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Using Artificial Intelligence to Find the Optimal Margin Width in Hepatectomy for Colorectal Cancer Liver Metastases.
Bertsimas, Dimitris; Margonis, Georgios Antonios; Sujichantararat, Suleeporn; Boerner, Thomas; Ma, Yu; Wang, Jane; Kamphues, Carsten; Sasaki, Kazunari; Tang, Seehanah; Gagniere, Johan; Dupré, Aurelien; Løes, Inger Marie; Wagner, Doris; Stasinos, Georgios; Macher-Beer, Andrea; Burkhart, Richard; Morioka, Daisuke; Imai, Katsunori; Ardiles, Victoria; O'Connor, Juan Manuel; Pawlik, Timothy M; Poultsides, George; Seeliger, Hendrik; Beyer, Katharina; Kaczirek, Klaus; Kornprat, Peter; Aucejo, Federico N; de Santibañes, Eduardo; Baba, Hideo; Endo, Itaru; Lønning, Per Eystein; Kreis, Martin E; Weiss, Matthew J; Wolfgang, Christopher L; D'Angelica, Michael.
Afiliação
  • Bertsimas D; Operations Research Center, Massachusetts Institute of Technology, Cambridge.
  • Margonis GA; Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York.
  • Sujichantararat S; Department of General and Visceral Surgery, Charité Campus Benjamin Franklin, Berlin, Germany.
  • Boerner T; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge.
  • Ma Y; Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York.
  • Wang J; Operations Research Center, Massachusetts Institute of Technology, Cambridge.
  • Kamphues C; Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland.
  • Sasaki K; Department of General and Visceral Surgery, Charité Campus Benjamin Franklin, Berlin, Germany.
  • Tang S; Department of General Surgery, Digestive Disease Institute, Cleveland Clinic, Cleveland, Ohio.
  • Gagniere J; Operations Research Center, Massachusetts Institute of Technology, Cambridge.
  • Dupré A; Service de Chirurgie Digestive, CHU Clermont-Ferrand, Inserm, Université Clermont Auvergne, Clermont-Ferrand, France.
  • Løes IM; Service de Chirurgie Digestive, CHU Clermont-Ferrand, Inserm, Université Clermont Auvergne, Clermont-Ferrand, France.
  • Wagner D; Department of Clinical Science, University of Bergen, Bergen, Norway.
  • Stasinos G; Department of Oncology, Haukeland University Hospital, Bergen, Norway.
  • Macher-Beer A; Department of General Surgery, Medical University of Graz, Graz, Austria.
  • Burkhart R; Technical Chamber of Greece, Athens, Greece.
  • Morioka D; Department of Pathology, Medical University of Vienna, Vienna, Austria.
  • Imai K; Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland.
  • Ardiles V; Department of Gastroenterological Surgery, Yokohama City University Graduate School of Medicine, Yokohama, Japan.
  • O'Connor JM; Department of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan.
  • Pawlik TM; Hepatopancreatobiliary Surgery and Liver Transplant Unit, Hospital Italiano de Buenos Aires, Buenos Aires, Argentina.
  • Poultsides G; Alexander Fleming Institute, Buenos Aires, Argentina.
  • Seeliger H; Department of Surgery, The Ohio State University, Columbus, Ohio.
  • Beyer K; Department of Surgery, Stanford University School of Medicine, Stanford, California.
  • Kaczirek K; Department of General and Visceral Surgery, Charité Campus Benjamin Franklin, Berlin, Germany.
  • Kornprat P; Department of General and Visceral Surgery, Charité Campus Benjamin Franklin, Berlin, Germany.
  • Aucejo FN; Department of General Surgery, Medical University of Vienna, Vienna, Austria.
  • de Santibañes E; Department of General Surgery, Medical University of Graz, Graz, Austria.
  • Baba H; Department of General Surgery, Digestive Disease Institute, Cleveland Clinic, Cleveland, Ohio.
  • Endo I; Hepatopancreatobiliary Surgery and Liver Transplant Unit, Hospital Italiano de Buenos Aires, Buenos Aires, Argentina.
  • Lønning PE; Department of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan.
  • Kreis ME; Department of Gastroenterological Surgery, Yokohama City University Graduate School of Medicine, Yokohama, Japan.
  • Weiss MJ; Department of Clinical Science, University of Bergen, Bergen, Norway.
  • Wolfgang CL; Department of Oncology, Haukeland University Hospital, Bergen, Norway.
  • D'Angelica M; Department of General and Visceral Surgery, Charité Campus Benjamin Franklin, Berlin, Germany.
JAMA Surg ; 157(8): e221819, 2022 08 01.
Article em En | MEDLINE | ID: mdl-35648428
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.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias Colorretais / Neoplasias Hepáticas Tipo de estudo: Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans / Male / Middle aged Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias Colorretais / Neoplasias Hepáticas Tipo de estudo: Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans / Male / Middle aged Idioma: En Ano de publicação: 2022 Tipo de documento: Article