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Synthetic MRI and amide proton transfer–weighted MRI for differentiating between benign and malignant sinonasal lesions

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Abstract

Objectives

To explore the value of the synthetic MRI (SyMRI), combined with amide proton transfer–weighted (APTw) MRI for quantitative and morphologic assessment of sinonasal lesions, which could provide relative scale for the quantitative assessment of tissue properties.

Methods

A total of 80 patients (31 malignant and 49 benign) with sinonasal lesions, who underwent the SyMRI and APTw examination, were retrospectively analyzed. Quantitative parameters (T1, T2, proton density (PD)) and APT % were obtained through outlining the region of interest (ROI) and comparing the two groups utilizing independent Student t test or a Wilcoxon test. Receiver operating characteristic curve (ROC), Delong test, and logistic regression analysis were performed to assess the diagnostic efficiency of one-parameter and multiparametric models.

Results

SyMRI-derived mean T1, T2, and PD were significantly higher and APT % was relatively lower in benign compared to malignant sinonasal lesions (p < 0.05). The ROC analysis showed that the AUCs of the SyMRI-derived quantitative (T1, T2, PD) values and APT % ranged from 0.677 to 0.781 for differential diagnosis between benign and malignant sinonasal lesions. The T2 values showed the best diagnostic performance among all single parameters for differentiating these two masses. The AUCs of combined SyMRI-derived multiple parameters with APT % (AUC = 0.866) were the highest than that of any single parameter, which was significantly improved (p < 0.05).

Conclusion

The combination of SyMRI and APTw imaging has the potential to reflect intrinsic tissue characteristics useful for differentiating benign from malignant sinonasal lesions.

Clinical relevance statement

Combining synthetic MRI with amide proton transfer–weighted imaging could function as a quantitative and contrast-free approach, significantly enhancing the differentiation of benign and malignant sinonasal lesions and overcoming the limitations associated with the superficial nature of endoscopic nasal sampling.

Key Points

• Synthetic MRI and amide proton transfer–weighted MRI could differentiate benign from malignant sinonasal lesions based on quantitative parameters.

• The diagnostic efficiency could be significantly improved through synthetic MRI + amide proton transfer–weighted imaging.

• The combination of synthetic MRI and amide proton transfer–weighted MRI is a noninvasive method to evaluate sinonasal lesions.

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Abbreviations

APTw:

Amide proton transfer–weighted

AUC:

Area under the curve

cMRI:

Conventional MRI

CT:

Computed tomography

H&E:

Hematoxylin-eosin staining

ICC:

Intraclass correlation coefficient

PD:

Proton density

ROC:

Receiver operating characteristic curve

ROIs:

Regions of interest

SyMRI:

Synthetic MRI

References

  1. Slootweg PJ, Ferlito A, Cardesa A et al (2013) Sinonasal tumors: a clinicopathologic update of selected tumors. Eur Arch Otorhinolaryngol 270:5–20. https://doi.org/10.1007/s00405-012-2025-4

    Article  PubMed  Google Scholar 

  2. Sen S, Chandra A, Mukhopadhyay S, Ghosh P (2015) Sinonasal tumors: computed tomography and MR imaging features. Neuroimaging Clin N Am 25:595–618. https://doi.org/10.1016/j.nic.2015.07.006

    Article  PubMed  Google Scholar 

  3. Eggesbø HB (2012) Imaging of sinonasal tumours. Cancer Imaging 12:136–152. https://doi.org/10.1102/1470-7330.2012.0015

    Article  PubMed  Google Scholar 

  4. Wang XY, Yan F, Hao H et al (2015) Improved performance in differentiating benign from malignant sinonasal tumors using diffusion-weighted combined with dynamic contrast-enhanced magnetic resonance imaging. Chin Med J (Engl) 128:586–592. https://doi.org/10.4103/0366-6999.151649

