The optimal cut‐off values for tumor size, number of lesions, and CEA levels in patients with surgically treated colorectal cancer liver metastases: An international, multi‐institutional study. Issue 4 (5th January 2021)

• Record Type: Journal Article
• Authors: Kamphues, Carsten; Andreatos, Nikolaos; Kruppa, Jochen; Buettner, Stefan; Wang, Jaeyun; Sasaki, Kazunari; Wagner, Doris; Morioka, Daisuke; Fitschek, Fabian; Løes, Inger Marie; Imai, Katsunori; Sun, Jinger; Poultsides, George; Kaczirek, Klaus; Lønning, Per Eystein; Endo, Itaru; Baba, Hideo; Kornprat, Peter; Aucejo, Federico N; Wolfgang, Christopher L.; Kreis, Martin E.; Weiss, Matthew J; Margonis, Georgios Antonios
• Is Part Of: Journal of surgical oncology. Volume 123:Issue 4(2021)
• Page Start: 939
• Page End: 948
• Publication Date: 2021-01-05
• Subjects: colorectal cancer -- metastases -- prognostic factors; Cancer -- Surgery -- Periodicals; Neoplasms -- Periodicals; 616
• DOI: 10.1002/jso.26361
• Languages: English
• ISSNs: 0022-4790

Abstract

Abstract: Background and Objectives: Despite the long‐standing consensus on the importance of tumor size, tumor number and carcinoembryonic antigen (CEA) levels as predictors of long‐term outcomes among patients with colorectal liver metastases (CRLM), optimal prognostic cut‐offs for these variables have not been established. Methods: Patients who underwent curative‐intent resection of CRLM and had available data on at least one of the three variables of interest above were selected from a multi‐institutional dataset of patients with known KRAS mutational status. The resulting cohort was randomly split into training and testing datasets and recursive partitioning analysis was employed to determine optimal cut‐offs. The concordance probability estimates (CPEs) for these optimal cut offs were calculated and compared to CPEs for the most widely used cut‐offs in the surgical literature. Results: A total of 1643 patients who met eligibility criteria were identified. Following recursive partitioning analysis in the training dataset, the following cut‐offs were identified: 2.95 cm for tumor size, 1.5 for tumor number and 6.15 ng/ml for CEA levels. In the entire dataset, the calculated CPEs for the new tumor size (0.52), tumor number (0.56) and CEA (0.53) cut offs exceeded CPEs for other commonly employed cut‐offs. Conclusion: The current study was able to identify optimal cut‐offs for the three most commonly employed prognostic factors in CRLM. While the per variable gains in discriminatory power are modest, these novel cut‐offs may help produce appreciable increases in prognostic performance when combined in the context of future risk scores.