Diamantis I Tsilimigras1, Rittal Mehta1, Dimitrios Moris1, Kota Sahara1, Fabio Bagante1, Anghela Z Paredes1, Amika Moro1, Alfredo Guglielmi2, Luca Aldrighetti3, Matthew Weiss4, Todd W Bauer5, Sorin Alexandrescu6, George A Poultsides7, Shishir K Maithel8, Hugo P Marques9, Guillaume Martel10, Carlo Pulitano11, Feng Shen12, Olivier Soubrane13, Bas Groot Koerkamp14, Itaru Endo15, Timothy M Pawlik16,17. 1. Division of Surgical Oncology, Department of Surgery, Wexner Medical Center and James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA. 2. Department of Surgery, University of Verona, Verona, Italy. 3. Department of Surgery, Ospedale San Raffaele, Milan, Italy. 4. Department of Surgery, Johns Hopkins Hospital, Baltimore, MD, USA. 5. Department of Surgery, University of Virginia, Charlottesville, VA, USA. 6. Department of Surgery, Fundeni Clinical Institute, Bucharest, Romania. 7. Department of Surgery, Stanford University, Stanford, CA, USA. 8. Department of Surgery, Emory University, Atlanta, GA, USA. 9. Department of Surgery, Curry Cabral Hospital, Lisbon, Portugal. 10. Department of Surgery, University of Ottawa, Ottawa, Canada. 11. Department of Surgery, Royal Prince Alfred Hospital, University of Sydney, Sydney, Australia. 12. Department of Surgery, Eastern Hepatobiliary Surgery Hospital, Shanghai, China. 13. Department of Hepatobiliopancreatic Surgery and Liver Transplantation, AP-HP, Beaujon Hospital, Clichy, France. 14. Department of Surgery, Erasmus University Medical Centre, Rotterdam, The Netherlands. 15. Department of Gastroenterological Surgery, Yokohama City University School of Medicine, Yokohama, Japan. 16. Division of Surgical Oncology, Department of Surgery, Wexner Medical Center and James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA. tim.pawlik@osumc.edu. 17. Department of Surgery, The Urban Meyer III and Shelley Meyer Chair for Cancer Research, Wexner Medical Center, The Ohio State University, 395 W. 12th Ave., Suite 670, Columbus, OH, USA. tim.pawlik@osumc.edu.
Abstract
BACKGROUND: Accurate risk stratification and patient selection is necessary to identify patients who will benefit the most from surgery or be better treated with other non-surgical treatment strategies. We sought to identify which patients in the preoperative setting would likely derive the most or least benefit from resection of intrahepatic cholangiocarcinoma (ICC). METHODS: Patients who underwent curative-intent resection for ICC between 1990 and 2017 were identified from an international multi-institutional database. A machine-based classification and regression tree (CART) was used to generate homogeneous groups of patients relative to overall survival (OS) based on preoperative factors. RESULTS: Among 1146 patients, CART analysis revealed tumor number and size, albumin-bilirubin (ALBI) grade and preoperative lymph node (LN) status as the strongest prognostic factors associated with OS among patients undergoing resection for ICC. In turn, four groups of patients with distinct outcomes were generated through machine learning: Group 1 (n = 228): single ICC, size ≤ 5 cm, ALBI grade I, negative preoperative LN status; Group 2 (n = 708): (1) single tumor > 5 cm, (2) single tumor ≤ 5 cm, ALBI grade 2/3, and (3) single tumor ≤ 5 cm, ALBI grade 1, metastatic/suspicious LNs; Group 3 (n = 150): 2-3 tumors; Group 4 (n = 60): ≥ 4 tumors. 5-year OS among Group 1, 2, 3, and 4 patients was 60.5%, 35.8%, 27.5%, and 3.8%, respectively (p < 0.001). Similarly, 5-year disease-free survival (DFS) among Group 1, 2, 3, and 4 patients was 47%, 27.2%, 6.8%, and 0%, respectively (p < 0.001). CONCLUSIONS: The machine-based CART model identified distinct prognostic groups of patients with distinct outcomes based on preoperative factors. Survival decision trees may be useful as guides in preoperative patient selection and risk stratification.
BACKGROUND: Accurate risk stratification and patient selection is necessary to identify patients who will benefit the most from surgery or be better treated with other non-surgical treatment strategies. We sought to identify which patients in the preoperative setting would likely derive the most or least benefit from resection of intrahepatic cholangiocarcinoma (ICC). METHODS:Patients who underwent curative-intent resection for ICC between 1990 and 2017 were identified from an international multi-institutional database. A machine-based classification and regression tree (CART) was used to generate homogeneous groups of patients relative to overall survival (OS) based on preoperative factors. RESULTS: Among 1146 patients, CART analysis revealed tumor number and size, albumin-bilirubin (ALBI) grade and preoperative lymph node (LN) status as the strongest prognostic factors associated with OS among patients undergoing resection for ICC. In turn, four groups of patients with distinct outcomes were generated through machine learning: Group 1 (n = 228): single ICC, size ≤ 5 cm, ALBI grade I, negative preoperative LN status; Group 2 (n = 708): (1) single tumor > 5 cm, (2) single tumor ≤ 5 cm, ALBI grade 2/3, and (3) single tumor ≤ 5 cm, ALBI grade 1, metastatic/suspicious LNs; Group 3 (n = 150): 2-3 tumors; Group 4 (n = 60): ≥ 4 tumors. 5-year OS among Group 1, 2, 3, and 4 patients was 60.5%, 35.8%, 27.5%, and 3.8%, respectively (p < 0.001). Similarly, 5-year disease-free survival (DFS) among Group 1, 2, 3, and 4 patients was 47%, 27.2%, 6.8%, and 0%, respectively (p < 0.001). CONCLUSIONS: The machine-based CART model identified distinct prognostic groups of patients with distinct outcomes based on preoperative factors. Survival decision trees may be useful as guides in preoperative patient selection and risk stratification.
Authors: Diamantis I Tsilimigras; Fabio Bagante; Dimitrios Moris; Katiuscha Merath; Anghela Z Paredes; Kota Sahara; Francesca Ratti; Hugo P Marques; Olivier Soubrane; Vincent Lam; George A Poultsides; Irinel Popescu; Sorin Alexandrescu; Guillaume Martel; Aklile Workneh; Alfredo Guglielmi; Tom Hugh; Luca Aldrighetti; Itaru Endo; Timothy M Pawlik Journal: Surgery Date: 2019-10-09 Impact factor: 3.982
Authors: Tal Sella; Olga Kantor; Anna Weiss; Ann H Partridge; Otto Metzger; Tari A King Journal: Breast Cancer Res Treat Date: 2022-06-25 Impact factor: 4.624
Authors: Diamantis I Tsilimigras; Kota Sahara; Lu Wu; Dimitrios Moris; Fabio Bagante; Alfredo Guglielmi; Luca Aldrighetti; Matthew Weiss; Todd W Bauer; Sorin Alexandrescu; George A Poultsides; Shishir K Maithel; Hugo P Marques; Guillaume Martel; Carlo Pulitano; Feng Shen; Olivier Soubrane; B Groot Koerkamp; Amika Moro; Kazunari Sasaki; Federico Aucejo; Xu-Feng Zhang; Ryusei Matsuyama; Itaru Endo; Timothy M Pawlik Journal: JAMA Surg Date: 2020-09-01 Impact factor: 14.766