Dynamic Susceptibility Contrast Perfusion-Weighted

ORIGINAL ARTICLE

Dynamic Susceptibility Contrast Perfusion-Weighted Magnetic Resonance Imaging and Diffusion-Weighted Magnetic Resonance Imaging in Differentiating Recurrent Head and Neck Cancer From Postradiation Changes Ahmed Abdel Khalek Abdel Razek, MD,* Gada Gaballa, MD,* Germin Ashamalla, MD,† Mohamed Saad Alashry, MD,† and Nadia Nada, MD‡ Purpose: The aim of this study was to assess dynamic susceptibility contrast (DSC) perfusion-weighted magnetic resonance (MR) imaging and diffusion-weighted MR imaging in differentiating recurrent head and neck cancer from postradiation changes. Methods: A prospective study was done on 41 patients with head and neck cancer after radiotherapy who underwent diffusion-weighted MR imaging, DSC perfusion-weighted MR imaging, and routine postcontrast MR imaging. The apparent diffusion coefficient (ADC) map and time signal intensity curve of the lesion were created. The ADC value, DSC percentage (DSC%), and contrast enhancement percentage of the lesion were calculated. The final diagnosis was done with biopsy. Results: There was significant difference (P = 0.001) in ADC between recurrent cancer (0.94 ± 0.16 10−3mm2/s) and postradiation changes (1.37 ± 0.12 10−3mm2/s). There was significant difference (P = 0.001) in DSC% of recurrent cancer (30.9% ± 5.16%) and postradiation changes (12.1% ± 3.06%). Selection of ADC equal to or less than 1.07 10−3mm2/s and DSC% greater than 16.6% to predict recurrence have areas under the curve of 0.822 and 0.900 and accuracy of 92.7% and 95.1%, respectively. Combination of ADC and DSC% has are under the curve of 0.992 and accuracy of 97.6%. Conclusions: Combined ADC and DSC% are noninvasive imaging parameters that can play a role in the differentiation of recurrent head and neck cancer from postradiation changes. Key Words: cancer, diffusion, head and neck, MR imaging, perfusion (J Comput Assist Tomogr 2015;39: 849–854)

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reatment of head and neck cancer with surgery, radiation therapy, and/or chemotherapy improves patient survival and quality of life; however, interpretation of posttreatment follow-up imaging studies is difficult because surgery can alter anatomy, and radiation therapy and chemotherapy can result in edema and fibrosis.1–3 These posttreatment changes can mimic tumor recurrence, and sometimes it is difficult to distinguish these from residual or recurrent tumor on computed tomography (CT) or magnetic resonance (MR) images. The differentiation between residual or recurrent tumor and postradiation fibrosis is difficult with routine CT and MR.4–6 Dynamic contrast MR imaging and proton MR spectroscopy are used in this differentiation in head and neck but are of limited value.7–10 Advanced CT techniques such as CT perfusion, dual-energy CT, and volumetric assessment of the tumor are used, but they are associated with administration of contrast medium.11–13 Positron emission tomography CT is used for From the Departments of *Diagnostic Radiology, †Radiotherapy, and ‡Pathology, Mansoura Faculty of Medicine, Mansoura, Egypt. Received for publication April 20, 2015; accepted June 21, 2015. Correspondence to: Ahmed Abdel Khalek Abdel Razek, MD, Department of Diagnostic Radiology, Mansoura Faculty of Medicine, Mansoura, Dakahlia, Egypt 35512 (e‐mail: [email protected]). The authors declare no conflict of interest. Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved. DOI: 10.1097/RCT.0000000000000311

