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ASO Author Reflections: Development and Validation of a Novel Risk Score Using Machine-Learning Methodology to Predict Recurrence After Hepatectomy for Colorectal Liver Metastases

  • ASO Author Reflections
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References

  1. Brudvik KW, Jones RP, Giuliante F, et al. RAS mutation clinical risk score to predict survival after resection of colorectal liver metastases. Ann Surg. 2019;269:120–6.

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  2. Fong Y, Fortner J, Sun RL, Brennan MF, Blumgart LH. Clinical score for predicting recurrence after hepatic resection for metastatic colorectal cancer: analysis of 1001 consecutive cases. Ann Surg. 1999;230:309–18; discussion 318–21.

  3. Paredes AZ, Hyer JM, Tsilimigras DI, et al. A novel machine-learning approach to predict recurrence after resection of colorectal liver metastases. Ann Surg Oncol. 2020. https://doi.org/10.1245/s10434-020-08991-9.

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Authors and Affiliations

  1. Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA

    Anghela Z. Paredes MD, MPH, MS, Diamantis I. Tsilimigras MD & Timothy M. Pawlik MD, MPH, MTS, PhD

Authors
  1. Anghela Z. Paredes MD, MPH, MS
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  2. Diamantis I. Tsilimigras MD
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  3. Timothy M. Pawlik MD, MPH, MTS, PhD
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Corresponding author

Correspondence to Timothy M. Pawlik MD, MPH, MTS, PhD.

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Paredes, A.Z., Tsilimigras, D.I. & Pawlik, T.M. ASO Author Reflections: Development and Validation of a Novel Risk Score Using Machine-Learning Methodology to Predict Recurrence After Hepatectomy for Colorectal Liver Metastases. Ann Surg Oncol 27, 5148–5149 (2020). https://doi.org/10.1245/s10434-020-08995-5

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  • DOI: https://doi.org/10.1245/s10434-020-08995-5

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