± 21% and 86 ± 26%, respectively) ( P < .05). Diagnostic accuracy was 100%, 76.9%, and 65.8% for PD, MD, and WD HNSCC, respectively.
Conclusions
Histographic analysis of ADC maps on the basis of the entire tumor volume can be useful in differentiating histologic grades of HNSCC using mean ADC at b = 2000 s/mm 2 and kurtosis ratio.
Several clinicopathologic parameters are implicated in prognosis of head and neck squamous cell carcinoma (HNSCC), including tumor size, stage of disease, primary site of the tumor, nodal involvement, tumor thickness, status of surgical margins and status of human papilloma virus infection, and histologic grade . Histologic grade is generally determined by subjective assessment of the degree of keratinization, cellular and nuclear pleomorphism, and mitotic activity . Several studies have shown the association of histologic grade with prognosis . A multivariate analysis study by Larsen et al revealed that histologic grade was significantly related to nodal disease at the time of diagnosis. Jerjes et al reported that recurrence after surgery was associated with histologic grades, and about 90% of mortality due to locoregional spread or distant metastasis occurred in moderately differentiated (MD) or poorly differentiated (PD) carcinomas. In addition, Kademani et al reported histologic grade to have a significant effect on survival using a Kaplan-Meier model, showing 44% decrease in survival per grade. Therefore, the assessment of histologic grade prior to treatment may be of clinical importance.
Diffusion-weighted imaging with apparent diffusion coefficient (ADC) maps provides a quantitative index of water diffusivity for each voxel, allowing visualization of molecular diffusion in tissue and providing indirect information on the microstructure of tissue . Many studies have demonstrated the value of ADC maps in pretreatment brain tumor grading, on the basis of the concept that the high cellularity of high-grade tumors results in greater diffusion restriction and lower ADCs . In the head and neck region, ADC maps have recently been introduced and applied to differentiate neck pathologies , characterize neck lymph nodes , monitor posttreatment response , assess tumor recurrence , and predict local failure .
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Materials and methods
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Study Populations
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Table 1
Characteristics of Patients with Head and Neck Squamous Cell Carcinoma ( n = 54)
Variable Value Age (y) 60.7 ± 10.3 Well differentiated 61.1 ± 11.0 Moderately differentiated 62.9 ± 11.1 Poorly differentiated 59.8 ± 10.4 Men/women 32/22 Tumor location Sinonasal 1 (1.9%) Oral cavity 42 (77.8%) Pharyngeal and laryngeal 11 (20.3%) Tumor grade Well differentiated 35 (64.8%) Moderately differentiated 13 (24.1%) Poorly differentiated 6 (11.1%)
Data are expressed as mean ± standard deviation or as number (percentage).
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MRI Protocol
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Image Analysis
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Statistical Analyses
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Results
Quantitative Analysis of Histographic Parameters of ADC Maps
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Table 2
Histographic Parameters of ADC Maps on the Basis of the Entire Volume of WD, MD, and PD HNSCC
Parameter WD HNSCC ( n = 35) MD HNSCC ( n = 13) PD HNSCC ( n = 6)P b = 1000 s/mm 2 mean ADC1000 1176 ± 202 1094 ± 181 1044 ± 260 .224 kurtosis ADC1000 4.8 ± 1.5 4.7 ± 1.9 3.9 ± 1.5 .439 skewness ADC1000 0.6 ± 0.5 0.7 ± 0.5 0.5 ± 0.7 .850 b = 2000 s/mm 2 mean ADC2000 881 ± 131 ∗ 770 ± 163 780 ± 158 .035 kurtosis ADC2000 4.1 ± 0.8 3.8 ± 1.0 4.4 ± 1.6 .391 skewness ADC2000 0.2 ± 0.4 0.3 ± 0.5 0.3 ± 0.6 .794 Kurtosis ratio † (%) 91 ± 21 86 ± 25 115 ± 10 ∗ .024
ADC, apparent diffusion coefficient; HNSCC, head and neck squamous cell carcinoma; MD, moderately differentiated; PD, poorly differentiated; WD, well-differentiated.
