Skip to main content
SpringerLink
Log in

Value of combined multiparametric MRI and FDG-PET/CT to identify well-responding rectal cancer patients before the start of neoadjuvant chemoradiation

  • Gastrointestinal
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To explore the value of multiparametric MRI combined with FDG-PET/CT to identify well-responding rectal cancer patients before the start of neoadjuvant chemoradiation.

Methods

Sixty-one locally advanced rectal cancer patients who underwent a baseline FDG-PET/CT and MRI (T2W + DWI) and received long-course neoadjuvant chemoradiotherapy were retrospectively analysed. Tumours were delineated on MRI and PET/CT from which the following quantitative parameters were calculated: T2W volume and entropy, ADC mean and entropy, CT density (mean-HU), SUV maximum and mean, metabolic tumour volume (MTV42%) and total lesion glycolysis (TLG). These features, together with sex, age, mrTN-stage (“baseline parameters”) and the CRT-surgery interval were analysed using multivariable stepwise logistic regression. Outcome was a good (TRG 1–2) versus poor histopathological response. Performance (AUC) to predict response was compared for different combinations of baseline ± quantitative imaging parameters and performance in an ‘independent’ dataset was estimated using bootstrapped leave-one-out cross-validation (LOOCV).

Results

The optimal multivariable prediction model consisted of a combination of baseline + quantitative imaging parameters and included mrT-stage (OR 0.004, p < 0.001), T2W-signal entropy (OR 7.81, p = 0.0079) and T2W volume (OR 1.028, p = 0.0389) as the selected predictors. AUC in the study dataset was 0.88 and 0.83 after LOOCV. No PET/CT features were selected as predictors.

Conclusions

A multivariable model incorporating mrT-stage and quantitative parameters from baseline MRI can aid in identifying well-responding patients before the start of treatment. Addition of FDG-PET/CT is not beneficial.

Key Points

• A multivariable model incorporating the mrT-stage and quantitative features derived from baseline MRI can aid in identifying well-responding patients before the start of neoadjuvant chemoradiotherapy.

• mrT-stage was the strongest predictor in the model and was complemented by the tumour volume and signal entropy calculated from T2W-MRI.

• Adding quantitative features derived from pre-treatment PET/CT or DWI did not contribute to the model’s predictive performance.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Abbreviations

ADC:

Apparent diffusion coefficient

AIC:

Akaike Information Criterion

AUC:

Area under the receiver operating characteristic (ROC) curve

CRT:

Chemoradiotherapy

CT:

Computed tomography

DWI:

Diffusion-weighted imaging

FDG:

Fluorodeoxyglucose; 2-deoxy-2-[18F]fluoro-D-glucose; 18F-FDG

Gy:

Gray

HU:

Hounsfield unit

LARC:

Locally advanced rectal cancer

LOOCV:

Leave-one-out cross-validation

MRI:

Magnetic resonance imaging

MTV:

Metabolic tumour volume

OR:

Odds ratio

PET/CT:

Positron-emission tomography/computed tomography

SUV:

Standardised uptake value

T2W:

T2-weighted

TLG:

Total lesion glycolysis

TRG:

Tumour regression grade

W&W:

Watch-and-wait

References

  1. Maas M, Nelemans PJ, Valentini V et al (2010) Long-term outcome in patients with a pathological complete response after chemoradiation for rectal cancer: a pooled analysis of individual patient data. Lancet Oncol 11:835–844

    Article  PubMed  Google Scholar 

  2. Habr-Gama A, Perez RO, Wynn G, Marks J, Kessler H, Gama-Rodrigues J (2010) Complete clinical response after neoadjuvant chemoradiation therapy for distal rectal cancer: characterization of clinical and endoscopic findings for standardization. Dis Colon Rectum 53:1692–1698

  3. Paun BC, Cassie S, MacLean AR, Dixon E, Buie WD (2010) Postoperative complications following surgery for rectal cancer. Ann Surg 251:807–818