this assessment, but it is not routinely available and is expensive.14 Biopsy is the criterion standard for the assessment of postradiation neck, but it may be inconclusive, have discordant cytology findings, or suffer from inadequate samples.15 Diffusion-weighted MR imaging is based on Brownian motion of water protons in the tissue, which is affected by the microstructure of tissue. Diffusion-weighted imaging can distinguish between different tissue compartments at the cellular level.16,17 Diffusion-weighted MR imaging is used for differentiation and characterization of primary tumors of the head and neck, nodal staging,18 and prediction of treatment response.19 In addition, several promising studies have been reported on the usefulness of diffusion-weighted MR imaging in discrimination between recurrent or residual tumor and posttreatment changes.20–23 Dynamic susceptibility contrast (DSC) perfusion-weighted MR imaging obtained with bolus tracking technique monitors the passage of contrast medium through a capillary bed. It is based on inhomogeneity of the magnetic field during the passage of a short bolus of contrast medium through the capillary bed.24,25 Dynamic susceptibility contrast perfusion-weighted MR imaging is applied for differentiation of residual or recurrent brain tumors from postradiation changes described in several articles.26–29 In addition, it is used in assessment of head and neck tumors and cervical lymphadenopathy.30,31 To our knowledge, there are no previous studies in the literature about the potential use of DSC perfusion-weighted MR imaging in differentiating recurrent head and neck tumors from postradiation changes. The aim of this work was to assess DSC perfusion-weighted MR imaging and diffusion-weighted MR imaging in differentiating recurrent head and neck cancer from postradiation changes.

MATERIALS AND METHODS A prospective study was done on 43 consecutive patients with head and neck cancer. Inclusion criteria were patients with head and neck cancer who underwent a complete course of radiotherapy (4–6 months after treatment) with suspected recurrence of the lesion clinically. Two patients were excluded from the study because of motion artifacts. The final number of patients included in this study was 41 (31 men and 10 women; age range, 55–79 years; mean age, 63 years). Pathological findings of primary cancer were as follows: squamous cell carcinoma (n = 32), mucoepidermoid carcinoma (n = 4), adenoid cystic carcinoma (n = 3), and adenocarcinoma (n = 2). Table 1 shows the pathological types of recurrent tumors and postradiation changes. These tumors were located at the nasal cavity and paranasal sinuses (n = 12), oropharynx (n = 9), oral cavity (n = 8), larynx (n = 6), and parotid (n = 6). The final diagnosis was done with surgical biopsy (n = 25), fine-needle aspiration biopsy (n = 9), and core biopsy (n = 7). The biopsy was done 10 to 18 days after MR imaging. All patients underwent routine T1- and T2-weighted MR imaging, diffusion-weighted MR imaging, dynamic susceptibility perfusion-weighted MR imaging, and finally

J Comput Assist Tomogr • Volume 39, Number 6, November/December 2015

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TABLE 1. Pathological Types of Recurrent Tumors and Postradiation Changes Recurrent Tumor Postradiation (n = 26) Changes (n = 15) Squamous cell carcinoma (n = 32) Mucoepidermoid carcinoma (n = 4) Adenoid cystic carcinoma (n = 3) Adenocarcinoma (n = 2)

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acquisitions for each slice, and the total number of images obtained was 1100 images.

Contrast MR Imaging Routine postcontrast study T1-weighted images were obtained after the dynamic study. The scanning parameters were as follows: TR/TE = 800/15 milliseconds, section thickness = 5 mm, interslice gap = 1.5 mm, FOV = 25 to 30 cm2 and acquisition matrix = 256 224.

Image Analysis

postcontrast fat-suppressed T1-weighted imaging of the head and neck. Editorial board approval and informed consent from the patients were obtained.

MR Imaging All MR images were acquired on a 1.5-T scanner (Symphony; Siemens Medical Solutions, Erlangen, Germany) equipped with a self-shielding gradient set (maximum gradient strength of 30 mTm, slew rate of 120 T/m/s). All patients underwent T1-weighted (repetition time [TR]/time to echo [TE] = 800/15 milliseconds) and T2-weighted fast-spin-echo imaging (TR/TE = 6000/80 milliseconds). The scanning parameters were as follows: section thickness = 5 mm, interslice gap = 1.5 mm, field of view (FOV) = 25 to 30 cm2, and acquisition matrix = 256 224.

Diffusion-Weighted MR Imaging Diffusion-weighted MR images were obtained before contrast medium injection using a multislice, single-shot, spin-echo, echo-planar image sequence. Automatic multiangle-projection shim and chemical shift selective fat-suppression technique were applied to reduce the artifacts at diffusion-weighted MR images. The motion-probing gradient was applied before and after the 180-pulse with echoplanar imaging readout. A set of multiple axial scans of the head and neck was obtained. The imaging parameters were: as follows TR/TE = 10000/108 milliseconds, number of excitation = 16, bandwidth = 300 kHz, FOV = 25 to 30 cm2, section thickness = 5 mm, interslice gap = 1 mm, and acquisition matrix = 256 128. The diffusion gradients were applied in 3 orthogonal directions (Y/X and Z). Diffusion-weighted MR images were acquired with b factor of 500 and 1000 s/mm2, and the apparent diffusion coefficient (ADC) maps were reconstructed.

