European Radiology

, Volume 23, Issue 8, pp 2165–2174 | Cite as

Apparent diffusion coefficient modifications in assessing gastro-oesophageal cancer response to neoadjuvant treatment: comparison with tumour regression grade at histology

  • Francesco De Cobelli
  • Francesco Giganti
  • Elena Orsenigo
  • Michaela Cellina
  • Antonio Esposito
  • Giulia Agostini
  • Luca Albarello
  • Elena Mazza
  • Alessandro Ambrosi
  • Carlo Socci
  • Carlo Staudacher
  • Alessandro Del Maschio
Gastrointestinal
First Online:
Received:
Revised:
Accepted:

Abstract

Objectives

To assess changes in apparent diffusion coefficient (ΔADC) and volume (ΔV) after neoadjuvant treatment (NT), and tumour regression grade (TRG) in gastro-oesophageal cancers (GEC), and to discriminate responders from non-responders.

Methods

Thirty-two patients with biopsy-proven locally-advanced GEC underwent diffusion weighted magnetic resonance imaging (DWI) pre- and post-NT. Lesion ADC, volume, ΔADC and ΔV were calculated. TRG 1-2-3 patients were classified as R; TRG 4-5 as non-responders. ΔADC-TRG and ΔV-TRG correlations, pre-NT and post-NT ADC, ΔADC and ΔV cut-off values for responders and non-responders were calculated. Two readers measured mean tumour ADCs and interobserver variability was calculated. (Spearman’s and intraclass correlation coefficient [ICC]).

Results

The interobserver reproducibility was very good both for pre-NT (Spearman’s rho = 0.8160; ICC = 0.8993) and post-NT (Spearman’s rho = 0.8357; ICC = 0.8663). Responders showed lower pre-NT ADC (1.32 versus 1.63 × 10−3 mm2/s; P = 0.002) and higher post-NT ADC (2.22 versus 1.51 × 10−3 mm2/s; P = 0.001) than non-responders and ADC increased in responders (ΔADC, 85.45 versus −8.21 %; P = 0.00005). ΔADC inversely correlated with TRG (r = −0.71, P = 0.000004); no difference in ΔV between responders and non-responders (−50.92 % versus −14.12 %; P = 0.068) and no correlation ΔV-TRG (r = 0.02 P = 0.883) were observed.

Conclusions

The ADC can be used to assess gastro-oesophageal tumour response to neoadjuvant treatment as a reliable expression of tumour regression.

Key Points

• DWI is now being used to assess many cancers.

• Change in ADC measurements offer new information about oesophageal tumours.

• ADC changes are more reliable than dimensional criteria in assessing neoadjuvant treatment.

• Such ADC assessment could optimise management of locally advanced gastro-oesophageal cancers.

Keywords

Diffusion magnetic resonance imaging Gastroesophageal cancer Apparent diffusion coefficient Response to neoadjuvant therapy Diagnosis 
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References

