Using machine learning to preoperatively stratify prognosis among patients with gallbladder cancer: a multi-institutional analysis. Issue 11 (November 2022)

• Record Type: Journal Article
• Authors: Cotter, Garrett; Beal, Eliza W.; Poultsides, George A.; Idrees, Kamran; Fields, Ryan C.; Weber, Sharon M.; Scoggins, Charles R.; Shen, Perry; Wolfgang, Christopher; Maithel, Shishir K.; Pawlik, Timothy M.
• Is Part Of: HPB. Volume 24:Issue 11(2022)
• Page Start: 1980
• Page End: 1988
• Publication Date: 2022-11
• Subjects: Liver -- Diseases -- Periodicals; Biliary tract -- Diseases -- Periodicals; Pancreas -- Diseases -- Periodicals; 616.362005
• DOI: 10.1016/j.hpb.2022.06.008
• Languages: English
• ISSNs: 1365-182X

Abstract

Abstract: Background: Gallbladder cancer (GBC) is an aggressive malignancy associated with a high risk of recurrence and mortality. We used a machine-based learning approach to stratify patients into distinct prognostic groups using preperative variables. Methods: Patients undergoing curative-intent resection of GBC were identified using a multi-institutional database. A classification and regression tree (CART) was used to stratify patients relative to overall survival (OS) based on preoperative clinical factors. Results: CART analysis identified tumor size, biliary drainage, carbohydrate antigen 19-9 (CA19-9) levels, and neutrophil-lymphocyte ratio (NLR) as the factors most strongly associated with OS. Machine learning cohorted patients into four prognostic groups: Group 1 ( n = 109): NLR ≤1.5, CA19-9 ≤20, no drainage, tumor size <5.0 cm; Group 2 ( n = 88): NLR >1.5, CA19-9 ≤20, no drainage, tumor size <5.0 cm; Group 3 ( n = 46): CA19-9 >20, no drainage, tumor size <5.0 cm; Group 4 ( n = 77): tumor size <5.0 cm with drainage OR tumor size ≥5.0 cm. Median OS decreased incrementally with CART group designation (59.5, 27.6, 20.6, and 12.1 months; p < 0.0001). Conclusions: A machine-based model was able to stratify GBC patients into four distinct prognostic groups based only on preoperative characteristics. Characterizing patient prognosis with machine learning tools may help physicians provide more patient-centered care.