Data are expressed as mean ± standard deviation. ADC measurements are expressed ×10 −6 mm 2 /s. The difference between grades was evaluated by using repeated-measures analysis of variance with Tukey-Kramer post hoc comparisons.
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Table 3
Histographic Parameters of ADC Maps on the Basis of the Single Representative Slice of WD, MD, and PD HNSCCs
Parameter WD HNSCC ( n = 35) MD HNSCC ( n = 13) PD HNSCC ( n = 6)P b = 1000 s/mm 2 mean ADC1000 1183 ± 220 1121 ± 204 1092 ± 225 .463 kurtosis ADC1000 4.1 ± 1.2 4.5 ± 2.1 4.0 ± 1.3 .583 skewness ADC1000 0.4 ± 0.5 0.8 ± 0.5 0.6 ± 0.5 .115 b = 2000 s/mm 2 mean ADC2000 883 ± 126 ∗ 767 ± 171 798 ± 163 .030 kurtosis ADC2000 3.9 ± 0.9 3.6 ± 1.0 4.3 ± 1.6 .393 skewness ADC2000 0.2 ± 0.5 0.4 ± 0.4 0.4 ± 0.6 .387 Kurtosis ratio † (%) 102 ± 31 92 ± 40 107 ± 19 .520
ADC, apparent diffusion coefficient; HNSCC, head and neck squamous cell carcinoma; MD, moderately differentiated; PD, poorly differentiated; WD, well-differentiated.
Data are expressed as mean ± standard deviation. ADC measurements are expressed ×10 −6 mm 2 /s. The difference between grades was evaluated by using repeated-measures analysis of variance with Tukey-Kramer post hoc comparisons.
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Receiver-Operating Characteristic Curve Analysis for Histographic Parameters
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Table 4
Receiver-Operating Characteristic Analysis of mean ADC2000 in Differentiation of WD from MD or PD HNSCC
Parameter AUC Accuracy for WD HNSCC Accuracy for MD and PD HNSCC Cutoff Value mean ADC2000 ∗ 0.692 70.1% (25 of 35 WD) 71.1% (14 of 19 MD or PD) 829 × 10 −6 mm 2 /s
ADC, apparent diffusion coefficient; AUC, area under the curve; HNSCC, head and neck squamous cell carcinoma; MD, moderately differentiated; PD, poorly differentiated; WD, well-differentiated.
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Table 5
Receiver-Operating Characteristic Analysis of Kurtosis Ratio in Differentiation of PD from WD or MD HNSCC
Parameter AUC Accuracy for PD HNSCC Accuracy for WD or MD HNSCC Cutoff Value Kurtosis ratio ∗ 0.846 100% (6 of 6 PD) 66.7% (32 of 48 WD and MD) 105 × 10 −6 mm 2 /s
ADC, apparent diffusion coefficient; AUC, area under the curve; HNSCC, head and neck squamous cell carcinoma; MD, moderately differentiated; PD, poorly differentiated; WD, well-differentiated.
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Discussion
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Conclusions
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Acknowledgment
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References
1. Jerjes W., Upile T., Petrie A., et. al.: Clinicopathological parameters, recurrence, locoregional and distant metastasis in 115 T1-T2 oral squamous cell carcinoma patients. Head Neck Oncol 2010; 2: pp. 1-21.
2. Woolgar J.A., Rogers S., West C.R., et. al.: Histopathological prognosticators in oral and oropharyngeal squamous cell carcinoma. Oral Oncol 2006; 42: pp. 229-239.
3. Larsen S.R., Johansen J., Sørensen J.A., et. al.: The prognostic significance of histological features in oral squamous cell carcinoma. J Oral Pathol Med 2009; 38: pp. 657-662.
4. Kademani D., Bell R.B., Bagheri S., et. al.: Prognostic factors in intraoral squamous cell carcinoma: the influence of histologic grade. J Oral Maxillofac Surg 2005; 63: pp. 1599-1605.
5. Scully C., Bagan J.: Oral squamous cell carcinoma overview. Oral Oncol 2009; 45: pp. 301-308.
6. Hayashida Y., Hirai T., Morishita S., et. al.: Diffusion-weighted imaging of metastatic brain tumors: comparison with histologic type and tumor cellularity. AJNR Am J Neuroradiol 2006; 27: pp. 1419-1425.