Imaging analysis was performed by 2 radiologists (G.G. and A.G.) with 15 and 10 years of experience in MR imaging, blinded to the clinical findings and histopathologic examination results. Disagreement of both radiologists was solved in consensus. A round region of interest (ROI) was placed around the most enhanced region of the lesion at contrast T1-weighted images that was not affected by chemical shift and magnetic susceptibility artifacts using an electronic cursor. A copy of ROI was placed on the ADC map and DSC images. The contrast enhancement percentage (CE%) was computed by means of the following formula: (SIpost − SIpre) / SIpre 100, where SIpre and SIpost indicate signal intensity of the lesion obtained before and after CE, respectively. The ADC values were calculated according to the following formula: ADC = −(1 / b) ln(S2 / S1), where the S2 and S1 are the signal intensities at b value of 500 and 1000 10−3 s/mm2, respectively. The signal-intensity-over-time curves were established with dedicated software for mean curve analysis (mean curve; Siemens Medical Solutions, Erlangen, Germany). The DSC percentage (DSC%) was calculated as S0 − SI / S0 100%, where SO represents the signal intensity of the lesion just before descent of signal intensity, and SI represents the signal intensity at peak descent.

Statistical Analysis

DSC Perfusion MR Imaging

The statistical analysis of data was done by using Statistical Package for Social Science version 16.0 (SPSS Inc, Chicago, Ill). The mean and SD of ADC values, DSC%, and CE% of recurrent cancer and postradiation changes were calculated. The analysis of data was done to test statistically significant difference. Student t test was used to compare between the ADC, DSC%, and CE% of recurrent cancer and postradiation changes. P ≤ 0.05 was considered statistically significant. The receiver operating characteristic (ROC) curve was drawn to detect the cutoff point of ADC value, DSC%, and CE% used to differentiate recurrence cancer from postradiation changes with calculation of area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Multivariate logistic regression model was done to determine the combined parameters with high accuracy for differentiating recurrence from postradiation changes by generating AUC.

Multislice echo-planar imaging gradient echo sequence was used. The scanning parameters were as follows: TR/TE = 2280/ 50 milliseconds, number of excitation = 1, flip angle = 70 degrees, section thickness = 5 mm, interslice gap = 1.5 mm, FOV = 25 to 30 cm2, and acquisition matrix = 128 128. Dynamic susceptibility contrast T2*-weighted perfusion MR images were obtained after administration of gadopentate-dimeglumine in a dose of 0.1 mmol/ kg body weight. The injection was performed by automatic injector in the right arm at a rate of 4 mL/s followed by 20 mL saline. The first 10 acquisitions were performed prior to contrast medium injection to establish precontrast baseline series. The data acquisition time was 110 seconds, and the time between the data points was 2 seconds. The number of slices was 20 with 55

The final diagnosis was residual or recurrent tumor (Fig. 1) in 26 patients and postradiation changes (Fig. 2) in 15 patients. Table 2 shows the mean, SD, minimum and maximum of ADC, DSC%, and CE% of recurrent head and neck cancer and postradiation changes. There was insignificant difference in the ADC value, DSC%, and CE% between the recurrent squamous cell carcinoma and other recurrent malignancies (P = 0.08, 0.06, 0.9, respectively). Figure 3 shows the box-and-whisker plot of the ADC, DSC%, and CE% of recurrent cancer and postradiation changes. Table 3 and Figure 4 show the results of ROC with cutoff

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RESULTS

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Head and Neck Cancer and Postradiation Changes

FIGURE 1. Recurrent head and neck cancer. A, Axial contrast MR image shows mild inhomogeneous enhancement of the nasal mass with calculated CE% of 0.28. B, Axial diffusion-weighted MR image shows restricted diffusion with low ADC value (0.91 10−3 mm2/s) of the recurrent lesion at the ROI. C, Axial susceptibility perfusion-weighted MR image shows the ROI of the lesion. D, Time signal intensity curve of the lesion shows the DSC% is 30.7%.