    Article  PubMed  Google Scholar 

  5. Jégoux F, Métreau A, Louvel G, Bedfert C (2013) Paranasal sinus cancer. Eur Ann Otorhinolaryngol Head Neck Dis 130:327–335. https://doi.org/10.1016/j.anorl.2012.07.007

    Article  PubMed  Google Scholar 

  6. Carta F, Blancal JP, Verillaud B et al (2013) Surgical management of inverted papilloma: approaching a new standard for surgery. Head Neck 35:1415–1420. https://doi.org/10.1002/hed.23159

    Article  PubMed  Google Scholar 

  7. Valente G, Mamo C, Bena A et al (2006) Prognostic significance of microvessel density and vascular endothelial growth factor expression in sinonasal carcinomas. Hum Pathol 37:391–400. https://doi.org/10.1016/j.humpath.2005.11.021

    Article  CAS  PubMed  Google Scholar 

  8. Airoldi M, Garzaro M, Valente G et al (2009) Clinical and biological prognostic factors in 179 cases with sinonasal carcinoma treated in the Italian Piedmont region. Oncology 76:262–269. https://doi.org/10.1159/000206140

    Article  PubMed  Google Scholar 

  9. Cellina M, Gibelli D, Floridi C et al (2020) Sphenoid sinuses: pneumatisation and anatomical variants-what the radiologist needs to know and report to avoid intraoperative complications. Surg Radiol Anat 42:1013–1024. https://doi.org/10.1007/s00276-020-02490-y

    Article  PubMed  Google Scholar 

  10. Madani G, Beale TJ, Lund VJ (2009) Imaging of sinonasal tumors. Semin Ultrasound CT MR 30:25–38. https://doi.org/10.1053/j.sult.2008.10.013

    Article  PubMed  Google Scholar 

  11. Wang P, Tang Z, Xiao Z et al (2022) Dual-energy CT in differentiating benign sinonasal lesions from malignant ones: comparison with simulated single-energy CT, conventional MRI, and DWI. Eur Radiol 32:1095–1105. https://doi.org/10.1007/s00330-021-08159-3

    Article  PubMed  Google Scholar 

  12. Razek AA, Sieza S, Maha B (2009) Assessment of nasal and paranasal sinus masses by diffusion-weighted MR imaging. J Neuroradiol 36:206–211. https://doi.org/10.1016/j.neurad.2009.06.001

    Article  CAS  PubMed  Google Scholar 

  13. Martínez Barbero JP, Rodríquez Jiménez I, Martin Noguerol T, Luna Alcalá A (2013) Utility of MRI diffusion techniques in the evaluation of tumors of the head and neck. Cancers (Basel) 5:875–889. https://doi.org/10.3390/cancers5030875

    Article  PubMed  Google Scholar 

  14. Abdel Razek AA, Gaballa G, Elhawarey G et al (2009) Characterization of pediatric head and neck masses with diffusion-weighted MR imaging. Eur Radiol 19:201–208. https://doi.org/10.1007/s00330-008-1123-6

    Article  PubMed  Google Scholar 

  15. Xiao Z, Tang Z, Qiang J et al (2018) Intravoxel incoherent motion MR imaging in the differentiation of benign and malignant sinonasal lesions: comparison with conventional diffusion-weighted MR imaging. AJNR Am J Neuroradiol 39:538–546. https://doi.org/10.3174/ajnr.A5532

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Xiao Z, Zhong Y, Tang Z et al (2018) Standard diffusion-weighted, diffusion kurtosis and intravoxel incoherent motion MR imaging of sinonasal malignancies: correlations with Ki-67 proliferation status. Eur Radiol 28:2923–2933. https://doi.org/10.1007/s00330-017-5286-x

    Article  PubMed  Google Scholar 

  17. Jiang JX, Tang ZH, Zhong YF, Qiang JW (2017) Diffusion kurtosis imaging for differentiating between the benign and malignant sinonasal lesions. J Magn Reson Imaging 45:1446–1454. https://doi.org/10.1002/jmri.25500

