Skip to main content

Advertisement

Log in

The value of intravoxel incoherent motion diffusion-weighted imaging in predicting the pathologic response to neoadjuvant chemotherapy in locally advanced esophageal squamous cell carcinoma

  • Magnetic Resonance
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objective

To explore the value of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) for the prediction of pathologic response to neoadjuvant chemotherapy (NAC) in locally advanced esophageal squamous cell carcinoma (ESCC).

Material and methods

Forty patients with locally advanced ESCC who were treated with NAC followed by radical resection were prospectively enrolled from September 2015 to May 2018. MRI and IVIM were performed within 1 week before and 2–3 weeks after NAC, prior to surgery. Parameters including apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudodiffusion coefficient (D*), and pseudodiffusion fraction (f) before and after NAC were measured. Pathologic response was evaluated according to the AJCC tumor regression grade (TRG) system. The changes in IVIM values before and after therapy in different TRG groups were assessed. Receiver operating characteristic (ROC) curves analysis was used to determine the best cutoff value for predicting the pathologic response to NAC.

Results

Twenty-two patients were identified as TRG 2 (responders), and eighteen as TRG 3 (non-responders) in pathologic evaluation. The ADC, D, and f values increased significantly after NAC. The post-NAC D and ΔD values of responders were significantly higher than those of non-responders. The area under the curve (AUC) was 0.722 for post-NAC D and 0.859 for ΔD in predicting pathologic response. The cutoff values of post-NAC D and ΔD were 1.685 × 10−3 mm2/s and 0.350 × 10−3 mm2/s, respectively.

Conclusion

IVIM-DWI may be used as an effective functional imaging technique to predict pathologic response to NAC in locally advanced ESCC.

Key Points

• The optimal cutoff values of post-NAC D and ΔD for predicting pathologic response to NAC in locally advanced ESCC were 1.685 × 10−3 mm2/s and 0.350 × 10−3 mm2/s, respectively.

Pathologic response to NAC in locally advanced ESCC was favorable in patients with post-NAC D and ΔD values that were higher than the optimal cutoff values.

IVIM-DWI can potentially be used to preoperatively predict pathologic response to NAC in esophageal carcinoma. Accurate quantification of the D value derived from IVIM-DWI may eventually translate into an effective and non-invasive marker to predict therapeutic efficacy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Abbreviations

18F-FDG PET/CT:

Fluorine-18 fluorodeoxyglucose positron emission tomography-computed tomography

ADC:

Apparent diffusion coefficient

CI:

Confidence intervals

CT:

Computed tomography

D :

True diffusion coefficient

D * :

Pseudodiffusion coefficient

DCE-MRI:

Dynamic contrast-enhanced magnetic resonance images

DWI:

Diffusion-weighted imaging

ESCC:

Esophageal squamous cell carcinoma

EUS:

Endoscopic ultrasonography

f :

Pseudodiffusion fraction

ICC:

Intraclass correlation coefficient

iShim:

Integrated specific slice dynamic shim

IVIM:

Intravoxel incoherent motion

IVIM-DWI:

Intravoxel incoherent motion diffusion-weighted imaging

MRI:

Magnetic resonance imaging

NAC:

Neoadjuvant chemotherapy

NT:

Neoadjuvant therapy

RECIST:

Response evaluation criteria in solid tumors

ROC:

Receiver operating characteristic

ROI:

Region of interest

SD:

Standard deviation

SNR:

Signal-to-noise ratio

TRG:

Tumor regression grade

VIBE:

Volumetric interpolated breath hold examination

References

  1. Shapiro J, van Lanschot JJB, Hulshof MCCM et al (2015) Neoadjuvant chemoradiotherapy plus surgery versus surgery alone for oesophageal or junctional cancer (CROSS): long-term results of a randomised controlled trial. Lancet Oncol 16(9):1090–1098. https://doi.org/10.1016/S1470-2045(15)00040-6

    Article  PubMed  Google Scholar 

  2. Ando N, Kato H, Igaki H et al (2012) A randomized trial comparing postoperative adjuvant chemotherapy with cisplatin and 5-fluorouracil versus preoperative chemotherapy for localized advanced squamous cell carcinoma of the thoracic esophagus (JCOG9907). Ann Surg Oncol 19(1):68–74. https://doi.org/10.1245/s10434-011-2049-9

    Article  PubMed  Google Scholar 

  3. Zheng Y, Liu X, Zhang R et al (2018) Neoadjuvant chemotherapy with or without neoadjuvant radiotherapy compared with neoadjuvant chemoradiotherapy for esophageal cancer. J Thorac Dis 10(8):4715–4723. https://doi.org/10.21037/jtd.2018.07.124

