Preoperative and postoperative predictive models of early recurrence for colorectal liver metastases following chemotherapy and curative-intent one-stage hepatectomy.

Colorectal liver metastasis Conversion surgery Early recurrence Neoadjuvant chemotherapy Online calculator Predictive model

Journal

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
ISSN: 1532-2157
Titre abrégé: Eur J Surg Oncol
Pays: England
ID NLM: 8504356

Informations de publication

Date de publication:
03 Jul 2024
Historique:
received: 11 06 2024
revised: 27 06 2024
accepted: 03 07 2024
medline: 15 7 2024
pubmed: 15 7 2024
entrez: 14 7 2024
Statut: aheadofprint

Résumé

Accurate prediction of patients at risk for early recurrence (ER) among patients with colorectal liver metastases (CRLM) following preoperative chemotherapy and hepatectomy remains limited. Patients with CRLM who received chemotherapy prior to undergoing curative-intent resection between 2000 and 2020 were identified from an international multi-institutional database. Multivariable Cox regression analysis was used to assess clinicopathological factors associated with ER, and an online calculator was developed and validated. Among 768 patients undergoing preoperative chemotherapy and curative-intent resection, 128 (16.7 %) patients had ER. Multivariable Cox analysis demonstrated that Eastern Cooperative Oncology Group Performance status ≥1 (HR 2.09, 95%CI 1.46-2.98), rectal cancer (HR 1.95, 95%CI 1.35-2.83), lymph node metastases (HR 2.39, 95%CI 1.60-3.56), mutated Kirsten rat sarcoma oncogene status (HR 1.95, 95%CI 1.25-3.02), increase in tumor burden score during chemotherapy (HR 1.51, 95%CI 1.03-2.24), and bilateral metastases (HR 1.94, 95%CI 1.35-2.79) were independent predictors of ER in the preoperative setting. In the postoperative model, in addition to the aforementioned factors, tumor regression grade was associated with higher hazards of ER (HR 1.91, 95%CI 1.32-2.75), while receipt of adjuvant chemotherapy was associated with lower likelihood of ER (HR 0.44, 95%CI 0.30-0.63). The discriminative accuracy of the preoperative (training: c-index: 0.77, 95%CI 0.72-0.81; internal validation: c-index: 0.79, 95%CI 0.75-0.82) and postoperative (training: c-index: 0.79, 95%CI 0.75-0.83; internal validation: c-index: 0.81, 95%CI 0.77-0.84) models was favorable (https://junkawashima.shinyapps.io/CRLMfollwingchemotherapy/). Patient-, tumor- and treatment-related characteristics in the preoperative and postoperative setting were utilized to develop an online, easy-to-use risk calculator for ER following resection of CRLM.

Identifiants

pubmed: 39004061
pii: S0748-7983(24)00584-5
doi: 10.1016/j.ejso.2024.108532
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

108532

Informations de copyright

Copyright © 2024 Elsevier Ltd, BASO ~ The Association for Cancer Surgery, and the European Society of Surgical Oncology. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. No funding was received for this study.

Auteurs

Jun Kawashima (J)

Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA; Department of Gastroenterological Surgery, Yokohama City University School of Medicine, Yokohama, Japan.

Odysseas P Chatzipanagiotou (OP)

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

Diamantis I Tsilimigras (DI)

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

Muhammad Muntazir Mehdi Khan (MMM)

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

Giovanni Catalano (G)

Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA; Department of Surgery, University of Verona, Italy.

Zayed Rashid (Z)

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

Mujtaba Khalil (M)

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

Abdullah Altaf (A)

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

Muhammad Musaab Munir (MM)

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

Yutaka Endo (Y)

Department of Surgery, University of Rochester, Rochester, NY, USA.

Selamawit Woldesenbet (S)

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

Alfredo Guglielmi (A)

Department of Surgery, University of Verona, Italy.

Andrea Ruzzenente (A)

Department of Surgery, University of Verona, Italy.

Luca Aldrighetti (L)

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

Sorin Alexandrescu (S)

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

Minoru Kitago (M)

Department of Surgery, Keio University, Tokyo, Japan.

George Poultsides (G)

Department of Surgery, Stanford University, CA, USA.

Kazunari Sasaki (K)

Department of Surgery, Stanford University, CA, USA.

Federico Aucejo (F)

Department of General Surgery, Cleveland Clinic Foundation, OH, USA.

Itaru Endo (I)

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

Timothy M Pawlik (TM)

Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA. Electronic address: Tim.Pawlik@osumc.edu.

Classifications MeSH