    Article  PubMed  Google Scholar 

  18. Su GY, Xu YK, Liu J et al (2023) Texture analysis of diffusion kurtosis imaging for differentiating malignant from benign sinonasal lesions: added value to conventional imaging features. Br J Radiol 96:20220806. https://doi.org/10.1259/bjr.20220806

    Article  PubMed  Google Scholar 

  19. Karkuzhali P, Gnanaguruparan A, Bhattachryya M (2006) Psammomatoid ossifying fibroma of sinonasal tract. Otolaryngol Head Neck Surg 134:705–707. https://doi.org/10.1016/j.otohns.2005.03.077

    Article  CAS  PubMed  Google Scholar 

  20. van Rijswijk CS, Kunz P, Hogendoorn PC et al (2002) Diffusion-weighted MRI in the characterization of soft-tissue tumors. J Magn Reson Imaging 15:302–307. https://doi.org/10.1002/jmri.10061

    Article  PubMed  Google Scholar 

  21. White ML, Zhang Y, Robinson RA (2006) Evaluating tumors and tumorlike lesions of the nasal cavity, the paranasal sinuses, and the adjacent skull base with diffusion-weighted MRI. J Comput Assist Tomogr 30:490–495. https://doi.org/10.1097/00004728-200605000-00023

    Article  PubMed  Google Scholar 

  22. Wang P, Hu S, Wang X et al (2023) Synthetic MRI in differentiating benign from metastatic retropharyngeal lymph node: combination with diffusion-weighted imaging. Eur Radiol 33:152–161. https://doi.org/10.1007/s00330-022-09027-4

    Article  PubMed  Google Scholar 

  23. Yang F, Li Y, Lei H et al (2023) Histogram analysis of synthetic magnetic resonance imaging: correlations with histopathological factors in head and neck squamous cell carcinoma. Eur J Radiol 160:110715. https://doi.org/10.1016/j.ejrad.2023.110715

    Article  PubMed  Google Scholar 

  24. Yang F, Li X, Li Y et al (2023) Histogram analysis of quantitative parameters from synthetic MRI: correlations with prognostic factors in nasopharyngeal carcinoma. Eur Radiol 33:5344–5354. https://doi.org/10.1007/s00330-023-09553-9

    Article  CAS  PubMed  Google Scholar 

  25. Li M, Fu W, Ouyang L et al (2023) Potential clinical feasibility of synthetic MRI in bladder tumors: a comparative study with conventional MRI. Quant Imaging Med Surg 13:5109–5118. https://doi.org/10.21037/qims-22-1419

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Li Q, Xiao Q, Yang M et al (2021) Histogram analysis of quantitative parameters from synthetic MRI: correlations with prognostic factors and molecular subtypes in invasive ductal breast cancer. Eur J Radiol 139:109697. https://doi.org/10.1016/j.ejrad.2021.109697

    Article  PubMed  Google Scholar 

  27. Cui Y, Han S, Liu M et al (2020) Diagnosis and grading of prostate cancer by relaxation maps from synthetic MRI. J Magn Reson Imaging 52:552–564. https://doi.org/10.1002/jmri.27075

    Article  PubMed  Google Scholar 

  28. Zhu K, Chen Z, Cui L et al (2022) The preoperative diagnostic performance of multi-parametric quantitative assessment in rectal carcinoma: a preliminary study using synthetic magnetic resonance imaging. Front Oncol 12:682003. https://doi.org/10.3389/fonc.2022.682003

    Article  PubMed  PubMed Central  Google Scholar 

  29. Konar AS, Paudyal R, Shah AD et al (2022) Qualitative and quantitative performance of magnetic resonance image compilation (MAGiC) method: an exploratory analysis for head and neck imaging. Cancers (Basel) 14:3624. https://doi.org/10.3390/cancers14153624

    Article  PubMed  Google Scholar 

  30. Peng Y, Zou X, Chen G et al (2023) Chemical shift-encoded sequence (IDEAL-IQ) and amide proton transfer (APT) MRI for prediction of histopathological factors of rectal cancer. Bioengineering (Basel) 10:720. https://doi.org/10.3390/bioengineering10060720