  1. 1.
    Cunningham D, Allum WH, Stenning SP et al (2006) Perioperative chemotherapy versus surgery alone for resectable gastroesophageal cancer. N Eng J Med 355:11–20CrossRefGoogle Scholar
  2. 2.
    Sendler A (2010) Metabolic response evaluation by PET during neoadjuvant treatment for adenocarcinoma of the esophagus and esophagogastric junction. Recent Results Cancer Res 182:167–177PubMedCrossRefGoogle Scholar
  3. 3.
    Law S, Fok M, Chow S, Chu KM, Wong J (1997) Preoperative chemotherapy versus surgical therapy alone for squamous cell carcinoma of the esophagus: a prospective randomized trial. J Thorac Cardiovasc Surg 114:210–217PubMedCrossRefGoogle Scholar
  4. 4.
    Shimada H, Okazumi S, Koyama M, Japanese MK (2011) Gastric cancer association task force for research promotion: clinical utility of 18F-fluoro-2-deoxyglucose positron emission tomography in gastric and esophageal cancer. Cancer 14:13–21Google Scholar
  5. 5.
    Westerterp M, Van Westreenen HL, Sloof GW, Plukker JT, Van Lanschot JJ (2006) Role of positron emission tomography in the (re-)staging of oesophageal cancer. Scand J Gastroenterol Suppl 2006:116–122Google Scholar
  6. 6.
    Brücher BL, Weber W, Bauer M et al (2001) Neoadjuvant therapy of esophageal squamous cell carcinoma: response evaluation by positron emission tomography. Ann Surg 233:300–309PubMedCrossRefGoogle Scholar
  7. 7.
    Smithers BM, Couper GC, Thomas JM et al (2008) Positron emission tomography and pathological evidence of response to neoadjuvant therapy in adenocarcinoma of the esophagus. Dis Esophagus 21:151–158PubMedCrossRefGoogle Scholar
  8. 8.
    Stahl M, Stuschke M, Lehmann N et al (2005) Chemoradiation with and without surgery in patients with locally advanced squamous cell carcinoma of the esophagus. J Clin Oncol 23:2310–2317PubMedCrossRefGoogle Scholar
  9. 9.
    Ott K, Fink U, Becker K et al (2003) Prediction of response to preoperative chemotherapy in gastric carcinoma by metabolic imaging: results of a prospective trial. J Clin Oncol 21:4604–4610PubMedCrossRefGoogle Scholar
  10. 10.
    Padhani AR (2002) Functional MRI for anticancer therapy assessment. Eur J Cancer 38:2116–2127PubMedCrossRefGoogle Scholar
  11. 11.
    Koh DM, Padhani AR (2006) Diffusion-weighted MRI: a new functional clinical technique for tumour imaging. Br J Radiol 79:633–635PubMedCrossRefGoogle Scholar
  12. 12.
    Koh DM, Scurr E, Collins D, Kanber B, Norman A, Leach MO, Husband JE (2007) Predicting response of colorectal hepatic metastasis: value of pretreatment apparent diffusion coefficients. AJR Am J Roentgenol 188:1001–1008PubMedCrossRefGoogle Scholar
  13. 13.
    Figueiras RG, Goh V, Padhani AR, Naveira AB, Caamaño AG, Martin CV (2010) The role of functional imaging in colorectal cancer. AJR Am J Roentgenol 195:54–66PubMedCrossRefGoogle Scholar
  14. 14.
    Harry VN (2010) Novel imaging techniques as response biomarkers in cervical cancer. Gynecol Oncol 116:253–261PubMedCrossRefGoogle Scholar
  15. 15.
    Hein PA, Kremser C, Judmaier W et al (2003) Diffusion-weighted magnetic resonance imaging for monitoring diffusion changes in rectal carcinoma during combined, preoperative chemoradiation: preliminary results of a prospective study. Eur J Radiol 45:214–222PubMedCrossRefGoogle Scholar
  16. 16.
    Kim SH, Lee JY, Lee JM, Han JK, Choi BI (2011) Apparent diffusion coefficient for evaluating tumour response to neoadjuvant chemoradiation therapy for locally advanced rectal cancer. Eur Radiol 21:987–995PubMedCrossRefGoogle Scholar
  17. 17.
    Mandard AM, Dalibard F, Mandard JC et al (1994) Pathologic assessment of tumor regression after preoperative chemoradiotherapy of esophageal carcinoma. Clinicopathologic correlations. Cancer 73:2680–2686PubMedCrossRefGoogle Scholar
  18. 18.
    Hermann RM, Horstmann O, Haller F et al (2006) Histomorphological tumor regression grading of esophageal carcinoma after neoadjuvant radiochemotherapy: which score to use? Dis Esophagus 19:329–334PubMedCrossRefGoogle Scholar
  19. 19.
    Vecchio FM, Valentini V, Minsky BD et al (2005) The relationship of pathologic tumor regression grade (TRG) and outcomes after preoperative therapy in rectal cancer. Int J Radiat Oncol Biol Phys 62:752–760PubMedCrossRefGoogle Scholar
  20. 20.
    Siewert JR, Stein HJ (1998) Classification of adenocarcinoma of the oesophagogastric junction. Br J Surg 85:1457–1459PubMedCrossRefGoogle Scholar
  21. 21.
    Barbaro B, Fiorucci C, Tebala C et al (2009) Locally advanced rectal cancer: MR imaging in prediction of response after preoperative chemotherapy and radiation therapy. Radiology 250:730–739PubMedCrossRefGoogle Scholar
  22. 22.
    Lambrecht M, Vandecaveye V, De Keyzer F et al (2012) Value of diffusion-weighted magnetic resonance imaging for prediction and early assessment of response to neoadjuvant radiochemotherapy in rectal cancer. Int J Radiat Oncol Biol Phys 82:863–870PubMedCrossRefGoogle Scholar
  23. 23.
    Aoyagi T, Shuto K, Okazumi S, Shimada H, Kazama T, Matsubara H (2011) Apparent diffusion coefficient values measured by diffusion-weighted imaging predict chemoradiotherapeutic effect for advanced esophageal cancer. Dig Surgery 28:252–257CrossRefGoogle Scholar
  24. 24.
    Sun YS, Zhang XP, Tang L, Ji JF, Gu J, Cai Y, Zhang XY (2010) Locally advanced rectal carcinoma treated with preoperative chemotherapy and radiation therapy: preliminary analysis of diffusion-weighted MR imaging for early detection of tumor histopathologic downstaging. Radiology 254:170–178PubMedCrossRefGoogle Scholar
  25. 25.
    Dzik-Jurasz A, Domenig C, George M, Wolber J, Padhani A, Brown G, Doran S (2002) Diffusion MRI for prediction of response of rectal cancer to chemoradiation. Lancet 360:307–308PubMedCrossRefGoogle Scholar
  26. 26.
    Cui Y, Zhang XP, Sun YS, Tang L, Shen L (2008) Apparent diffusion coefficient: potential imaging biomarker for prediction and early detection of response to chemotherapy in hepatic metastases. Radiology 248:894–900PubMedCrossRefGoogle Scholar
  27. 27.
    Tang L, Zhang XP, Sun YS, Shen L, Li J, Qi LP, Cui Y (2011) Gastrointestinal stromal tumors treated with imatinib mesylate: apparent diffusion coefficient in the evaluation of therapy response in patients. Radiology 258:729–738PubMedCrossRefGoogle Scholar
  28. 28.
    Park SH, Moon WK, Cho N et al (2010) Diffusion-weighted MR imaging: pretreatment prediction of response to neoadjuvant chemotherapy in patients with breast cancer. Radiology 257:56–63PubMedCrossRefGoogle Scholar
  29. 29.
    Dudeck O, Zeile M, Pink D et al (2008) Diffusion-weighted magnetic resonance imaging allows monitoring of anticancer treatment effects in patients with soft-tissue sarcomas. J Magn Reson Imaging 27:1109–1113PubMedCrossRefGoogle Scholar
  30. 30.
    Sun YS, Cui Y, Tang L et al (2011) Early evaluation of cancer response by a new functional biomarker: apparent diffusion coefficient. AJR Am J Roentgenol 197:23–29CrossRefGoogle Scholar
  31. 31.
    Yankeelov TE, Lepage M, Chakravarthy A et al (2007) Integration of quantitative DCE-MRI and ADC mapping to monitor treatment response in human breast cancer: initial results. Magn Reson Imaging 25:1–13PubMedCrossRefGoogle Scholar
  32. 32.
    Forastiere AA, Ang K, Brizel D et al (2005) National comprehensive cancer network. Head and neck cancers. J Natl Compr Cancer Netw 3:316–391Google Scholar
  33. 33.
    Therasse P, Arbuck SG, Eisenhauer EA et al (2000) New guidelines to evaluate the response to treatment in solid tumors. European Organization for Research and Treatment of Cancer, National Cancer Institute of the United States, National Cancer Institute of Canada. J Natl Cancer Inst 92:205–216PubMedCrossRefGoogle Scholar
  34. 34.
    Lee KC, Moffat BA, Schott AF et al (2007) Prospective early response imaging biomarker for neoadjuvant breast cancer chemotherapy. Clin Cancer Res 13:443–450PubMedCrossRefGoogle Scholar
  35. 35.
    Harry VN, Semple SI, Gilbert FJ, Parkin DE (2008) Diffusion-weighted magnetic resonance imaging in the early detection of response to chemoradiation in cervical cancer. Gynecol Oncol 111:213–220PubMedCrossRefGoogle Scholar
  36. 36.
    Lambregts DM, Beets GL, Maas M, Curvo-Semedo L, Kessels AG, Thywissen T, Beets-Tan RG (2012) Tumour ADC measurements in rectal cancer: effect of ROI methods on ADC values and interobserver variability. Eur Radiol 21:2567–2574CrossRefGoogle Scholar
  37. 37.
    Edge SB, Byrd DR, Compton CC, Fritz AG, Greene FL, Trotti A (eds) (2010) AJCC cancer staging manual, 7th edn. Springer, New YorkGoogle Scholar

