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Potential of MR histogram analyses for prediction of response to chemotherapy in patients with colorectal hepatic metastases

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

Abstract

Purpose

To determine if magnetic resonance imaging (MRI) histogram analyses can help predict response to chemotherapy in patients with colorectal hepatic metastases by using response evaluation criteria in solid tumours (RECIST1.1) as the reference standard.

Materials and methods

Standard MRI including diffusion-weighted imaging (b=0, 500 s/mm2) was performed before chemotherapy in 53 patients with colorectal hepatic metastases. Histograms were performed for apparent diffusion coefficient (ADC) maps, arterial, and portal venous phase images; thereafter, mean, percentiles (1st, 10th, 50th, 90th, 99th), skewness, kurtosis, and variance were generated. Quantitative histogram parameters were compared between responders (partial and complete response, n=15) and non-responders (progressive and stable disease, n=38). Receiver operator characteristics (ROC) analyses were further analyzed for the significant parameters.

Results

The mean, 1st percentile, 10th percentile, 50th percentile, 90th percentile, 99th percentile of the ADC maps were significantly lower in responding group than that in non-responding group (p=0.000–0.002) with area under the ROC curve (AUCs) of 0.76–0.82. The histogram parameters of arterial and portal venous phase showed no significant difference (p>0.05) between the two groups.

Conclusion

Histogram-derived parameters for ADC maps seem to be a promising tool for predicting response to chemotherapy in patients with colorectal hepatic metastases.

Key Points

• ADC histogram analyses can potentially predict chemotherapy response in colorectal liver metastases.

• Lower histogram-derived parameters (mean, percentiles) for ADC tend to have good response.

• MR enhancement histogram analyses are not reliable to predict response.

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Abbreviations

MRI:

magnetic resonance imaging

RECIST:

response evaluation criteria in solid tumours

ADC:

apparent diffusion coefficient

ROC:

receiver operator characteristics

AUCs:

area under the ROC curve

CLM:

colorectal liver metastasis

SOS:

sinusoidal obstruction syndrome

CT:

computed tomography

PET:

positron emission tomography

DWI:

diffusion weighted imaging

TR:

repetition time

TE:

echo time

VIBE:

volumetric interpolated breath-hold examination

DCE:

dynamic contrast enhanced

AP:

arterial phase

PVP:

portal venous phase

ROI:

region of interest

CR:

complete response

PR:

partial response

PD:

progressive disease

SD:

stable disease

PPV:

positive predictive values

NPV:

negative predictive values

References

  1. Manfredi S, Lepage C, Hatem C et al (2006) Epidemiology and management of liver metastases from colorectal cancer. Ann Surg 244(2):254–259

    Article  PubMed  PubMed Central  Google Scholar 

  2. Sofue K, Tsurusaki M, Murakami T et al (2014) Does Gadoxetic acid-enhanced 3.0T MRI in addition to 64-detector-row contrast-enhanced CT provide better diagnostic performance and change the therapeutic strategy for the preoperative evaluation of colorectal liver metastases? Eur Radiol 24(10):2532–2539

    Article  PubMed  Google Scholar 

  3. Scharitzer M, Ba-Ssalamah A, Ringl H et al (2013) Preoperative evaluation of colorectal liver metastases: comparison between gadoxetic acid-enhanced 3.0-T MRI and contrast-enhanced MDCT with histopathological correlation. Eur Radiol 23(8):2187–2196

    Article  CAS  PubMed  Google Scholar 

  4. Nordlinger B, Van Cutsem E, Gruenberger T et al (2009) Combination of surgery and chemotherapy and the role of targeted agents in the treatment of patients with colorectal liver metastases: recommendations from an expert panel. Ann Oncol 20:985–992

    Article  CAS  PubMed  Google Scholar 

  5. Bruera G, Cannita K, Giuliante F et al (2012) Effectiveness of Liver Metastasectomies in Patients With Metastatic Colorectal Cancer Treated With FIr-B/FOx Triplet Chemotherapy Plus Bevacizumab. Clin Colorectal Cancer 11(2):119–126