values of ADC, DSC%, and CE% used to differentiate recurrence from postradiation changes. The mean ADC value of recurrent head and neck cancer was 0.94 ± 0.16 10−3 mm2/s, and that of postradiation changes was 1.37 ± 0.12 10−3 mm2/s. There was significant difference in the ADC value between recurrence and postradiation changes (P = 0.001). When an ADC value of 1.07 10−3 mm2/s or less was used as a cutoff value for differentiation of recurrence from postradiation changes with AUC of 0.942, the following values were obtained: accuracy of 92.7%, sensitivity of 88.5%, specificity of 100%, PPV of 100%, and NPV of 83.3%. The DSC% values of patients with recurrent lesions ranged from 15.1% to 36.7%, with a mean value of 30.9% ± 5.16%. The mean DSC% in patients with posttreatment changes ranged from 6.3% to 16.6% with a mean value of 12.14% ± 3.06%. The difference between DSC% values of recurrent tumor and postradiation changes was statistically significant (P = 0.001). Despite the statistical difference between the mean values of both groups, the plot revealed a relative range of overlapping values in DSC%. When DSC% value of greater than 16.6% was used for differentiating tumor recurrence from postradiation changes, the best results were obtained with AUC of 0.992, accuracy of 95.1%, sensitivity of 92.3%, specificity of 88.9%, PPV of 100%, and NPV of 88.2%. The mean CE% value of recurrence of head and neck cancer was 0.20 ± 0.03, and that of postradiation changes was 0.17 ± 0.02. There was no significance in the CE% between recurrence and postradiation changes (P = 0.06). When a CE% value greater than 0.19 was used as a cutoff value for differentiation of recurrence from postradiation changes with AUC of 0.713, accuracy was 73.3%; sensitivity, 46.2%; specificity, 100%; PPV, 100%; and NPV, 51.7%. Multiparametric analysis revealed the best combination for diagnosis of recurrence was ADC of 1.07 10−3 mm2/s and

DSC% of 16.6% with AUC of 0.992, accuracy of 97.6%, sensitivity of 96.2%, specificity of 100%, PPV of 100%, and NPV of 93.8%.

DISCUSSION In this study, the main finding is that diffusion-weighted MR imaging and DSC perfusion-weighted MR imaging can be used for the assessment of head and neck cancer after treatment. Recurrent head and neck cancer revealed restricted diffusion with high DSC percentage, and postradiation changes show unrestricted diffusion with low DSC percentage. Multiparametric analysis revealed the best combination for diagnosis of recurrence was ADC of 1.07 10−3 mm2/s and DSC% of 16.6% with AUC of 0.992%. Dynamic susceptibility contrast perfusion-weighted MR imaging is well suited for evaluation of tumor angiogenesis as it reflects the physiology of the microcirculation, especially the microvasculature and the extracellular space. Tumor angiogenesis is a complex process where new vessels grow toward and within the tissue that is mediated by factors released from malignant tumor cells. Dynamic susceptibility contrast perfusion-weighted MR imaging responds only to more specific changes in microvascular perfusion.22–26 The DSC% represents the percentage of signal intensity loss at the start of the first pass of contrast medium. The degree of this loss is dependent on the size of extravascular space and the rate of blood flow. This parameter is an important hemodynamic variable that can be used in characterization of tumors.26–29 In this study, DSC% of recurrent head and neck cancer is significantly different than that of postradiation changes. This is attributed to high vascularity with increased capillary perfusion of recurrent or residual tumor compared with postradiation change. The tumor blood vessels are typically dilated and tortuous with

FIGURE 2. Postradiation changes. A, Axial MR image shows lesion in the left parotid region after radiotherapy with calculated CE% of 0.14. B, Axial diffusion-weighted MR image shows unrestricted diffusion of the lesion with low ADC value (1.32 10−3 mm2/s) lesion at the ROI. C, Axial susceptibility perfusion-weighted MR image shows the ROI of the lesion. D, Time signal intensity curve shows the DSC% of the lesion is 6%. © 2015 Wolters Kluwer Health, Inc. All rights reserved.