7. Herneth A.M., Guccione S., Bednarski M.: Apparent diffusion coefficient: a quantitative parameter for in vivo tumor characterization. Eur J Radiol 2003; 45: pp. 208-213.
8. Wang J., Takashima S., Takayama F., et. al.: Head and neck lesions: characterization with diffusion-weighted echo-planar MR imaging. Radiology 2001; 220: pp. 621-630.
9. Arvinda H., Kesavadas C., Sarma P., et. al.: Glioma grading: sensitivity, specificity, positive and negative predictive values of diffusion and perfusion imaging. J Neurooncol 2009; 94: pp. 87-96.
10. Lee E., Lee S., Agid R., et. al.: Preoperative grading of presumptive low-grade astrocytomas on MR imaging: diagnostic value of minimum apparent diffusion coefficient. AJNR Am J Neuroradiol 2008; 29: pp. 1872-1877.
11. Sumi M., Ichikawa Y., Nakamura T.: Diagnostic ability of apparent diffusion coefficients for lymphomas and carcinomas in the pharynx. Eur Radiol 2007; 17: pp. 2631-2637.
12. Sumi M., Nakamura T.: Diagnostic importance of focal defects in the apparent diffusion coefficient-based differentiation between lymphoma and squamous cell carcinoma nodes in the neck. Eur Radiol 2009; 19: pp. 975-981.
13. Maeda M., Kato H., Sakuma H., et. al.: Usefulness of the apparent diffusion coefficient in line scan diffusion-weighted imaging for distinguishing between squamous cell carcinomas and malignant lymphomas of the head and neck. AJNR Am J Neuroradiol 2005; 26: pp. 1186-1192.
14. Srinivasan A., Dvorak R., Perni K., et. al.: Differentiation of benign and malignant pathology in the head and neck using 3T apparent diffusion coefficient values: early experience. AJNR Am J Neuroradiol 2008; 29: pp. 40-44.
15. Sumi M., Sakihama N., Sumi T., et. al.: Discrimination of metastatic cervical lymph nodes with diffusion-weighted MR imaging in patients with head and neck cancer. AJNR Am J Neuroradiol 2003; 24: pp. 1627-1634.
16. Razek A.A.K.A., Megahed A.S., Denewer A., et. al.: Role of diffusion-weighted magnetic resonance imaging in differentiation between the viable and necrotic parts of head and neck tumors. Acta Radiol 2008; 49: pp. 364-370.
17. Razek A., Kandeel A., Soliman N., et. al.: Role of diffusion-weighted echo-planar MR imaging in differentiation of residual or recurrent head and neck tumors and posttreatment changes. AJNR Am J Neuroradiol 2007; 28: pp. 1146-1152.
18. Vandecaveye V., De Keyzer F., Vander Poorten V., et. al.: Evaluation of the larynx for tumour recurrence by diffusion-weighted MRI after radiotherapy: initial experience in four cases. Br J Radiol 2006; 79: pp. 681-687.
19. King A.D., Mo F.K.F., Yu K.H., et. al.: Squamous cell carcinoma of the head and neck: diffusion-weighted MR imaging for prediction and monitoring of treatment response. Eur Radiol 2010; 20: pp. 2213-2220.
20. Hatakenaka M., Nakamura K., Yabuuchi H., et. al.: Pretreatment apparent diffusion coefficient of the primary lesion correlates with local failure in head-and-neck cancer treated with chemoradiotherapy or radiotherapy. Int J Radiat Oncol Biol Phys 2011; 81: pp. 339-345.
21. Law M., Young R., Babb J., et. al.: Histogram analysis versus region of interest analysis of dynamic susceptibility contrast perfusion MR imaging data in the grading of cerebral gliomas. AJNR Am J Neuroradiol 2007; 28: pp. 761-766.
22. Tozer D.J., Jager H.R., Danchaivijitr N., et. al.: Apparent diffusion coefficient histograms may predict low grade glioma subtype. NMR Biomed 2007; 20: pp. 49-57.
