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Identifying enhancement-based staging markers on baseline MRI in patients with colorectal cancer liver metastases undergoing intra-arterial tumor therapy

  • Oncology
  • Published:
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Abstract

Objectives

To determine if three-dimensional whole liver and baseline tumor enhancement features on MRI can serve as staging biomarkers and help predict survival of patients with colorectal cancer liver metastases (CRCLM) more accurately than one-dimensional and non-enhancement-based features.

Methods

This retrospective study included 88 patients with CRCLM, treated with transarterial chemoembolization or Y90 transarterial radioembolization between 2001 and 2014. Semi-automated segmentations of up to three dominant lesions were performed on pre-treatment MRI to calculate total tumor volume (TTV) and total liver volumes (TLV). Quantitative 3D analysis was performed to calculate enhancing tumor volume (ETV), enhancing tumor burden (ETB, calculated as ETV/TLV), enhancing liver volume (ELV), and enhancing liver burden (ELB, calculated as ELV/TLV). Overall and enhancing tumor diameters were also measured. A modified Kaplan-Meier method was used to determine appropriate cutoff values for each metric. The predictive value of each parameter was assessed by Kaplan-Meier survival curves and univariable and multivariable cox proportional hazard models.

Results

All methods except whole liver (ELB, ELV) and one-dimensional/non-enhancement-based methods were independent predictors of survival. Multivariable analysis showed a HR of 2.1 (95% CI 1.3–3.4, p = 0.004) for enhancing tumor diameter, HR 1.7 (95% CI 1.1–2.8, p = 0.04) for TTV, HR 2.3 (95% CI 1.4–3.9, p < 0.001) for ETV, and HR 2.4 (95% CI 1.4–4.0, p = 0.001) for ETB.

Conclusions

Tumor enhancement of CRCLM on baseline MRI is strongly associated with patient survival after intra-arterial therapy, suggesting that enhancing tumor volume and enhancing tumor burden are better prognostic indicators than non-enhancement-based and one-dimensional-based markers.

Key Points

• Tumor enhancement of colorectal cancer liver metastases on MRI prior to treatment with intra-arterial therapies is strongly associated with patient survival.

• Three-dimensional, enhancement-based imaging biomarkers such as enhancing tumor volume and enhancing tumor burden may serve as the basis of a novel prognostic staging system for patients with liver-dominant colorectal cancer metastases.

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Abbreviations

CRC:

Colorectal cancer

CRCLM:

Colorectal cancer liver metastases

ELB:

Enhancing liver burden

ELV:

Enhancing liver volume

ETB:

Enhancing tumor burden

ETV:

Enhancing tumor volume

IAT:

Intra-arterial therapy

TACE:

Transarterial chemoembolization

TARE:

Transarterial radioembolization

TLV:

Total liver volume

TTV:

Total tumor volume

References

  1. Lozano R, Naghavi M, Foreman K et al (2012) Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 380:2095–2128

    Article  Google Scholar 

  2. DeSantis CE, Lin CC, Mariotto AB et al (2014) Cancer treatment and survivorship statistics, 2014. CA Cancer J Clin 64:252–271

    Article  Google Scholar 

  3. Engstrom PF, Arnoletti JP, Benson AB et al (2009) NCCN Clinical Practice Guidelines in Oncology: colon cancer. J Natl Compr Canc Netw 7:778–831

    Article  Google Scholar 

  4. Haddad AJ, Bani Hani M, Pawlik TM, Cunningham SC (2011) Colorectal liver metastases. Int J Surg Oncol 2011. https://doi.org/10.1155/2011/285840

  5. Saltz LB, Douillard JY, Pirotta N et al (2001) Irinotecan plus fluorouracil/leucovorin for metastatic colorectal cancer: a new survival standard. Oncologist 6:81–91

    Article  CAS  Google Scholar 

  6. Fiorentini G, Aliberti C, Tilli M et al (2012) Intra-arterial infusion of irinotecan-loaded drug-eluting beads (DEBIRI) versus intravenous therapy (FOLFIRI) for hepatic metastases from colorectal cancer: final results of a phase III study. Anticancer Res 32:1387–1395

    CAS  PubMed  Google Scholar 

  7. Van Hazel G, Blackwell A, Anderson J et al (2004) Randomised phase 2 trial of SIR-Spheres plus fluorouracil/leucovorin chemotherapy versus fluorouracil/leucovorin chemotherapy alone in advanced colorectal cancer. J Surg Oncol 88:78–85