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TABLE 2. The Mean, SD, Minimum, and Maximum of ADC, DSC%, and CE% of Recurrent Head and Neck Cancer and Postradiation Changes Recurrence

Postradiation Changes

P

ADC 0.94 ± 0.16 (0.81–1.47) 1.37 ± 0.12 (1.08–1.51) 0.001 DSC% 30.9 ± 5.16 (15.1–36.7) 12.14 ± 3.06 (6.3–16.6) 0.001 CE% 0.20 ± 0.03 (0.16–0.28) 0.17 ± 0.02 (0.13–0.19) 0.06

abnormal branching pattern, dead ends, and no organization into the arterioles, capillaries, and venules.23–27 Not only the number but also the size of the vessels increase with increased tumor angiogenesis. Several unique properties of new vessels include increased tumor blood volume, arteriovenous shunt formation, altered capillary transit time, and increased capillary permeability. Radiation necrosis is characterized by extensive fibrinoid necrosis, vascular dilation, and endothelial injury of the surrounding normal cerebral vasculature.25–29 Recurrent head and neck cancer shows decreased ADC compared with nonmalignant changes or radionecrosis, presumably

due to increased free water in necrosis and increased cellularity in recurrent tumors.16–18 As diffusion within recurrent or residual tumors is impeded by the presence of cellular membranes and macromolecular structures, treatment with radiation and/or chemotherapy triggers cell death that can result in the loss of cell membrane integrity and reduced cell density, which can be detected as an increase in mean diffusion value for the tumor.18–20 Razek et al23 found ADC values for residual or recurrent tumor of 1.17 10−3 mm2/s and for posttreatment changes of 2.07 10−3 mm2/s. Another study added that the ADC values of diffusion-weighted MR imaging including biexponential fitting are lower for patients with tumor (1.20 ± 49 10−3 mm2/s) compared with those without tumor (1.82 ± 41 10−3 mm2/s; P < 0.0002). The ROC analysis provided an optimal threshold for ADC of 1.30 10−3 mm2/s that results in sensitivity of 67%, specificity of 86%, and accuracy of 78%. Qualitative diffusion-weighted MR imaging combined with morphological images allowed the detection of tumor recurrence with a sensitivity of 94% and specificity of 100%.21 Dynamic contrast susceptibility–weighted MR imaging is used to track the first pass of an exogenous, paramagnetic, nondiffusible contrast agent. The contrast agent induces a transient

FIGURE 3. Box-and-whisker plot of ADC, DSC%, and CE% of recurrence of head and neck cancer and postradiation changes. A, Box-and-whisker plot shows mean ADC of recurrence (0.94 ± 0.16 10−3 mm2/s) is significantly different (P = 0.001) from that of postradiation (1.37 ± 0.12 10−3 mm2/s). B, Box-and-whisker plot shows the DSC% of recurrence (30.9% ± 5.16%) is significantly different (P = 0.001) from that of postradiation (12.14% ± 3.06%). C, Box-and-whisker plot shows CE% of recurrence (0.20 ± 0.03) is not significant (P = 0.06) from that of postradiation changes (0.17 ± 0.02). Figure 3 can be viewed online in color at www.jcat.org.

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Head and Neck Cancer and Postradiation Changes

TABLE 3. The Receiver Operating Characteristic Curve Results of ADC, DSC%, and CE% of Recurrent Head and Neck Cancer Versus Postradiation Changes Parameter ADC DSC% CE%

Cutoff Point

AUC

Accuracy

Sensitivity

Specificity

PPV

NPV

≤1.07 >16.6 >0.19

0.942 0.992 0.713

92.7% 95.1% 73.3%

88.5% 92.3% 46.2%

100% 88.9% 100%

100% 100% 100%

83.3% 88.2% 51.7%

decrease in the signal intensity of T2* images by susceptibility effects. The drop in T2* signal is used to construct an intensity time curve of the lesion.25–28 Different parameters can be calculated from the time intensity curve such as percentages of signal intensity loss30,31 and signal intensity recovery.32 The degree of signal intensity loss and recovery is dependent on many factors related to contrast agent leakage, the size of extravascular space, and the rate of blood flow.30–32 In this work, we applied semiquantitative analysis of time intensity curve with calculation of DSC%. This technique assesses the status of the capillaries and microvessel attenuation of tumors and can measure perfusion and other related hemodynamic parameters of the tumor. Applications of advanced postprocessing techniques with creation of parametric maps such as quantitative regional tumor blood flow map and quantitative assessment of the tumor blood volume will improve the results.24,33 In addition, application of advanced postprocessing of diffusion-weighted MR imaging such as diffusion kurtosis will decrease bias from regionof-interest selection.34,35 There are few limitations to this study. First, the patient population studied is a mixture of pathology with heterogeneous group of tumors at different regions of the head and neck. Further studies that discuss the role of dynamic contrast susceptibility perfusion-weighted MR imaging upon a more homogeneous group such as squamous cell carcinoma would make the results stronger and more applicable. Second, there is no follow-up of the same patients. Further studies are recommended with monitoring patients with radiotherapy after treatment.