  4. Hendren SK, O’Connor BI, Liu M et al (2005) Prevalence of male and female sexual dysfunction is high following surgery for rectal cancer. Ann Surg 242:212–223

    Article  PubMed  PubMed Central  Google Scholar 

  5. van der Valk MJM, Hilling DE, Bastiaannet E et al (2018) Long-term outcomes of clinical complete responders after neoadjuvant treatment for rectal cancer in the International Watch & Wait Database (IWWD): an international multicentre registry study. Lancet 391:2537–2545

    Article  PubMed  Google Scholar 

  6. Maas M, Beets-Tan RGH, Lambregts DMJ et al (2011) Wait-and-see policy for clinical complete responders after chemoradiation for rectal cancer. J Clin Oncol 29:4633–4640

    Article  PubMed  Google Scholar 

  7. Habr-Gama A, Gama-Rodrigues J, São Julião GP et al (2014) Local recurrence after complete clinical response and watch and wait in rectal cancer after neoadjuvant chemoradiation: impact of salvage therapy on local disease control. Int J Radiat Oncol Biol Phys 88:822–828

    Article  PubMed  Google Scholar 

  8. Smith JD, Ruby JA, Goodman KA et al (2012) Nonoperative management of rectal cancer with complete clinical response after neoadjuvant therapy. Ann Surg 256:965–972

    Article  PubMed  Google Scholar 

  9. Appelt AL, Pløen J, Harling H et al (2015) High-dose chemoradiotherapy and watchful waiting for distal rectal cancer: a prospective observational study. Lancet Oncol 16:919–927

    Article  PubMed  Google Scholar 

  10. Rombouts AJM, Al-Najami I, Abbott NL et al (2017) Can we Save the rectum by watchful waiting or TransAnal microsurgery following (chemo) Radiotherapy versus Total mesorectal excision for early REctal Cancer (STAR-TREC study)?: protocol for a multicentre, randomised feasibility study. BMJ Open 7:e019474

    Article  PubMed  PubMed Central  Google Scholar 

  11. Van Stiphout RGPM, Valentini V, Buijsen J et al (2014) Nomogram predicting response after chemoradiotherapy in rectal cancer using sequential PETCT imaging: a multicentric prospective study with external validation. Radiother Oncol 113:215–222

    Article  PubMed  Google Scholar 

  12. Janssen MHM, Öllers MC, Van Stiphout RGPM et al (2012) PET-based treatment response evaluation in rectal cancer: prediction and validation. Int J Radiat Oncol Biol Phys 82:871–876

    Article  PubMed  Google Scholar 

  13. Maffione AM, Marzola MC, Capirci C, Colletti PM, Rubello D (2015) Value of 18 F-FDG PET for predicting response to neoadjuvant therapy in rectal cancer: systematic review and meta-analysis. AJR Am J Roentgenol 204:1261–1268

  14. Cliffe H, Patel C, Prestwich R, Scarsbrook A (2017) Radiotherapy response evaluation using FDG PET-CT—established and emerging applications. Br J Radiol 90:20160764

    Article  PubMed  PubMed Central  Google Scholar 

  15. Joye I, Debucquoy A, Deroose CM et al (2017) Quantitative imaging outperforms molecular markers when predicting response to chemoradiotherapy for rectal cancer. Radiother Oncol 124:104–109

    Article  PubMed  PubMed Central  Google Scholar 

  16. Schurink NW, Lambregts DM, Beets-Tan RG (2019) Diffusion-weighted imaging in rectal cancer: current applications and future perspectives. Br J Radiol 92:20180655

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  Google Scholar 

  18. Mahadevan LS, Zhong J, Venkatesulu BP et al (2018) Imaging predictors of treatment outcomes in rectal cancer: an overview. Crit Rev Oncol Hematol 129:153–162

    Article  PubMed  Google Scholar 

  19. Meng X, Huang Z, Wang R, Yu J (2014) Prediction of response to preoperative chemoradiotherapy in patients with locally advanced rectal cancer. Biosci Trends 8:11–23

