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Development and Validation of a Machine-Learning Model to Predict Early Recurrence of Intrahepatic Cholangiocarcinoma

  • Hepatobiliary Tumors
  • Published:
Annals of Surgical Oncology Aims and scope Submit manuscript

Abstract

Background

The high incidence of early recurrence after hepatectomy for intrahepatic cholangiocarcinoma (ICC) has a detrimental effect on overall survival (OS). Machine-learning models may improve the accuracy of outcome prediction for malignancies.

Methods

Patients who underwent curative-intent hepatectomy for ICC were identified using an international database. Three machine-learning models were trained to predict early recurrence (< 12 months after hepatectomy) using 14 clinicopathologic characteristics. The area under the receiver operating curve (AUC) was used to assess their discrimination ability.

Results

In this study, 536 patients were randomly assigned to training (n = 376, 70.1%) and testing (n = 160, 29.9%) cohorts. Overall, 270 (50.4%) patients experienced early recurrence (training: n = 150 [50.3%] vs testing: n = 81 [50.6%]), with a median tumor burden score (TBS) of 5.6 (training: 5.8 [interquartile range {IQR}, 4.1–8.1] vs testing: 5.5 [IQR, 3.7–7.9]) and metastatic/undetermined nodes (N1/NX) in the majority of the patients (training: n = 282 [75.0%] vs testing n = 118 [73.8%]). Among the three different machine-learning algorithms, random forest (RF) demonstrated the highest discrimination in the training/testing cohorts (RF [AUC, 0.904/0.779] vs support vector machine [AUC, 0.671/0.746] vs logistic regression [AUC, 0.668/0.745]). The five most influential variables in the final model were TBS, perineural invasion, microvascular invasion, CA 19-9 lower than 200 U/mL, and N1/NX disease. The RF model successfully stratified OS relative to the risk of early recurrence.

Conclusions

Machine-learning prediction of early recurrence after ICC resection may inform tailored counseling, treatment, and recommendations. An easy-to-use calculator based on the RF model was developed and made available online.

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Author information

Authors and Affiliations

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

    Laura Alaimo MD, Henrique A. Lima MD, Zorays Moazzam MD, Yutaka Endo MD, PhD, Jason Yang BA & Timothy M. Pawlik MD, PhD, MPH, MTS, MBA, FACS, FRACS (Hon)

  2. Department of Surgery, University of Verona, Verona, Italy

    Laura Alaimo MD, Andrea Ruzzenente MD, PhD & Alfredo Guglielmi MD

  3. Department of Surgery, Ospedale San Raffaele, Milan, Italy

    Luca Aldrighetti MD

  4. Department of Surgery, Johns Hopkins Hospital, Baltimore, MD, USA

    Matthew Weiss MD

  5. Department of Surgery, University of Virginia, Charlottesville, VA, USA

    Todd W. Bauer MD

  6. Department of Surgery, Fundeni Clinical Institute, Bucharest, Romania

    Sorin Alexandrescu MD

  7. Department of Surgery, Stanford University, Stanford, CA, USA

    George A. Poultsides MD

  8. Department of Surgery, Emory University, Atlanta, GA, USA

    Shishir K. Maithel MD

  9. Department of Surgery, Curry Cabral Hospital, Lisbon, Portugal

    Hugo P. Marques MD

  10. Department of Surgery, University of Ottawa, Ottawa, ON, Canada

    Guillaume Martel MD

  11. Department of Surgery, Royal Prince Alfred Hospital, University of Sydney, Sydney, NSW, Australia

    Carlo Pulitano MD

  12. Department of Surgery, Eastern Hepatobiliary Surgery Hospital, Shanghai, China

    Feng Shen MD

  13. Department of Hepatobiliopancreatic Surgery and Liver Transplantation, AP-HP, Beaujon Hospital, Clichy, France

    François Cauchy MD

  14. Department of Surgery, Erasmus University Medical Centre, Rotterdam, The Netherlands

    Bas Groot Koerkamp MD

  15. Department of Gastroenterological Surgery, Yokohama City University School of Medicine, Yokohama, Japan

    Itaru Endo MD, PhD

  16. Department of Surgery, Keio University, Tokyo, Japan

    Minoru Kitago MD

  17. Department of Surgery, The Urban Meyer III and Shelley Meyer Chair for Cancer Research, The Ohio State University, Wexner Medical Center, Columbus, OH, USA

    Timothy M. Pawlik MD, PhD, MPH, MTS, MBA, FACS, FRACS (Hon)

Authors
  1. Laura Alaimo MD
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  2. Henrique A. Lima MD
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  18. François Cauchy MD
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  22. Timothy M. Pawlik MD, PhD, MPH, MTS, MBA, FACS, FRACS (Hon)
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Corresponding author

Correspondence to Timothy M. Pawlik MD, PhD, MPH, MTS, MBA, FACS, FRACS (Hon).

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Disclosure

Dr. Hugo Marques has ownership of ophiomics precision medicine and is a consultant for JNJ and Roche. Dr. Guillaume Martel receives speaker’s honorarium from Incyte Biosciences. The remaining author have no conflicts of interest.

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Alaimo, L., Lima, H.A., Moazzam, Z. et al. Development and Validation of a Machine-Learning Model to Predict Early Recurrence of Intrahepatic Cholangiocarcinoma. Ann Surg Oncol 30, 5406–5415 (2023). https://doi.org/10.1245/s10434-023-13636-8

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