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Preoperative Serum Markers and Risk Classification in Intrahepatic Cholangiocarcinoma: A Multicenter Retrospective Study

SIMPLE SUMMARY: We were able to stratify intrahepatic cholangiocellular carcinoma (ICC) patients who underwent hepatectomy into three risk groups using a classification and regression tree (CART) model for recurrence-free survival (RFS) and overall survival (OS). CART analysis, using results from th...

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Detalles Bibliográficos
Autores principales: Kaibori, Masaki, Yoshii, Kengo, Kosaka, Hisashi, Ota, Masato, Komeda, Koji, Ueno, Masaki, Hokutou, Daisuke, Iida, Hiroya, Matsui, Kosuke, Sekimoto, Mitsugu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9658667/
https://www.ncbi.nlm.nih.gov/pubmed/36358877
http://dx.doi.org/10.3390/cancers14215459
Descripción
Sumario:SIMPLE SUMMARY: We were able to stratify intrahepatic cholangiocellular carcinoma (ICC) patients who underwent hepatectomy into three risk groups using a classification and regression tree (CART) model for recurrence-free survival (RFS) and overall survival (OS). CART analysis, using results from the multivariate analysis, revealed decision trees for RFS and OS based on machine learning using preoperative serum markers. These three risk classifications using preoperative noninvasive prognostic factors could predict prognosis for ICC. These risk classifications are simple and easy to understand and can be clinically applied. ABSTRACT: Accurate risk stratification selects patients who are expected to benefit most from surgery. This retrospective study enrolled 225 Japanese patients with intrahepatic cholangiocellular carcinoma (ICC) who underwent hepatectomy between January 2009 and December 2020 and identified preoperative blood test biomarkers to formulate a classification system that predicted prognosis. The optimal cut-off values of blood test parameters were determined by ROC curve analysis, with Cox univariate and multivariate analyses identifying prognostic factors. Risk classifications were established using classification and regression tree (CART) analysis. CART analysis revealed decision trees for recurrence-free survival (RFS) and overall survival (OS) and created three risk classifications based on machine learning of preoperative serum markers. Five-year rates differed significantly (p < 0.001) between groups: 60.4% (low-risk), 22.8% (moderate-risk), and 4.1% (high-risk) for RFS and 69.2% (low-risk), 32.3% (moderate-risk), and 9.2% (high-risk) for OS. No difference in OS was observed between patients in the low-risk group with or without postoperative adjuvant chemotherapy, although OS improved in the moderate group and was prolonged significantly in the high-risk group receiving chemotherapy. Stratification of patients with ICC who underwent hepatectomy into three risk groups for RFS and OS identified preoperative prognostic factors that predicted prognosis and were easy to understand and apply clinically.