CONCLUSIONS We conclude that combination of ADC and DSC% is a noninvasive imaging parameter that can play a role in the differentiation of recurrent head and neck cancer from postradiation changes.

REFERENCES 1. Kessel IL, Blanco A. Update in radiation therapy for oral and maxillofacial tumors and dose mapping. Oral Maxillofac Surg Clin North Am. 2014;26: 223–229. 2. Grégoire V, Jeraj R, Lee J, et al. Radiotherapy for head and neck tumours in 2012 and beyond: conformal, tailored, and adaptive? Lancet Oncol. 2012; 13:e292–e300. 3. Sjövall J, Brun E, Almquist H, et al. Radiotherapy response in head and neck cancer - evaluation of the primary tumour site. Acta Otolaryngol. 2014;134:646–651. 4. Iannessi A, Ouvrier MJ, Thariat J, et al. Imaging in head and neck cancers. Bull Cancer. 2014;101:469–480. 5. Lobert P, Srinivasan A, Shah GV, et al. Postoperative and postradiation changes on imaging. Otolaryngol Clin North Am. 2012;45:1405–1422. 6. Saito N, Nadgir RN, Nakahira M, et al. Posttreatment CT and MR imaging in head and neck cancer: what the radiologist needs to know. Radiographics. 2012;32:1261–1282. 7. King AD, Keung CK, Yu KH, et al. T2-weighted MR imaging early after chemoradiotherapy to evaluate treatment response in head and neck squamous cell carcinoma. AJNR Am J Neuroradiol. 2013;34:1237–1241. 8. Chikui T, Kawano S, Kawazu T, et al. Prediction and monitoring of the response to chemoradiotherapy in oral squamous cell carcinomas using a pharmacokinetic analysis based on the dynamic contrast-enhanced MR imaging findings. Eur Radiol. 2011;21:1699–1708. 9. King AD, Yeung DK, Yu KH, et al. Monitoring of treatment response after chemoradiotherapy for head and neck cancer using in vivo 1H MR spectroscopy. Eur Radiol. 2010;20:165–172. 10. Razek AA, Poptani H. MR spectroscopy of head and neck cancer. Eur J Radiol. 2013;82:982–989. 11. Clavel S, Charron MP, Bélair M, et al. The role of computed tomography in the management of the neck after chemoradiotherapy in patients with head-and-neck cancer. Int J Radiat Oncol Biol Phys. 2012;82: 567–573. 12. Razek AA, Tawfik AM, Elsorogy LG, et al. Perfusion CT of head and neck cancer. Eur J Radiol. 2014;38:537. 13. Tawfik AM, Razek AA, Kerl JM, et al. Comparison of dual-energy CT-derived iodine content and iodine overlay of normal, inflammatory and metastatic squamous cell carcinoma cervical lymph nodes. Eur Radiol. 2014;24:574–580.

FIGURE 4. Receiver operating characteristic curve of ADC, DSC%, and CE%. The thresholds of ADC, DSC%, and CE% used for differentiating recurrence from postradiation changes are 1.07 10−3 mm2/s, 31%, and 0.31 with AUC of 0.929, 0.918, and 0.713, respectively. Figure 4 can be viewed online in color at www.jcat.org.