    Article  CAS  PubMed  Google Scholar 

  20. Giannini V, Mazzetti S, Bertotto I et al (2019) Predicting locally advanced rectal cancer response to neoadjuvant therapy with 18 F-FDG PET and MRI radiomics features. Eur J Nucl Med Mol Imaging 46:878–888

    Article  CAS  PubMed  Google Scholar 

  21. Van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77:e104–e107

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Erdi YE, Mawlawi O, Larson SM et al (1997) Segmentation of lung lesion volume by adaptive positron emission tomography image thresholding. Cancer 80:2505–2509

    Article  CAS  PubMed  Google Scholar 

  23. Miccò M, Vargas HA, Burger IA et al (2014) Combined pre-treatment MRI and 18F-FDG PET/CT parameters as prognostic biomarkers in patients with cervical cancer. Eur J Radiol 83:1169–1176

    Article  PubMed  Google Scholar 

  24. Ueno Y, Lisbona R, Tamada T, Alaref A, Sugimura K, Reinhold C (2017) Comparison of FDG PET metabolic tumour volume versus ADC histogram: prognostic value of tumour treatment response and survival in patients with locally advanced uterine cervical cancer. Br J Radiol 90:20170035

  25. Shu Z, Fang S, Ye Q et al (2019) Prediction of efficacy of neoadjuvant chemoradiotherapy for rectal cancer: the value of texture analysis of magnetic resonance images. Abdom Radiol (NY) 21:1051–1058

    Google Scholar 

  26. Greenbaum A, Martin DR, Bocklage T, Lee JH, Ness SA, Rajput A (2019) Tumor heterogeneity as a predictor of response to neoadjuvant chemotherapy in locally advanced rectal cancer. Clin Colorectal Cancer 18:102–109

  27. Bozkaya Y, Özdemir NY, Erdem GU et al (2018) Clinical predictive factors associated with pathologic complete response in locally advanced rectal cancer. J Oncol Sci 4:5–10

    Article  Google Scholar 

  28. Deantonio L, Caroli A, Puta E et al (2018) Does baseline [18F] FDG-PET/CT correlate with tumor staging, response after neoadjuvant chemoradiotherapy, and prognosis in patients with rectal cancer? Radiat Oncol 13:211

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Traverso A, Wee L, Dekker A, Gillies R (2018) Repeatability and reproducibility of radiomic features: a systematic review. Int J Radiat Oncol Biol Phys 102:1143–1158

    Article  PubMed  PubMed Central  Google Scholar 

  30. Mandard A-M, Dalibard F, Mandard J-C et al (1994) Pathologic assessment of tumor regression after preoperative chemoradiotherapy of esophageal carcinoma. Clinicopathologic correlations. Cancer 73:2680–2686

    Article  CAS  PubMed  Google Scholar 

  31. Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19:716–723

    Article  Google Scholar 

  32. Burnham KP, Anderson DR (2004) Multimodel Inference. Sociol Methods Res 33:261–304

    Article  Google Scholar 

  33. Akgun E, Caliskan C, Bozbiyik O et al (2018) Randomized clinical trial of short or long interval between neoadjuvant chemoradiotherapy and surgery for rectal cancer. Br J Surg 105:1417–1425

    Article  CAS  PubMed  Google Scholar 

  34. Joye I, Debucquoy A, Fieuws S et al (2016) Can clinical factors be used as a selection tool for an organ-preserving strategy in rectal cancer? Acta Oncol 55:1047–1052

    Article  PubMed  Google Scholar 

  35. Al-Sukhni E, Attwood K, Mattson DM, Gabriel E, Nurkin SJ (2016) Predictors of pathologic complete response following neoadjuvant chemoradiotherapy for rectal cancer. Ann Surg Oncol 23:1177–1186

  36. Lahaye MJ, Engelen SME, Nelemans PJ et al (2005) Imaging for predicting the risk factors—the circumferential resection margin and nodal disease—of local recurrence in rectal cancer: a meta-analysis. Semin Ultrasound CT MR 26:259–268