    Article  CAS  PubMed  Google Scholar 

  31. Wang HJ, Cai Q, Huang YP et al (2022) Amide proton transfer-weighted MRI in predicting histologic grade of bladder cancer. Radiology 305:127–134. https://doi.org/10.1148/radiol.211804

    Article  PubMed  Google Scholar 

  32. Yuan J, Chen S, King AD et al (2014) Amide proton transfer-weighted imaging of the head and neck at 3 T: a feasibility study on healthy human subjects and patients with head and neck cancer. NMR Biomed 27:1239–1247. https://doi.org/10.1002/nbm.3184

    Article  PubMed  PubMed Central  Google Scholar 

  33. Law BKH, King AD, Ai QY et al (2018) Head and neck tumors: amide proton transfer MRI. Radiology 288:782–790. https://doi.org/10.1148/radiol.2018171528

    Article  PubMed  Google Scholar 

  34. Ma C, Tian S, Song Q et al (2023) Amide proton transfer-weighted imaging combined with intravoxel incoherent motion for evaluating microsatellite instability in endometrial cancer. J Magn Reson Imaging 57:493–505. https://doi.org/10.1002/jmri.28287

    Article  PubMed  Google Scholar 

  35. Chen W, Liu G, Chen J et al (2023) Whole-tumor amide proton transfer-weighted imaging histogram analysis to predict pathological extramural venous invasion in rectal adenocarcinoma: a preliminary study. Eur Radiol 33:5159–5171. https://doi.org/10.1007/s00330-023-09418-1

    Article  PubMed  Google Scholar 

  36. Yu L, Li C, Luo X et al (2019) Differentiation of malignant and benign head and neck tumors with amide proton transfer-weighted MR imaging. Mol Imaging Biol 21:348–355. https://doi.org/10.1007/s11307-018-1248-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Han Y, Wang W, Yang Y et al (2020) Amide proton transfer imaging in predicting isocitrate dehydrogenase 1 mutation status of grade II/III gliomas based on support vector machine. Front Neurosci 14:144. https://doi.org/10.3389/fnins.2020.00144

    Article  PubMed  PubMed Central  Google Scholar 

  38. Zhang Z, Li S, Wang W et al (2023) Synthetic MRI for the quantitative and morphologic assessment of head and neck tumors: a preliminary study. Dentomaxillofac Radiol 52:20230103. https://doi.org/10.1259/dmfr.20230103

    Article  PubMed  Google Scholar 

  39. Meng N, Wang X, Sun J et al (2020) Application of the amide proton transfer-weighted imaging and diffusion kurtosis imaging in the study of cervical cancer. Eur Radiol 30:5758–5767. https://doi.org/10.1007/s00330-020-06884-9

    Article  CAS  PubMed  Google Scholar 

  40. Bobak CA, Barr PJ, O’Malley AJ (2018) Estimation of an inter-rater intra-class correlation coefficient that overcomes common assumption violations in the assessment of health measurement scales. BMC Med Res Methodol 18:93. https://doi.org/10.1186/s12874-018-0550-6

    Article  PubMed  PubMed Central  Google Scholar 

  41. Zhao L, Liang M, Xie L et al (2021) Prediction of pathological prognostic factors of rectal cancer by relaxation maps from synthetic magnetic resonance imaging. Eur J Radiol 138:109658. https://doi.org/10.1016/j.ejrad.2021.109658

    Article  PubMed  Google Scholar 

  42. Zhao L, Liang M, Wang S et al (2021) Preoperative evaluation of extramural venous invasion in rectal cancer using radiomics analysis of relaxation maps from synthetic MRI. Abdom Radiol (NY) 46:3815–3825. https://doi.org/10.1007/s00261-021-03021-y