14. Kostakoglu L, Fardanesh R, Posner M, et al. Early detection of recurrent disease by FDG-PET/CT leads to management changes in patients with squamous cell cancer of the head and neck. Oncologist. 2013;18: 1108–1117. 15. Gupta S, Henningsen G, Wallace M, et al. Percutaneous biopsy of head and neck lesions with CT guidance: various approaches and relevant



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anatomic and technical considerations. Radiographics. 2007;27: 371–390. 16. Razek AA. Diffusion-weighted magnetic resonance imaging of head and neck. J Comput Assist Tomogr. 2010;34:808–815. 17. Thoeny HC, De Keyzer F, King AD. Diffusion-weighted MR imaging in the head and neck. Radiology. 2012;263:19–32. 18. Razek AA, Soliman NY, Elkhamary S, et al. Role of diffusion-weighted MR imaging in cervical lymphadenopathy. Eur Radiol. 2006;16: 1468–1477. 19. Hauser T, Essig M, Jensen A, et al. Prediction of treatment response in head and neck carcinomas using IVIM-DWI: evaluation of lymph node metastasis. Eur J Radiol. 2014;83:783–787. 20. Lai V, Li X, Lee VH, et al. Intravoxel incoherent motion MR imaging: comparison of diffusion and perfusion characteristics between nasopharyngeal carcinoma and post-chemoradiation fibrosis. Eur Radiol. 2013;23:2793–2801. 21. Tshering Vogel DW, Zbaeren P, Geretschlaeger A, et al. Diffusion-weighted MR imaging including bi-exponential fitting for the detection of recurrent or residual tumour after (chemo)radiotherapy for laryngeal and hypopharyngeal cancers. Eur Radiol. 2013;23:562–569.

26. Shin KE, Ahn KJ, Choi HS, et al. DCE and DSC MR perfusion imaging in the differentiation of recurrent tumour from treatment-related changes in patients with glioma. Clin Radiol. 2014;69:e264–e272. 27. Seeger A, Braun C, Skardelly M, et al. Comparison of three different MR perfusion techniques and MR spectroscopy for multiparametric assessment in distinguishing recurrent high-grade gliomas from stable disease. Acad Radiol. 2013;20:1557–1565. 28. Fatterpekar GM, Galheigo D, Narayana A, et al. Treatment-related change versus tumor recurrence in high-grade gliomas: a diagnostic conundrum—use of dynamic susceptibility contrast-enhanced (DSC) perfusion MRI. AJR Am J Roentgenol. 2012;198:19–26. 29. Barajas RF Jr, Chang JS, Segal MR, et al. Differentiation of recurrent glioblastoma multiforme from radiation necrosis after external beam radiation therapy with dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging. Radiology. 2009;253:486–496. 30. Razek AA, Elsorogy LG, Soliman NY, et al. Dynamic susceptibility contrast perfusion MR imaging in distinguishing malignant from benign head and neck tumors: a pilot study. Eur J Radiol. 2011;77:73–79. 31. Abdel Razek AA, Gaballa G. Role of perfusion magnetic resonance imaging in cervical lymphadenopathy. J Comput Assist Tomogr. 2011;35:21–25.

22. Hwang I, Choi SH, Kim YJ, et al. Differentiation of recurrent tumor and posttreatment changes in head and neck squamous cell carcinoma: application of high b-value diffusion-weighted imaging. AJNR Am J Neuroradiol. 2013;34:2343–2348.

32. Mangla R, Kolar B, Zhu T, et al. Percentage signal recovery derived from MR dynamic susceptibility contrast imaging is useful to differentiate common enhancing malignant lesions of the brain. AJNR Am J Neuroradiol. 2011;32:1004–1010.

23. Razek AA, Kandeel AY, 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;28:1146–1152.

33. Knutsson L, Ståhlberg F, Wirestam R. Absolute quantification of perfusion using dynamic susceptibility contrast MRI: pitfalls and possibilities. MAGMA. 2010;23:1–21.

24. Calamante F. Perfusion MRI using dynamic-susceptibility contrast MRI: quantification issues in patient studies. Top Magn Reson Imaging. 2010;21:75–85.

34. Matoba M, Tuji H, Shimode Y, et al. Fractional change in apparent diffusion coefficient as an imaging biomarker for predicting treatment response in head and neck cancer treated with chemoradiotherapy. AJNR Am J Neuroradiol. 2014;35:379–385.

25. Romano A, Rossi Espagnet MC, Calabria LF, et al. Clinical applications of dynamic susceptibility contrast perfusion-weighted MR imaging in brain tumours. Radiol Med. 2012;117:445–460.

35. Abdel Razek AA, Elkhamary S, Al-Mesfer S, et al. Correlation of apparent diffusion coefficient at 3T with prognostic parameters of retinoblastoma. Am J Neuroradiol. 2012;33:944–948.

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