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Quantitative imaging features of pretreatment CT predict volumetric response to chemotherapy in patients with colorectal liver metastases

European Radiology volume 29, pages 458–467 (2019)Cite this article

Abstract

Objectives

This study investigates whether quantitative image analysis of pretreatment CT scans can predict volumetric response to chemotherapy for patients with colorectal liver metastases (CRLM).

Methods

Patients treated with chemotherapy for CRLM (hepatic artery infusion (HAI) combined with systemic or systemic alone) were included in the study. Patients were imaged at baseline and approximately 8 weeks after treatment. Response was measured as the percentage change in tumour volume from baseline. Quantitative imaging features were derived from the index hepatic tumour on pretreatment CT, and features statistically significant on univariate analysis were included in a linear regression model to predict volumetric response. The regression model was constructed from 70% of data, while 30% were reserved for testing. Test data were input into the trained model. Model performance was evaluated with mean absolute prediction error (MAPE) and R2. Clinicopatholologic factors were assessed for correlation with response.

Results

157 patients were included, split into training (n = 110) and validation (n = 47) sets. MAPE from the multivariate linear regression model was 16.5% (R2 = 0.774) and 21.5% in the training and validation sets, respectively. Stratified by HAI utilisation, MAPE in the validation set was 19.6% for HAI and 25.1% for systemic chemotherapy alone. Clinical factors associated with differences in median tumour response were treatment strategy, systemic chemotherapy regimen, age and KRAS mutation status (p < 0.05).

Conclusion

Quantitative imaging features extracted from pretreatment CT are promising predictors of volumetric response to chemotherapy in patients with CRLM. Pretreatment predictors of response have the potential to better select patients for specific therapies.

Key Points

• Colorectal liver metastases (CRLM) are downsized with chemotherapy but predicting the patients that will respond to chemotherapy is currently not possible.

• Heterogeneity and enhancement patterns of CRLM can be measured with quantitative imaging.

• Prediction model constructed that predicts volumetric response with 20% error suggesting that quantitative imaging holds promise to better select patients for specific treatments.

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Abbreviations

ACM:

Angle co-occurrence matrix

CEA:

Carcinoembryonic antigen

CRLM:

Colorectal liver metastases

CRS:

Clinical risk score

ETS:

Early tumour shrinkage

FD:

Fractal dimension

FUDR:

Floxuridine

GLCM:

Grey-level co-occurrence matrix

HAI:

Hepatic artery infusion

IH:

Intensity histogram

LBP:

Local binary pattern

MAPE:

Mean absolute prediction error

RECIST:

Response Evaluation Criteria in Solid Tumours

RLM:

Run-length matrix

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Funding

This study has received funding by NIH/NCI P30 CA008748 Cancer Center Support Grant and the Society for Memorial Sloan Kettering.

Author information

Affiliations

  1. Department of Surgery, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, C-891, New York, NY, 10065, USA

    John M. Creasy, Abhishek Midya, Jayasree Chakraborty, Lauryn B. Adams, Camilla Gomes, Kenneth P. Seastedt, Vinod P. Balachandran, T. Peter Kingham, Peter J. Allen, Ronald P. DeMatteo, William R. Jarnagin, Michael I. D’Angelica & Amber L. Simpson

  2. Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Elizabeth J. Sutton & Richard K. G. Do

  3. Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Andrea Cercek & Nancy E. Kemeny

  4. Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Jinru Shia

  5. Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Mithat Gonen

Authors
  1. John M. Creasy
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  2. Abhishek Midya
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  3. Jayasree Chakraborty
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  4. Lauryn B. Adams
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  5. Camilla Gomes
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  7. Kenneth P. Seastedt
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  8. Elizabeth J. Sutton
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  12. Vinod P. Balachandran
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  13. T. Peter Kingham
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  14. Peter J. Allen
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  17. Michael I. D’Angelica
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  18. Richard K. G. Do
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  19. Amber L. Simpson
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Corresponding author

Correspondence to Amber L. Simpson.

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Guarantor

The scientific guarantor of this publication is Amber L. Simpson, PhD.

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

Mithat Gonen, PhD kindly provided statistical advice for this manuscript.

Informed consent

Written informed consent was waived by the institutional review board.

Ethical approval

Institutional review board approval was obtained.

Methodology

• retrospective

• experimental

• performed at one institution

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Creasy, J.M., Midya, A., Chakraborty, J. et al. Quantitative imaging features of pretreatment CT predict volumetric response to chemotherapy in patients with colorectal liver metastases. Eur Radiol 29, 458–467 (2019). https://doi.org/10.1007/s00330-018-5542-8

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  • DOI: https://doi.org/10.1007/s00330-018-5542-8

Keywords

  • Colorectal neoplasms
  • Multidetector computed tomography
  • Liver
  • Prognosis
  • Models, statistical