    Article  PubMed  PubMed Central  Google Scholar 

  4. Sendler A (2010) Metabolic response evaluation by PET during neoadjuvant treatment for adenocarcinoma of the esophagus and esophagogastric junction. Recent Results Cancer Res 182:167–177. https://doi.org/10.1007/978-3-540-70579-6_14

    Article  CAS  PubMed  Google Scholar 

  5. Cunningham D, Allum WH, Stenning SP et al (2006) Perioperative chemotherapy versus surgery alone for resectable gastroesophageal cancer. N Engl J Med 355(1):11–20. https://doi.org/10.1056/NEJMoa055531

    Article  CAS  PubMed  Google Scholar 

  6. Griffin Y (2016) Esophageal cancer: role of imaging in primary staging and response assessment post neoadjuvant therapy. Semin Ultrasound CT MR 37(4):339–351. https://doi.org/10.1053/j.sult.2016.02.003

    Article  PubMed  Google Scholar 

  7. Konieczny A, Meyer P, Schnider A et al (2013) Accuracy of multidetector-row CT for restaging after neoadjuvant treatment in patients with oesophageal cancer. Eur Radiol 23(9):2492–2502. https://doi.org/10.1007/s00330-013-2844-8

    Article  PubMed  Google Scholar 

  8. Hamai Y, Hihara J, Emi M et al (2016) Ability of fluorine-18 fluorodeoxyglucose positron emission tomography to predict outcomes of neoadjuvant chemoradiotherapy followed by surgical treatment for esophageal squamous cell carcinoma. Ann Thorac Surg 102(4):1132–1139. https://doi.org/10.1016/j.athoracsur.2016.04.011

    Article  PubMed  Google Scholar 

  9. Joye I, Deroose CM, Vandecaveye V, Haustermans K (2014) The role of diffusion-weighted MRI and (18)F-FDG PET/CT in the prediction of pathologic complete response after radiochemotherapy for rectal cancer: a systematic review. Radiother Oncol 113:158–165. https://doi.org/10.1016/j.radonc.2014.11.026

    Article  PubMed  Google Scholar 

  10. Lei J, Han Q, Zhu S et al (2015) Assessment of esophageal carcinoma undergoing concurrent chemoradiotherapy with quantitative dynamic contrast-enhanced magnetic resonance imaging. Oncol Lett 10(6):3607–3612. https://doi.org/10.3892/ol.2015.3779

    Article  PubMed  PubMed Central  Google Scholar 

  11. Lu Y, Ma L, Qin J et al (2019) The value of GRASP on DCE-MRI for assessing response to neoadjuvant chemotherapy in patients with esophageal cancer. BMC Cancer 19(1):999. https://doi.org/10.1186/s12885-019-6247-3

    Article  PubMed  PubMed Central  Google Scholar 

  12. Biffar A, Dietrich O, Sourbron S, Duerr HR, Reiser MF, Baur-Melnyk A (2010) Diffusion and perfusion imaging of bone marrow. Eur J Radiol 76(3):323–328. https://doi.org/10.1016/j.ejrad.2010.03.011

  13. Iima M, Le Bihan D (2016) Clinical intravoxel incoherent motion and diffusion MR imaging: past, present, and future. Radiology 278(1):13–32. https://doi.org/10.1148/radiol.2015150244

    Article  PubMed  Google Scholar 

  14. Li H, Zhang J, Zheng Z et al (2018) Preoperative histogram analysis of intravoxel incoherent motion (IVIM) for predicting microvascular invasion in patients with single hepatocellular carcinoma. Eur J Radiol 105:65–71. https://doi.org/10.1016/j.ejrad.2018.05.032

    Article  PubMed  Google Scholar 

  15. De Cobelli F, Giganti F, Orsenigo E et al (2013) Apparent diffusion coefficient modifications in assessing gastro-oesophageal cancer response to neoadjuvant treatment: comparison with tumour regression grade at histology. Eur Radiol 23(8):2165–2174. https://doi.org/10.1007/s00330-013-2807-0

    Article  PubMed  Google Scholar 

  16. Donati F, Boraschi P, Pacciardi F et al (2017) 3T diffusion-weighted MRI in the response assessment of colorectal liver metastases after chemotherapy: correlation between ADC value and histological tumour regression grading. Eur J Radiol 91:57–65. https://doi.org/10.1016/j.ejrad.2017.03.020

    Article  PubMed  Google Scholar 

  17. Le Bihan D, Breton E, Lallemand D, Aubin ML, Vignaud J, Laval-Jeantet M (1988) Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology 168(2):497–505. https://doi.org/10.1148/radiology.168.2.3393671