Copyright information

© European Society of Radiology 2013

Authors and Affiliations

  • Francesco De Cobelli
    • 1
    • 7
  • Francesco Giganti
    • 1
  • Elena Orsenigo
    • 2
  • Michaela Cellina
    • 3
  • Antonio Esposito
    • 1
  • Giulia Agostini
    • 1
  • Luca Albarello
    • 4
  • Elena Mazza
    • 5
  • Alessandro Ambrosi
    • 6
  • Carlo Socci
    • 2
  • Carlo Staudacher
    • 2
  • Alessandro Del Maschio
    • 1
  1. 1.Department of Radiology and Center for Experimental Imaging, San Raffaele Scientific InstituteVita-Salute UniversityMilanItaly
  2. 2.Department of Surgery, San Raffaele Scientific InstituteVita-Salute UniversityMilanItaly
  3. 3.Department of RadiologyOspedale Fatebenefratelli e OftalmicoMilanItaly
  4. 4.Pathology UnitSan Raffaele Scientific InstituteMilanItaly
  5. 5.Department of OncologySan Raffaele Scientific InstituteMilanItaly
  6. 6.Neurobiology of Learning UnitVita-Salute San Raffaele UniversityMilanItaly
  7. 7.Department of Radiology, San Raffaele Scientific InstituteVita-Salute UniversityMilanoItaly

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