    Article  CAS  PubMed  Google Scholar 

  6. Nordlinger B, Sorbye H, Glimelius B et al (2008) Perioperative chemotherapy with FOLFOX4 and surgery vs. surgery alone for resectable liver metastases from colorectal cancer (EORTC Intergroup trial 40983): a randomised controlled trial. Lancet 371:1007–1016

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Lehmann K, Rickenbacher A, Weber A, Pestalozzi BC, Clavien PA (2012) Chemotherapy before liver resection of colorectal metastases: friend or foe? Ann Surg 255(2):237–247

    Article  PubMed  Google Scholar 

  8. Robinson SM, Wilson CH, Burt AD et al (2012) Chemotherapy-Associated Liver Injury in Patients with Colorectal Liver Metastases: A Systematic Review and Meta-analysis. Ann Surg Oncol 19:4287–4299

    Article  PubMed  PubMed Central  Google Scholar 

  9. Kneuertz PJ, Maithel SK, Staley CA, Kooby DA (2011) Chemotherapy associated liver injury: impact on surgical management of colorectal liver metastases. Ann Surg Oncol 18:181–190

    Article  PubMed  Google Scholar 

  10. Marsman HA, Van Der Pool AE, Verheij J et al (2011) Hepatic steatosis assessment with CT or MRI in patients with colorectal liver metastases after neoadjuvant chemotherapy. J Surg Oncol 104:10–16

    Article  CAS  PubMed  Google Scholar 

  11. Vauthey JN, Pawlik TM, Ribero D et al (2006) Chemotherapy regimen predicts steatohepatitis and an increase in 90-day mortality after surgery for hepatic colorectal metastases. J Clin Oncol 24:2065–2072

    Article  CAS  PubMed  Google Scholar 

  12. Anzidei M, Napoli A, Zaccagna F et al (2011) Liver Metastases From Colorectal Cancer Treated With Conventional and Antiangiogenetic Chemotherapy: Evaluation With Liver Computed Tomography Perfusion and Magnetic Resonance Diffusion-Weighted Imaging. J Comput Assist Tomogr 35:690–696

    Article  PubMed  Google Scholar 

  13. Kim DH, Kim SH, Im SA et al (2012) Intermodality comparison between 3D perfusion CT and 18F-FDG PET/CT imaging for predicting early tumor response in patients with liver metastasis after chemotherapy: Preliminary results of a prospective study. Eur J Radiol 81:3542–3550

    Article  PubMed  Google Scholar 

  14. De Bruyne S, Van Damme N, Smeets P et al (2012) Value of DCE-MRI and FDG-PET/CT in the prediction of response to preoperative chemotherapy with bevacizumab for colorectal liver metastases. Br J Cancer 106(12):1926–1933

    Article  PubMed  PubMed Central  Google Scholar 

  15. Coenegrachts K, Bols A, Haspeslagh M et al (2012) Prediction and monitoring of treatment effect using T1-weighted dynamic contrast-enhanced magnetic resonance imaging in colorectal liver metastases: Potential of whole tumour ROI and selective ROI analysis. Eur J Radiol 81:3870–3876

    Article  PubMed  Google Scholar 

  16. Cui Y, Zhang XP, Sun YS et al (2008) Apparent Diffusion Coefficient: Potential Imaging Biomarker for Prediction and Early Detection of Response to Chemotherapy in Hepatic Metastases. Radiology 248:894–900

    Article  PubMed  Google Scholar 

  17. Koh DM, Scurr E, Collins D et al (2007) Predicting response of colorectal hepatic metastasis: value of pretreatment apparent diffusion coefficients. AJR Am J Roentgenol 188:1001–1008

    Article  PubMed  Google Scholar 

  18. Kukuk GM, Murtz P, Traber F et al (2014) Diffusion-weighted imaging with acquisition of three b-values for response evaluation of neuroendocrine liver metastases undergoing selective internal radiotherapy. Eur Radiol 24(2):267–276

    Article  PubMed  Google Scholar 

  19. Filss CP, Stoffels G, Galldiks N et al (2014) Histogram analysis reveals a better delineation of tumor volume from background in 18F-FET PET compared to CBV maps in a hybrid PET–MR studie in gliomas. Nucl Inst Methods Phys Res A 734:175–178