    Article  PubMed  Google Scholar 

  18. Le Bihan D (2019) What can we see with IVIM MRI? Neuroimage 187:56–67. https://doi.org/10.1016/j.neuroimage.2017.12.062

    Article  PubMed  Google Scholar 

  19. Zhang Y, Kuang S, Shan Q et al (2019) Can IVIM help predict HCC recurrence after hepatectomy? Eur Radiol 29(11):5791–5803. https://doi.org/10.1007/s00330-019-06180-1

    Article  PubMed  Google Scholar 

  20. Catanese A, Malacario F, Cirillo L et al (2018) Application of intravoxel incoherent motion (IVIM) magnetic resonance imaging in the evaluation of primitive brain tumours. Neuroradiol J 31(1):4–9. https://doi.org/10.1177/1971400917693025

    Article  CAS  PubMed  Google Scholar 

  21. Wang LL, Lin J, Liu K et al (2014) Intravoxel incoherent motion diffusion-weighted MR imaging in differentiation of lung cancer from obstructive lung consolidation: comparison and correlation with pharmacokinetic analysis from dynamic contrast-enhanced MR imaging. Eur Radiol 24(8):1914–1922. https://doi.org/10.1007/s00330-014-3176-z

    Article  PubMed  Google Scholar 

  22. Baidya Kayal E, Kandasamy D, Khare K, Bakhshi S, Sharma R, Mehndiratta A (2019) Intravoxel incoherent motion (IVIM) for response assessment in patients with osteosarcoma undergoing neoadjuvant chemotherapy. Eur J Radiol 119:108635. https://doi.org/10.1016/j.ejrad.2019.08.004

    Article  PubMed  Google Scholar 

  23. Ding Y, Tan Q, Mao W et al (2019) Differentiating between malignant and benign renal tumors: do IVIM and diffusion kurtosis imaging perform better than DWI? Eur Radiol 29(12):6930–6939. https://doi.org/10.1007/s00330-019-06240-6

    Article  PubMed  Google Scholar 

  24. Foti PV, Privitera G, Piana S et al (2016) Locally advanced rectal cancer: qualitative and quantitative evaluation of diffusion-weighted MR imaging in the response assessment after neoadjuvant chemo-radiotherapy. Eur J Radiol Open 3:145–152. https://doi.org/10.1016/j.ejro.2016.06.003

    Article  PubMed  PubMed Central  Google Scholar 

  25. Fujima N, Yoshida D, Sakashita T et al (2017) Prediction of the treatment outcome using intravoxel incoherent motion and diffusional kurtosis imaging in nasal or sinonasal squamous cell carcinoma patients. Eur Radiol 27(3):956–965. https://doi.org/10.1007/s00330-016-4440-1

    Article  PubMed  Google Scholar 

  26. Zheng H, Ren W, Pan X et al (2018) Role of intravoxel incoherent motion MRI in early assessment of the response of esophageal squamous cell carcinoma to chemoradiotherapy: a pilot study. J Magn Reson Imaging 48(2):349–358. https://doi.org/10.1002/jmri.25934

    Article  PubMed  Google Scholar 

  27. Mandard AM, Dalibard F, Mandard JC et al (1994) Pathologic assessment of tumor regression after preoperative chemoradiotherapy of esophageal carcinoma. Clinicopathologic correlations. Cancer 73(11):2680–2686. https://doi.org/10.1002/1097-0142(19940601)73:11<2680::aid-cncr2820731105>3.0.co;2-c

    Article  CAS  PubMed  Google Scholar 

  28. Koh DM, Collins DJ, Orton MR (2011) Intravoxel incoherent motion in body diffusion-weighted MRI: reality and challenges. AJR Am J Roentgenol 196(6):1351–1361. https://doi.org/10.2214/AJR.10.5515

    Article  PubMed  Google Scholar 

  29. Nougaret S, Vargas HA, Lakhman Y et al (2016) 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 280(2):446–454. https://doi.org/10.1148/radiol.2016150702

    Article  PubMed  PubMed Central  Google Scholar 

  30. Ryan R, Gibbons D, Hyland JM et al (2005) Pathological response following long-course neoadjuvant chemoradiotherapy for locally advanced rectal cancer. Histopathology 47(2):141–146. https://doi.org/10.1111/j.1365-2559.2005.02176.x

    Article  CAS  PubMed  Google Scholar 

  31. Xiang SF, Zhang XQ, Yang SJ et al (2019) Intravoxel incoherent motion magnetic resonance imaging with integrated slice-specific shimming for old myocardial infarction: a pilot study. Sci Rep 9(1):19766. https://doi.org/10.1038/s41598-019-56489-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Zhang H, Xue H, Alto S et al (2016) Integrated shimming improves lesion detection in whole-body diffusion-weighted examinations of patients with plasma disorder at 3 T. Invest Radiol 51(5):297–305. https://doi.org/10.1097/RLI.0000000000000238

