Value of Apparent Diffusion Coefficient Histogram Analysis in the Differential Diagnosis of Nasopharyngeal Lymphoma and Nasopharyngeal Carcinoma Based on Readout-Segmented Diffusion-Weighted Imaging

Overview

Journal Front Oncol

Specialty Oncology

Date 2021 Mar 29

PMID 33777787

Citations 4

Authors

Chengru Song

Peng Cheng

Jingliang Cheng

Yong Zhang

Shanshan Xie

Affiliations

Soon will be listed here.

Abstract

Background: This study aims to explore the utility of whole-lesion apparent diffusion coefficient (ADC) histogram analysis for differentiating nasopharyngeal lymphoma (NPL) from nasopharyngeal carcinoma (NPC) following readout-segmented echo-planar diffusion-weighted imaging (RESOLVE sequence).

Methods: Thirty-eight patients with NPL and 62 patients with NPC, who received routine head-and-neck MRI and RESOLVE (b-value: 0 and 1,000 s/mm) examinations, were retrospectively evaluated as derivation cohort (February 2015 to August 2018); another 23 patients were analyzed as validation cohort (September 2018 to December 2019). The RESOLVE data were obtained from the MAGNETOM Skyra 3T MR system (Siemens Healthcare, Erlangen, Germany). Fifteen parameters derived from the whole-lesion histogram analysis (ADC, variance, skewness, kurtosis, ADC, ADC, ADC, ADC, ADC, ADC, ADC, ADC, ADC, ADC, and ADC) were calculated for each patient. Then, statistical analyses were performed between the two groups to determine the statistical significance of each histogram parameter. A receiver operating characteristic curve (ROC) analysis was conducted to assess the diagnostic performance of each histogram parameter for distinguishing NPL from NPC and further tested in the validation cohort; calibration of the selected parameter was tested with Hosmer-Lemeshow test.

Results: NPL exhibited significantly lower ADC, variance, ADC, ADC, ADC, ADC, ADC, ADC, ADC, ADC, ADC, ADC and ADC, when compared to NPC (all, < 0.05), while no significant differences were found on skewness and kurtosis. Furthermore, ADC revealed the highest diagnostic efficiency, followed by ADC and ADC. Optimal diagnostic performance (AUC = 0.790, sensitivity = 91.9%, and specificity = 63.2%) could be achieved when setting ADC = 1,485.0 × 10 mm/s as the threshold value. The predictive performance was maintained in the validation cohort (AUC = 0.817, sensitivity = 94.6%, and specificity = 56.2%).

Conclusion: Whole-lesion ADC histograms based on RESOLVE are effective in differentiating NPC from NPL.

Citing Articles

Synthetic MRI and diffusion-weighted imaging for differentiating nasopharyngeal lymphoma from nasopharyngeal carcinoma: combination with morphological features.

Zhang H, Hu L, Qin F, Chang J, Zhong Y, Dou W Br J Radiol. 2024; 97(1159):1278-1285.

PMID: 38733577 PMC: 11186575. DOI: 10.1093/bjr/tqae095.

Differentiating nasopharyngeal carcinoma from lymphoma in the head and neck region using the apparent diffusion coefficient (ADC) value: a systematic review and meta-analysis.

Tabnak P, HajiEsmailPoor Z Pol J Radiol. 2023; 88:e472-e482.

PMID: 38020498 PMC: 10660142. DOI: 10.5114/pjr.2023.132172.

Differentiation between pleomorphic adenoma and schwannoma in the parapharyngeal space: histogram analysis of apparent diffusion coefficient.

Kunimatsu N, Kunimatsu A, Miura K, Mori I, Kiryu S Dentomaxillofac Radiol. 2023; 52(7):20230140.

PMID: 37665011 PMC: 10552127. DOI: 10.1259/dmfr.20230140.

The value of whole-lesion histogram analysis based on field‑of‑view optimized and constrained undistorted single shot (FOCUS) DWI for predicting axillary lymph node status in early-stage breast cancer.

Fang S, Zhu J, Wang Y, Zhou J, Wang G, Xu W BMC Med Imaging. 2022; 22(1):163.

PMID: 36088299 PMC: 9464403. DOI: 10.1186/s12880-022-00891-6.

References

Payabvash S, Tihan T, Cha S . Differentiation of Cerebellar Hemisphere Tumors: Combining Apparent Diffusion Coefficient Histogram Analysis and Structural MRI Features. J Neuroimaging. 2018; 28(6):656-665. DOI: 10.1111/jon.12550. View

Guan Y, Shi H, Chen Y, Liu S, Li W, Jiang Z . Whole-Lesion Histogram Analysis of Apparent Diffusion Coefficient for the Assessment of Cervical Cancer. J Comput Assist Tomogr. 2016; 40(2):212-7. DOI: 10.1097/RCT.0000000000000349. View

Schob S, Voigt P, Bure L, Meyer H, Wickenhauser C, Behrmann C . Diffusion-Weighted Imaging Using a Readout-Segmented, Multishot EPI Sequence at 3 T Distinguishes between Morphologically Differentiated and Undifferentiated Subtypes of Thyroid Carcinoma-A Preliminary Study. Transl Oncol. 2016; 9(5):403-410. PMC: 5035355. DOI: 10.1016/j.tranon.2016.09.001. View

