A Machine-Based Approach to Preoperatively Identify Patients with the Most and Least Benefit Associated with Resection for Intrahepatic Cholangiocarcinoma: An International Multi-institutional Analysis of 1146 Patients.
Aged
Bile Duct Neoplasms
/ blood
Bile Ducts, Intrahepatic
Bilirubin
/ blood
Biomarkers
/ blood
Cholangiocarcinoma
/ blood
Databases, Factual
Disease-Free Survival
Female
Hepatectomy
Humans
Liver Neoplasms
/ surgery
Male
Middle Aged
Predictive Value of Tests
Proportional Hazards Models
Retrospective Studies
Time Factors
Treatment Outcome
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:
Apr 2020
Apr 2020
Historique:
received:
29
08
2019
pubmed:
16
11
2019
medline:
29
12
2020
entrez:
16
11
2019
Statut:
ppublish
Résumé
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). 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. 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). 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.
Sections du résumé
BACKGROUND
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
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
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
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.
Identifiants
pubmed: 31728792
doi: 10.1245/s10434-019-08067-3
pii: 10.1245/s10434-019-08067-3
doi:
Substances chimiques
Biomarkers
0
Bilirubin
RFM9X3LJ49
Types de publication
Journal Article
Multicenter Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
1110-1119Références
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