    Article  CAS  PubMed  Google Scholar 

  33. Song XL, Kang HK, Jeong GW et al (2016) Intravoxel incoherent motion diffusion-weighted imaging for monitoring chemotherapeutic efficacy in gastric cancer. World J Gastroenterol 22(24):5520–5531. https://doi.org/10.3748/wjg.v22.i24.5520

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Petrillo A, Fusco R, Granata V et al (2018) Assessing response to neo-adjuvant therapy in locally advanced rectal cancer using intra-voxel incoherent motion modelling by DWI data and standardized index of shape from DCE-MRI. Ther Adv Med Oncol 10:1758835918809875. https://doi.org/10.1177/1758835918809875

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. van Rossum PS, van Lier AL, van Vulpen M et al (2015) Diffusion-weighted magnetic resonance imaging for the prediction of pathologic response to neoadjuvant chemoradiotherapy in esophageal cancer. Radiother Oncol 115(2):163–170. https://doi.org/10.1016/j.radonc.2015.04.027

    Article  PubMed  Google Scholar 

  36. Li QW, Qiu B, Wang B et al (2018) Prediction of pathologic responders to neoadjuvant chemoradiotherapy by diffusion-weighted magnetic resonance imaging in locally advanced esophageal squamous cell carcinoma: a prospective study. Dis Esophagus 31(2):1–7. https://doi.org/10.1093/dote/dox121

    Article  PubMed  Google Scholar 

  37. Bae JS, Kim SH, Hur BY et al (2019) Prognostic value of MRI in assessing extramural venous invasion in rectal cancer: multi-readers’ diagnostic performance. Eur Radiol 29(8):4379–4388. https://doi.org/10.1007/s00330-018-5926-9

    Article  PubMed  Google Scholar 

  38. Hauser T, Essig M, Jensen A et al (2013) Characterization and therapy monitoring of head and neck carcinomas using diffusion-imaging-based intravoxel incoherent motion parameters-preliminary results. Neuroradiology 55(5):527–536. https://doi.org/10.1007/s00234-013-1154-9

    Article  PubMed  Google Scholar 

  39. Andreou A, Koh DM, Collins DJ et al (2013) Measurement reproducibility of perfusion fraction and pseudodiffusion coefficient derived by intravoxel incoherent motion diffusion-weighted MR imaging in normal liver and metastases. Eur Radiol 23(2):428–434. https://doi.org/10.1007/s00330-012-2604-1

    Article  CAS  PubMed  Google Scholar 

  40. Jerome NP, Miyazaki K, Collins DJ et al (2017) Repeatability of derived parameters from histograms following non-Gaussian diffusion modelling of diffusion-weighted imaging in a paediatric oncological cohort. Eur Radiol 27(1):345–353. https://doi.org/10.1007/s00330-016-4318-2

    Article  PubMed  Google Scholar 

  41. Cohen AD, Schieke MC, Hohenwalter MD, Schmainda KM (2015) The effect of low b-values on the intravoxel incoherent motion derived pseudodiffusion parameter in liver. Magn Reson Med 73(1):306–311. https://doi.org/10.1002/mrm.25109

    Article  PubMed  Google Scholar 

  42. Perucho JAU, Chang HCC, Vardhanabhuti V et al (2020) B-value optimization in the estimation of intravoxel incoherent motion parameters in patients with cervical cancer. Korean J Radiol 21(2):218–227. https://doi.org/10.3348/kjr.2019.0232

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

We thank Prof. Ihab R. Kamel for valuable advices on revised manuscript and language editing.

Funding

This study has received funding by the National Natural Science Foundation of China (81972802) and National nature science foundation of Henan Province (182300410355).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jinrong Qu.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Jinrong QU, MD, PHD.

Conflict of interest

Two of the authors of this manuscript (Shaoyu Wang and Xu Yan) are employees of Siemens. The remaining authors declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

Yan Zhao, one of the authors has significant statistical expertise.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• prospective

• diagnostic or prognostic study

• performed at one institution

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Song, T., Yao, Q., Qu, J. et al. The value of intravoxel incoherent motion diffusion-weighted imaging in predicting the pathologic response to neoadjuvant chemotherapy in locally advanced esophageal squamous cell carcinoma. Eur Radiol 31, 1391–1400 (2021). https://doi.org/10.1007/s00330-020-07248-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-020-07248-z

Keywords

Navigation