Cho S, Kim G, Jang Y, Ryeom H, Kim H, Shin K . Locally advanced rectal cancer: post-chemoradiotherapy ADC histogram analysis for predicting a complete response. Acta Radiol. 2014; 56(9):1042-50. DOI: 10.1177/0284185114550193. View

Schob S, Meyer H, Dieckow J, Pervinder B, Pazaitis N, Hohn A . Histogram Analysis of Diffusion Weighted Imaging at 3T is Useful for Prediction of Lymphatic Metastatic Spread, Proliferative Activity, and Cellularity in Thyroid Cancer. Int J Mol Sci. 2017; 18(4). PMC: 5412405. DOI: 10.3390/ijms18040821. View

Takahashi M, Kozawa E, Tanisaka M, Hasegawa K, Yasuda M, Sakai F . Utility of histogram analysis of apparent diffusion coefficient maps obtained using 3.0T MRI for distinguishing uterine carcinosarcoma from endometrial carcinoma. J Magn Reson Imaging. 2015; 43(6):1301-7. DOI: 10.1002/jmri.25103. View

Surov A, Meyer H, Wienke A . Apparent Diffusion Coefficient for Distinguishing Between Malignant and Benign Lesions in the Head and Neck Region: A Systematic Review and Meta-Analysis. Front Oncol. 2020; 9:1362. PMC: 6960101. DOI: 10.3389/fonc.2019.01362. View

Zhao M, Liu Z, Sha Y, Wang S, Ye X, Pan Y . Readout-segmented echo-planar imaging in the evaluation of sinonasal lesions: A comprehensive comparison of image quality in single-shot echo-planar imaging. Magn Reson Imaging. 2015; 34(2):166-72. DOI: 10.1016/j.mri.2015.10.010. View

Yu X, Hou J, Li F, Xiang W, Lu Q, Hu Y . Quantitative dynamic contrast-enhanced and diffusion-weighted MRI for differentiation between nasopharyngeal carcinoma and lymphoma at the primary site. Dentomaxillofac Radiol. 2016; 45(3):20150317. PMC: 4846150. DOI: 10.1259/dmfr.20150317. View

10.

Wang Y, Xu X, Hu H, Su G, Shen J, Shi H . Histogram analysis of apparent diffusion coefficient maps for the differentiation between lymphoma and metastatic lymph nodes of squamous cell carcinoma in head and neck region. Acta Radiol. 2017; 59(6):672-680. DOI: 10.1177/0284185117730688. View

11.

Kitamoto E, Chikui T, Kawano S, Ohga M, Kobayashi K, Matsuo Y . The application of dynamic contrast-enhanced MRI and diffusion-weighted MRI in patients with maxillofacial tumors. Acad Radiol. 2014; 22(2):210-6. DOI: 10.1016/j.acra.2014.08.016. View

12.

Fong D, Bhatia K, Yeung D, King A . 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(8):603-6. DOI: 10.1016/j.oraloncology.2010.05.004. View

13.

Just N . Improving tumour heterogeneity MRI assessment with histograms. Br J Cancer. 2014; 111(12):2205-13. PMC: 4264439. DOI: 10.1038/bjc.2014.512. View

14.

Rodriguez Gutierrez D, Awwad A, Meijer L, Manita M, Jaspan T, Dineen R . Metrics and textural features of MRI diffusion to improve classification of pediatric posterior fossa tumors. AJNR Am J Neuroradiol. 2013; 35(5):1009-15. PMC: 7964560. DOI: 10.3174/ajnr.A3784. View

15.

Woo S, Cho J, Kim S, Kim S . Histogram analysis of apparent diffusion coefficient map of diffusion-weighted MRI in endometrial cancer: a preliminary correlation study with histological grade. Acta Radiol. 2013; 55(10):1270-7. DOI: 10.1177/0284185113514967. View

16.

Holdsworth S, Skare S, Newbould R, Guzmann R, Blevins N, Bammer R . Readout-segmented EPI for rapid high resolution diffusion imaging at 3 T. Eur J Radiol. 2007; 65(1):36-46. PMC: 3360876. DOI: 10.1016/j.ejrad.2007.09.016. View

17.

Koyasu S, Iima M, Umeoka S, Morisawa N, Porter D, Ito J . The clinical utility of reduced-distortion readout-segmented echo-planar imaging in the head and neck region: initial experience. Eur Radiol. 2014; 24(12):3088-96. DOI: 10.1007/s00330-014-3369-5. View

18.

Wang Y, Wang G, Ye Y, Lu Y, Wu W, Li S . [High resolution diffusion-weighted imaging for characterising nasopharyngeal carcinoma]. Zhonghua Yi Xue Za Zhi. 2017; 97(17):1303-1306. DOI: 10.3760/cma.j.issn.0376-2491.2017.17.006. View

19.

Meyer H, Leifels L, Hamerla G, Hohn A, Surov A . ADC-histogram analysis in head and neck squamous cell carcinoma. Associations with different histopathological features including expression of EGFR, VEGF, HIF-1α, Her 2 and p53. A preliminary study. Magn Reson Imaging. 2018; 54:214-217. DOI: 10.1016/j.mri.2018.07.013. View

20.

Xu X, Qian W, Ma G, Hu H, Su G, Liu H . Combined diffusion-weighted imaging and dynamic contrast-enhanced MRI for differentiating radiologically indeterminate malignant from benign orbital masses. Clin Radiol. 2017; 72(10):903.e9-903.e15. DOI: 10.1016/j.crad.2017.04.007. View