    Article  Google Scholar 

  8. Hendlisz A, Van den Eynde M, Peeters M et al (2010) Phase III trial comparing protracted intravenous fluorouracil infusion alone or with yttrium-90 resin microspheres radioembolization for liver-limited metastatic colorectal cancer refractory to standard chemotherapy. J Clin Oncol 28:3687–3694

    Article  CAS  Google Scholar 

  9. Poston GJ, Figueras J, Giuliante F et al (2017) Urgent need for a new staging system in advanced colorectal cancer. J Clin Oncol 26:4828–4833

    Article  Google Scholar 

  10. Gonzalez-Guindalini FD, Botelho MPF, Harmath CB et al (2013) Assessment of liver tumor response to therapy: role of quantitative imaging. Radiographics 33:1781–1800

    Article  Google Scholar 

  11. Lin M, Pellerin O, Bhagat N et al (2012) Quantitative and volumetric European association for the study of the liver and response evaluation criteria in solid tumors measurements: feasibility of a semiautomated software method to assess tumor response after transcatheter arterial chemoembolization. J Vasc Interv Radiol 23:1629–1637

    Article  CAS  Google Scholar 

  12. Chapiro J, Duran R, Lin M et al (2015) Early survival prediction after intra-arterial therapies: a 3D quantitative MRI assessment of tumour response after TACE or radioembolization of colorectal cancer metastases to the liver. Eur Radiol 25:1993–2003

    Article  Google Scholar 

  13. Sahu S, Schernthaner R, Ardon R et al (2017) Imaging biomarkers of tumor response in neuroendocrine liver metastases treated with transarterial chemoembolization: can enhancing tumor burden of the whole liver help predict patient survival? Radiology 283:883–894

    Article  Google Scholar 

  14. Tacher V, Lin M, Duran R et al (2016) Comparison of existing response criteria in patients with hepatocellular carcinoma treated with transarterial chemoembolization using a 3D quantitative approach. Radiology 278:275–284

    Article  Google Scholar 

  15. Fleckenstein FN, Schernthaner RE, Duran R et al (2016) 3D Quantitative tumour burden analysis in patients with hepatocellular carcinoma before TACE: comparing single-lesion vs. multi-lesion imaging biomarkers as predictors of patient survival. Eur Radiol 26:3243–3252

    Article  Google Scholar 

  16. Chapiro J, Duran R, Lin M et al (2015) Identifying staging markers for hepatocellular carcinoma before transarterial chemoembolization: comparison of three-dimensional quantitative versus non-three-dimensional imaging markers. Radiology 275:438–447

    Article  Google Scholar 

  17. Bonekamp D, Bonekamp S, Halappa VG et al (2014) Interobserver agreement of semi-automated and manual measurements of functional MRI metrics of treatment response in hepatocellular carcinoma. Eur J Radiol 83:487–496

    Article  Google Scholar 

  18. Tacher V, Lin M, Chao M et al (2013) Semiautomatic volumetric tumor segmentation for hepatocellular carcinoma. Acad Radiol 20:446–452

    Article  Google Scholar 

  19. Chapiro J, Wood LD, Lin M et al (2014) Radiologic-pathologic analysis of contrast-enhanced and diffusion-weighted MR imaging in patients with HCC after TACE: diagnostic accuracy of 3D quantitative image analysis. Radiology 273:746–758

    Article  Google Scholar 

  20. Pellerin O, Lin M, Bhagat N, Ardon R, Mory B, Geschwind J-F (2013) Comparison of semi-automatic volumetric VX2 hepatic tumor segmentation from cone beam CT and multi-detector CT with histology in rabbit models. Acad Radiol 20:115–121

  21. Papademetris X, Jackowski MP, Rajeevan N et al (2006) BioImage Suite: an integrated medical image analysis suite: an update. Insight J 2006:209

    PubMed  PubMed Central  Google Scholar 

  22. Kim S, Mannelli L, Hajdu CH et al (2010) Hepatocellular carcinoma: Assessment of response to transarterial chemoembolization with image subtraction. J Magn Reson Imaging 31:348–355

    Article  Google Scholar 

  23. Contal C, O’Quigley J (1999) An application of changepoint methods in studying the effect of age on survival in breast cancer. Comput Stat Data Anal 30:253–270

    Article  Google Scholar 

  24. Bradburn MJ, Clark TG, Love SB, Altman DG (2003) Survival analysis part III: multivariate data analysis – choosing a model and assessing its adequacy and fit. Br J Cancer 89:605–611

    Article  CAS  Google Scholar 

  25. Levy J, Zuckerman J, Garfinkle R et al (2018) Intra-arterial therapies for unresectable and chemorefractory colorectal cancer liver metastases: a systematic review and meta-analysis. HPB (Oxford) 20:905–915