    Article  CAS  PubMed  Google Scholar 

  37. Gröne J, Loch FN, Taupitz M, Schmidt C, Kreis ME (2018) Accuracy of various lymph node staging criteria in rectal cancer with magnetic resonance imaging. J Gastrointest Surg 22:146–153

  38. Francois Y, Nemoz CJ, Baulieux J et al (1999) Influence of the interval between preoperative radiation therapy and surgery on downstaging and on the rate of sphincter-sparing surgery for rectal cancer: the Lyon R90-01 randomized trial. J Clin Oncol 17:2396–2396

    Article  CAS  PubMed  Google Scholar 

  39. Kalady MF, de Campos-Lobato LF, Stocchi L et al (2009) Predictive factors of pathologic complete response after neoadjuvant chemoradiation for rectal cancer. Trans Meet Am Surg Assoc 127:213–220

    Google Scholar 

  40. Foster JD, Jones EL, Falk S, Cooper EJ, Francis NK (2013) Timing of surgery after long-course neoadjuvant chemoradiotherapy for rectal cancer: a systematic review of the literature. Dis Colon Rectum 56:921–930

  41. Probst CP, Becerra AZ, Aquina CT et al (2015) Extended intervals after neoadjuvant therapy in locally advanced rectal cancer: the key to improved tumor response and potential organ preservation. J Am Coll Surg 221:430–440

    Article  PubMed  PubMed Central  Google Scholar 

  42. Petrelli F, Sgroi G, Sarti E, Barni S (2016) Increasing the interval between neoadjuvant chemoradiotherapy and surgery in rectal cancer. Ann Surg 263:458–464

    Article  PubMed  Google Scholar 

  43. Meng Y, Zhang C, Zou S et al (2018) MRI texture analysis in predicting treatment response to neoadjuvant chemoradiotherapy in rectal cancer. Oncotarget 9:11999–12008

    Article  PubMed  Google Scholar 

  44. De Cecco CN, Rengo M, Meinel FG et al (2015) Texture analysis as imaging biomarker of tumoral response to neoadjuvant chemoradiotherapy in rectal cancer patients studied with 3-T magnetic resonance. Invest Radiol 50:239–245

    Article  PubMed  CAS  Google Scholar 

  45. Martens MH, Van Heeswijk MM, Van Den Broek JJ et al (2015) Prospective, multicenter validation study of magnetic resonance volumetry for response assessment after preoperative chemoradiation in rectal cancer: can the results in the literature be reproduced? Int J Radiat Oncol Biol Phys 93:1005–1014

    Article  PubMed  Google Scholar 

  46. Lambregts DMJ, Rao S-X, Sassen S et al (2015) MRI and diffusion-weighted MRI volumetry for identification of complete tumor responders after preoperative chemoradiotherapy in patients with rectal cancer. Ann Surg 262:1034–1039

    Article  PubMed  Google Scholar 

  47. Curvo-Semedo L, Lambregts DMJ, Maas M et al (2011) Rectal cancer: assessment of complete response to preoperative combined radiation therapy with chemotherapy—conventional MR volumetry versus diffusion-weighted MR imaging. Radiology 260:734–743

    Article  PubMed  Google Scholar 

  48. Quaia E, Gennari AG, Ricciardi MC et al (2016) Value of percent change in tumoral volume measured at T2 -weighted and diffusion-weighted MRI to identify responders after neoadjuvant chemoradiation therapy in patients with locally advanced rectal carcinoma. J Magn Reson Imaging 44:1415–1424

    Article  PubMed  Google Scholar 

  49. Ha HI, Kim AY, Yu CS, Park SH, Ha HK (2013) Locally advanced rectal cancer: diffusion-weighted MR tumour volumetry and the apparent diffusion coefficient for evaluating complete remission after preoperative chemoradiation therapy. Eur Radiol 23:3345–3353

