A Novel Classification of Intrahepatic Cholangiocarcinoma Phenotypes Using Machine Learning Techniques: An International Multi-Institutional Analysis.
Journal
Annals of surgical oncology
ISSN: 1534-4681
Titre abrégé: Ann Surg Oncol
Pays: United States
ID NLM: 9420840
Informations de publication
Date de publication:
Dec 2020
Dec 2020
Historique:
received:
18
04
2020
pubmed:
5
6
2020
medline:
28
4
2021
entrez:
5
6
2020
Statut:
ppublish
Résumé
Patients with intrahepatic cholangiocarcinoma (ICC) generally have a poor prognosis, yet there can be heterogeneity in the patterns of presentation and associated outcomes. We sought to identify clusters of ICC patients based on preoperative characteristics that may have distinct outcomes based on differing patterns of presentation. Patients undergoing curative-intent resection of ICC between 2000 and 2017 were identified using a multi-institutional database. A cluster analysis was performed based on preoperative variables to identify distinct patterns of presentation. A classification tree was built to prospectively assign patients into cluster assignments. Among 826 patients with ICC, three distinct presentation patterns were noted. Specifically, Cluster 1 (common ICC, 58.9%) consisted of individuals who had a small-size ICC (median 4.6 cm) and median carbohydrate antigen (CA) 19-9 and neutrophil-to-lymphocyte ratio (NLR) levels of 40.3 UI/mL and 2.6, respectively; Cluster 2 (proliferative ICC, 34.9%) consisted of patients who had larger-size tumors (median 9.0 cm), higher CA19-9 levels (median 72.0 UI/mL), and similar NLR (median 2.7); Cluster 3 (inflammatory ICC, 6.2%) comprised of patients with a medium-size ICC (median 6.2 cm), the lowest range of CA19-9 (median 26.2 UI/mL), yet the highest NLR (median 13.5) (all p < 0.05). Median OS worsened incrementally among the three different clusters {Cluster 1 vs. 2 vs. 3; 60.4 months (95% confidence interval [CI] 43.0-77.8) vs. 27.2 months (95% CI 19.9-34.4) vs. 13.3 months (95% CI 7.2-19.3); p < 0.001}. The classification tree used to assign patients into different clusters had an excellent agreement with actual cluster assignment (κ = 0.93, 95% CI 0.90-0.96). Machine learning analysis identified three distinct prognostic clusters based solely on preoperative characteristics among patients with ICC. Characterizing preoperative patient heterogeneity with machine learning tools can help physicians with preoperative selection and risk stratification of patients with ICC.
Identifiants
pubmed: 32495285
doi: 10.1245/s10434-020-08696-z
pii: 10.1245/s10434-020-08696-z
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
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