Original Investigation 

June 1, 2022

Using Artificial Intelligence to Find the Optimal Margin Width in Hepatectomy for Colorectal Cancer Liver Metastases

Dimitris Bertsimas, Georgios Antonios Margonis, Suleeporn Sujichantararat, et al

JAMA Surg. 2022;157(8):e221819. doi:10.1001/jamasurg.2022.1819

Key Points

Question  What is the optimal margin width for hepatectomy for colorectal cancer liver metastases and should it be individualized based on patient characteristics?

Findings  In this cohort study of 1843 patients (internal cohort, 965; external cohort, 878), state-of-the-art random forest predictive models were trained; these models had the highest areas under the curve reported in the colorectal cancer liver metastases literature. Artificial intelligence– and game theory–based techniques built on these predictive models inferred that the margin width associated with the maximal decrease in 5-year death probability was 7 mm.

Meaning  This cohort study did not provide evidence for heterogeneous treatment outcomes but rather suggests a single optimal margin width (7 mm) for hepatectomy for KRAS-variant colorectal liver metastases.

Abstract

Importance  In patients with resectable colorectal cancer liver metastases (CRLM), the choice of surgical technique and resection margin are the only variables that are under the surgeon’s direct control and may influence oncologic outcomes. There is currently no consensus on the optimal margin width.

Objective  To determine the optimal margin width in CRLM by using artificial intelligence–based techniques developed by the Massachusetts Institute of Technology and to assess whether optimal margin width should be individualized based on patient characteristics.

Design, Setting, and Participants  The internal cohort of the study included patients who underwent curative-intent surgery for KRAS-variant CRLM between January 1, 2000, and December 31, 2017, at Johns Hopkins Hospital, Baltimore, Maryland, Memorial Sloan Kettering Cancer Center, New York, New York, and Charité–University of Berlin, Berlin, Germany. Patients from institutions in France, Norway, the US, Austria, Argentina, and Japan were retrospectively identified from institutional databases and formed the external cohort of the study. Data were analyzed from April 15, 2019, to November 11, 2021.

Exposures  Hepatectomy.

Main Outcomes and Measures  Patients with KRAS-variant CRLM who underwent surgery between 2000 and 2017 at 3 tertiary centers formed the internal cohort (training and testing). In the training cohort, an artificial intelligence–based technique called optimal policy trees (OPTs) was used by building on random forest (RF) predictive models to infer the margin width associated with the maximal decrease in death probability for a given patient (ie, optimal margin width). The RF component was validated by calculating its area under the curve (AUC) in the testing cohort, whereas the OPT component was validated by a game theory–based approach called Shapley additive explanations (SHAP). Patients from international institutions formed an external validation cohort, and a new RF model was trained to externally validate the OPT-based optimal margin values.

Results  This cohort study included a total of 1843 patients (internal cohort, 965; external cohort, 878). The internal cohort included 386 patients (median [IQR] age, 58.3 [49.0-68.7] years; 200 men [51.8%]) with KRAS-variant tumors. The AUC of the RF counterfactual model was 0.76 in both the internal training and testing cohorts, which is the highest ever reported. The recommended optimal margin widths for patient subgroups A, B, C, and D were 6, 7, 12, and 7 mm, respectively. The SHAP analysis largely confirmed this by suggesting 6 to 7 mm for subgroup A, 7 mm for subgroup B, 7 to 8 mm for subgroup C, and 7 mm for subgroup D. The external cohort included 375 patients (median [IQR] age, 61.0 [53.0-70.0] years; 218 men [58.1%]) with KRAS-variant tumors. The new RF model had an AUC of 0.78, which allowed for a reliable external validation of the OPT-based optimal margin. The external validation was successful as it confirmed the association of the optimal margin width of 7 mm with a considerable prolongation of survival in the external cohort.

Conclusions and Relevance  This cohort study used artificial intelligence–based methodologies to provide a possible resolution to the long-standing debate on optimal margin width in CRLM.