23. Emblem K.E., Nedregaard B., Nome T., et. al.: Glioma grading by using histogram analysis of blood volume heterogeneity from MR-derived cerebral blood volume maps. Radiology 2008; 247: pp. 808-817.
24. Srinivasan A., Galban C., Johnson T., et. al.: Utility of the k-means clustering algorithm in differentiating apparent diffusion coefficient values of benign and malignant neck pathologies. AJNR Am J Neuroradiol 2010; 31: pp. 736-740.
25. Kim H.S., Kim J.H., Kim S.H., et. al.: Posttreatment high-grade glioma: usefulness of peak height position with semiquantitative MR perfusion histogram analysis in an entire contrast-enhanced lesion for predicting volume fraction of recurrence. Radiology 2010; 256: pp. 906-915.
26. Ma J., Kim H., Rim N.J., et. al.: Differentiation among glioblastoma multiforme, solitary metastatic tumor, and lymphoma using whole-tumor histogram analysis of the normalized cerebral blood volume in enhancing and perienhancing lesions. AJNR Am J Neuroradiol 2010; 31: pp. 1699-1706.
27. Pindborg J.J., Reichart P.A., Smith C.J., et. al.: Histological typing of cancer and precancer of the oral mucosa.2nd ed.1997.SpringerNew York 11–13
28. Abramoff M.D., Magalhaes P., Ram S.: Image processing with ImageJ. Biophotonics Int 2004; 11: pp. 36-43.
29. Cardesa A., Gale N., Nadal A., et. al.: Squamous cell carcinoma.Barnes L.Eveson J.W.Reichart P.Sidransky D.World Health Organization classification of tumours. Pathology and genetics of head and neck tumours.2005.IARC PressLyon, France:pp. 118-121.
30. Santhosh K., Thomas B., Radhakrishnan V.V., et. al.: Diffusion tensor and tensor metrics imaging in intracranial epidermoid cysts. J Magn Reson Imaging 2009; 29: pp. 967-970.
31. Seo H., Chang K.H., Na D., et. al.: High b-value diffusion (b = 3000 s/mm 2 ) MR imaging in cerebral gliomas at 3T: visual and quantitative comparisons with b = 1000 s/mm 2 . AJNR Am J Neuroradiol 2008; 29: pp. 458-463.
32. Niendorf T., Dijkhuizen R.M., Norris D.G., et. al.: Biexponential diffusion attenuation in various states of brain tissue: implications for diffusion weighted imaging. Magn Reson Med 1996; 36: pp. 847-857.
33. Mulkern R.V., Gudbjartsson H., Westin C.F., et. al.: Multi component apparent diffusion coefficients in human brain. NMR Biomed 1999; 12: pp. 51-62.
34. DeLano M.C., Cooper T.G., Siebert J.E., et. al.: High-b-value diffusion-weighted MR imaging of adult brain: image contrast and apparent diffusion coefficient map features. AJNR Am J Neuroradiol 2000; 21: pp. 1830-1836.
35. Clark C., Le Bihan D.: Water diffusion compartmentation and anisotrophy at high b values in the hhuman brain. Magn Reson Med 2000; 44: pp. 852-859.
36. Choi S.H., Paeng J.C., Shon C.H., et. al.: Correlation of 18F-FDG upatke with apparent diffusion coefficient ratio measured on standard and high b value diffusion MRI in head and neck cancer. J Nucl Med 2011; 52: pp. 1056-1062.
37. Nicholson C., Syková E.: Extracellular space structure revealed by diffusion analysis. Trends Neurosci 1998; 21: pp. 207-215.
38. Fong D., Bhatia K.S.S., Yeung D., et. al.: Diagnostic accuracy of diffusion-weighted MR imaging for nasopharyngeal carcinoma, head and neck lymphoma and squamous cell carcinoma at the primary site. Oral Oncol 2010; 46: pp. 603-606.
39. Ogura A., Hayakawa K., Miyati T., et. al.: Imaging parameter effects in apparent diffusion coefficient determination of magnetic resonance imaging. Eur J Radiol 2011; 77: pp. 185-188.