    Article  Google Scholar 

  26. Van Cutsem E, Cervantes A, Nordlinger B, Arnold D; ESMO Guidelines Working Group (2014) Metastatic colorectal cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol Off J Eur Soc Med Oncol 25(Suppl 3):iii1–iii9

  27. Edwards MS, Chadda SD, Zhao Z, Barber BL, Sykes DP (2012) A systematic review of treatment guidelines for metastatic colorectal cancer. Colorectal Dis 14:e31–e47

  28. Abdalla EK, Vauthey J-N, Ellis LM et al (2004) Recurrence and outcomes following hepatic resection, radiofrequency ablation, and combined resection/ablation for colorectal liver metastases. Ann Surg 239:818–827

    Article  Google Scholar 

  29. Kanematsu M, Kondo H, Goshima S et al (2006) Imaging liver metastases: review and update. Eur J Radiol 58:217–228

    Article  Google Scholar 

  30. Granata V, Catalano O, Fusco R et al (2015) The target sign in colorectal liver metastases: an atypical Gd-EOB-DTPA “uptake” on the hepatobiliary phase of MR imaging. Abdom Imaging 40:2364–2371

    Article  Google Scholar 

  31. Paulatto L, Dioguardi Burgio M, Sartoris R et al (2020) Colorectal liver metastases: radiopathological correlation. Insights Imaging 11:99

    Article  Google Scholar 

  32. Yazdani S, Yusof R, Riazi A, Karimian A (2014) Magnetic resonance image tissue classification using an automatic method. Diagn Pathol 9:207

    Article  Google Scholar 

  33. Knijn N, Mekenkamp LJM, Klomp M et al (2011) KRAS mutation analysis: a comparison between primary tumours and matched liver metastases in 305 colorectal cancer patients. Br J Cancer 104:1020–1026

    Article  CAS  Google Scholar 

Download references

Funding

This work was generously supported by the RSNA Medical Student Research Grant (RMS1608) and the National Institutes of Health (NIH R01-CA206180).

Author information

Authors and Affiliations

  1. Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar St, New Haven, CT, USA

    Mansur A. Ghani, Arash Fereydooni, Evan Chen, Brian Letzen, Fabian Laage-Gaupp, Nariman Nezami, Vinayak Thakur, MingDe Lin, Xenophon Papademetris, Steffen Huber & Julius Chapiro

  2. Russel H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Baltimore, MD, USA

    Nariman Nezami, Ruediger E. Schernthaner, Julius Chapiro, Kelvin Hong & Christos Georgiades

  3. Interventional Radiology and Image-Guided Medicine, Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA

    Nariman Nezami

  4. Yale Center for Analytical Science, Yale School of Public Health, New Haven, CT, USA

    Yanhong Deng & Geliang Gan

  5. Department of Biomedical Engineering, Yale University, New Haven, CT, USA

    Xenophon Papademetris

  6. Department of Diagnostic and Interventional Radiology, Hospital Landstraße, Vienna, Austria

    Ruediger E. Schernthaner

Authors
  1. Mansur A. Ghani
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  2. Arash Fereydooni
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  7. Yanhong Deng
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  10. MingDe Lin
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  11. Xenophon Papademetris
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  12. Ruediger E. Schernthaner
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  13. Steffen Huber
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  14. Julius Chapiro
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  16. Christos Georgiades
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Corresponding author

Correspondence to Julius Chapiro.

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Guarantor

The scientific guarantor of this publication is Julius Chapiro, MD, PhD.

Conflict of interest

MingDe Lin was an employee of Philips Healthcare at the time this research was performed, and is currently an employee of Visage Imaging

Statistics and biometry

Biostatisticians Yanhong Deng, MPH and Geliang Gan, PhD kindly performed statistical calculations for this manuscript and are co-authors of this study.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

Some study subjects or cohorts have been previously reported in Chapiro J, Duran R, Lin M, et al. Early survival prediction after intra-arterial therapies: a 3D quantitative MRI assessment of tumour response after TACE or radioembolization of colorectal cancer metastases to the liver. Eur Radiol. 2015;25(7):1993–2003.

Methodology

Retrospective analysis of prospectively collected data at a single institution

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Ghani, M.A., Fereydooni, A., Chen, E. et al. Identifying enhancement-based staging markers on baseline MRI in patients with colorectal cancer liver metastases undergoing intra-arterial tumor therapy. Eur Radiol 31, 8858–8867 (2021). https://doi.org/10.1007/s00330-021-08058-7

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  • DOI: https://doi.org/10.1007/s00330-021-08058-7

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