    Article  PubMed  Google Scholar 

  43. Yang F, Wei H, Li X et al (2023) Pretreatment synthetic magnetic resonance imaging predicts disease progression in nonmetastatic nasopharyngeal carcinoma after intensity modulation radiation therapy. Insights Imaging 14:59. https://doi.org/10.1186/s13244-023-01411-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Duchaussoy T, Budzik JF, Norberciak L et al (2019) Synthetic T2 mapping is correlated with time from stroke onset: a future tool in wake-up stroke management? Eur Radiol 29:7019–7026. https://doi.org/10.1007/s00330-019-06270-0

    Article  PubMed  Google Scholar 

  45. Jung Y, Gho SM, Back SN et al (2018) The feasibility of synthetic MRI in breast cancer patients: comparison of T(2) relaxation time with multiecho spin echo T(2) mapping method. Br J Radiol 92:20180479. https://doi.org/10.1259/bjr.20180479

    Article  PubMed  PubMed Central  Google Scholar 

  46. Cai Q, Wen Z, Huang Y et al (2021) Investigation of synthetic magnetic resonance imaging applied in the evaluation of the tumor grade of bladder cancer. J Magn Reson Imaging 54:1989–1997. https://doi.org/10.1002/jmri.27770

    Article  PubMed  Google Scholar 

  47. Raz E, Win W, Hagiwara M et al (2015) Fungal sinusitis. Neuroimaging Clin N Am 25:569–576. https://doi.org/10.1016/j.nic.2015.07.004

    Article  PubMed  Google Scholar 

  48. Wang YZ, Yang BT, Wang ZC, Song L, Xian JF (2012) MR evaluation of sinonasal angiomatous polyp. AJNR Am J Neuroradiol 33:767–772. https://doi.org/10.3174/ajnr.A2856

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Schmidt H, Schwenzer NF, Gatidis S et al (2016) Systematic evaluation of amide proton chemical exchange saturation transfer at 3 T: effects of protein concentration, pH, and acquisition parameters. Invest Radiol 51:635–646. https://doi.org/10.1097/rli.0000000000000292

    Article  CAS  PubMed  Google Scholar 

  50. Wang F, Xiang YS, Wu P, Shen AJ, Wang PJ (2023) Evaluation of amide proton transfer imaging for bladder cancer histopathologic features: a comparative study with diffusion- weighted imaging. Eur J Radiol 159:110664. https://doi.org/10.1016/j.ejrad.2022.110664

    Article  PubMed  Google Scholar 

  51. Yamada I, Yoshino N, Hikishima K et al (2017) Colorectal carcinoma: ex vivo evaluation using 3-T high-spatial-resolution quantitative T2 mapping and its correlation with histopathologic findings. Magn Reson Imaging 38:174–181. https://doi.org/10.1016/j.mri.2016.12.028

    Article  PubMed  Google Scholar 

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Acknowledgements

The authors acknowledge the support received from the GE Healthcare.

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Authors and Affiliations

  1. Department of Radiology, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China

    Ying Xiang, Qiujuan Zhang, Xin Chen, Honghong Sun, Xiaohui Li, Jinman Zhong, Bo Gao, Wei Huang, Wenbin Liang, Haiqiao Sun & Quanxin Yang

  2. GE Healthcare, MR Research China, Beijing, China

    Xiaocheng Wei

  3. Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China

    Xiaoyong Ren

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  1. Ying Xiang
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  2. Qiujuan Zhang
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  3. Xin Chen
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Correspondence to Quanxin Yang or Xiaoyong Ren.

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The scientific guarantor of this publication is Prof. Xiaoyong Ren, MD, The Second Affiliated Hospital of Xi’an Jiaotong University.

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One of the authors of this manuscript (X.W.) is an employee of GE Healthcare. The remaining authors declare no relationships with any companies whose products or services may be related to the subject matter of the article.

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Xiang, Y., Zhang, Q., Chen, X. et al. Synthetic MRI and amide proton transfer–weighted MRI for differentiating between benign and malignant sinonasal lesions. Eur Radiol (2024). https://doi.org/10.1007/s00330-024-10696-6

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