  50. Young HK, Dae YK, Tae HK et al (2005) Usefulness of magnetic resonance volumetric evaluation in predicting response to preoperative concurrent chemoradiotherapy in patients with resectable rectal cancer. Int J Radiat Oncol Biol Phys 62:761–768

    Article  Google Scholar 

  51. Okuno T, Kawai K, Koyama K et al (2018) Value of FDG–PET/CT volumetry after chemoradiotherapy in rectal cancer. Dis Colon Rectum 61:320–327

    Article  PubMed  Google Scholar 

  52. Dos Anjos DA, Perez RO, Habr-Gama A et al (2016) Semiquantitative volumetry by sequential PET/CT may improve prediction of complete response to neoadjuvant chemoradiation in patients with distal rectal cancer. Dis Colon Rectum 59:805–812

    Article  PubMed  Google Scholar 

  53. Park J, Chang KJ, Seo YS et al (2014) Tumor SUVmax normalized to liver uptake on 18 F-FDG PET/CT predicts the pathologic complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Nucl Med Mol Imaging 48:295–302

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Funding

This study has received funding by the Dutch Cancer Society (project number 10138).

Author information

Author notes

  1. Niels W. Schurink and Lisa A. Min contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands

    Niels W. Schurink, Lisa A. Min, Joost J. M. van Griethuysen, Max J. Lahaye, Monique Maas, Regina G. H. Beets-Tan & Doenja M. J. Lambregts

  2. GROW School for Oncology and Developmental Biology, University of Maastricht, Maastricht, The Netherlands

    Niels W. Schurink, Lisa A. Min, Maaike Berbee, Wouter van Elmpt, Joost J. M. van Griethuysen, Geerard L. Beets & Regina G. H. Beets-Tan

  3. Department of Radiation Oncology (Maastro Clinic), Maastricht University Medical Centre, Maastricht, The Netherlands

    Maaike Berbee & Wouter van Elmpt

  4. Department of Radiology, Maastricht University Medical Center, Maastricht, The Netherlands

    Frans C. H. Bakers

  5. Department of Epidemiology and Biostatistics, The Netherlands Cancer Institute, Amsterdam, The Netherlands

    Sander Roberti

  6. Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands

    Simon R. van Kranen

  7. Department of Surgery, The Netherlands Cancer Institute, Amsterdam, The Netherlands

    Geerard L. Beets

Authors
  1. Niels W. Schurink
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lisa A. Min
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Maaike Berbee
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wouter van Elmpt
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Joost J. M. van Griethuysen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Frans C. H. Bakers
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Sander Roberti
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Simon R. van Kranen
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Max J. Lahaye
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Monique Maas
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Geerard L. Beets
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Regina G. H. Beets-Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Doenja M. J. Lambregts
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Doenja M. J. Lambregts.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Dr. Doenja MJ Lambregts.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors, Mr. Sander Roberti, has significant statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Boards (retrospective analysis of prospectively obtained observational data).

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

Some study subjects or cohorts have been previously reported in:

Cusomano (2018) Radiol Med

Van Stiphout (2014) Radiother Oncol

Janssen (2012) Int J Rad Oncol Biol Phys

Van Stiphout (2011) Radiother Oncol

Janssen (2010) Int J Rad Oncol Biol Phys

Janssen (2010) Radiother Oncol

Lambregts (2011) Ann Surg Oncol

Lambregts (2015) Ann Surg

Martens (2015) Int J Radiat Oncol Biol Phys

Lambregts (2018) Dis Colon rectum

Methodology

• Retrospective

• 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.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Schurink, N.W., Min, L.A., Berbee, M. et al. Value of combined multiparametric MRI and FDG-PET/CT to identify well-responding rectal cancer patients before the start of neoadjuvant chemoradiation. Eur Radiol 30, 2945–2954 (2020). https://doi.org/10.1007/s00330-019-06638-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-019-06638-2

Keywords

Search

Navigation