    Article  CAS  Google Scholar 

  20. Kim JY, Kim JK, Kim N et al (2008) CT Histogram Analysis: Differentiation of Angiomyolipoma without Visible Fat from Renal Cell Carcinoma at CT Imaging. Radiology 246:472–479

    Article  PubMed  Google Scholar 

  21. Kyriazi S, Collins DJ, Messiou C et al (2011) Metastatic Ovarian and Primary Peritoneal Cancer: Assessing Chemotherapy Response with Diffusion-weighted MR Imaging —Value of Histogram Analysis of Apparent Diffusion Coefficients. Radiology 261:182–192

    Article  PubMed  Google Scholar 

  22. Nowosielski M, Recheis W, Goebel G et al (2011) ADC histograms predict response to anti-angiogenic therapy in patients with recurrent high-grade glioma. Neuroradiology 53:291–302

    Article  PubMed  Google Scholar 

  23. Pope WB, Lai A, Mehta R et al (2011) Apparent Diffusion Coefficient Histogram Analysis Stratifies Progression-Free Survival in Newly Diagnosed Bevacizumab-Treated Glioblastoma. AJNR Am J Neuroradiol 32:882–889

    Article  CAS  PubMed  Google Scholar 

  24. Pope WB, Kim HJ, Huo J et al (2009) Recurrent Glioblastoma Multiforme: ADC Histogram Analysis Predicts Response to Bevacizumab Treatment. Radiology 252:182–189

    Article  PubMed  Google Scholar 

  25. Pope WB, Qiao XJ, Kim HJ et al (2012) Apparent diffusion coefficient histogram analysis stratifies progression-free and overall survival in patients with recurrent GBM treated with bevacizumab: a multi-center study. J Neurooncol 108:491–498

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Szczypiński PM, Strzelecki M, Materka A et al (2009) MaZda--a software package for image texture analysis. Comput Methods Prog Biomed 94(1):66–76

    Article  Google Scholar 

  27. Eisenhauer EA, Therasse P, Bogaerts J et al (2009) New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 45(2):228–247

    Article  CAS  PubMed  Google Scholar 

  28. Lyng H, Haraldseth O, Rofstad EK (2000) Measurement of cell density and necrotic fraction in human melanoma xenografts by diffusion weighted magnetic resonance imaging. Magn Reson Med 43:828–836

    Article  CAS  PubMed  Google Scholar 

  29. Deng J, Rhee TK, Sato KT et al (2006) In vivo diffusion-weighted imaging of liver tumor necrosis in the VX2 rabbit model at 1.5 Tesla. Invest Radiol 41:410–414

    Article  PubMed  Google Scholar 

  30. Herneth AM, Guccione S, Bednarski M (2003) Apparent diffusion coefficient: a quantitative parameter for in vivo tumor characterization. Eur J Radiol 45(3):208–213

    Article  PubMed  Google Scholar 

  31. Rao SX, Chen CZ, Liu H et al (2013) Three-dimensional whole-liver perfusion magnetic resonance imaging in patients with hepatocellular carcinomas and colorectal hepatic metastases. BMC Gastroenterol 13:53

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgments

The scientific guarantor of this publication is Sheng-Xiang Rao. 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. This study has received funding by the National Science Foundation of China (Grant No. 81371543. No complex statistical methods were necessary for this paper. Institutional Review Board approval was obtained. Written informed consent was waived by the Institutional Review Board. Methodology: retrospective, case-control study, performed at one institution.

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Authors and Affiliations

  1. Department of Radiology, Zhongshan Hospital of Fudan University and Shanghai Medical Imaging Institute, No180, Fenglin Road, Xuhui District, Shanghai, 200032, China

    He-Yue Liang, Ya-Qin Huang, Zhao-Xia Yang,  Ying-Ding, Meng-Su Zeng & Sheng-Xiang Rao

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  1. He-Yue Liang
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Liang, HY., Huang, YQ., Yang, ZX. et al. Potential of MR histogram analyses for prediction of response to chemotherapy in patients with colorectal hepatic metastases. Eur Radiol 26, 2009–2018 (2016). https://doi.org/10.1007/s00330-015-4043-2

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  • DOI: https://doi.org/10.1007/s00330-015-4043-2

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