TY - JOUR
T1 - A machine learning predictive model for recurrence of resected distal cholangiocarcinoma
T2 - Development and validation of predictive model using artificial intelligence
AU - Perez, Marc
AU - Palnaes Hansen, Carsten
AU - Burdio, Fernando
AU - Sanchez-Velázquez, Patricia
AU - Giuliani, Antonio
AU - Lancellotti, Francesco
AU - de Liguori-Carino, Nicola
AU - Malleo, Giuseppe
AU - Marchegiani, Giovanni
AU - Podda, Mauro
AU - Pisanu, Adolfo
AU - De Luca, Giuseppe Massimiliano
AU - Anselmo, Alessandro
AU - Siragusa, Leandro
AU - Kobbelgaard Burgdorf, Stefan
AU - Tschuor, Christoph
AU - Cacciaguerra, Andrea Benedetti
AU - Koh, Ye Xin
AU - Masuda, Yoshio
AU - Hao Xuan, Mark Yeo
AU - Seeger, Nico
AU - Breitenstein, Stefan
AU - Grochola, Filip Lukasz
AU - Di Martino, Marcello
AU - Secanella, Luis
AU - Busquets, Juli
AU - Dorcaratto, Dimitri
AU - Mora-Oliver, Isabel
AU - Ingallinella, Sara
AU - Salvia, Roberto
AU - Abu Hilal, Mohammad
AU - Aldrighetti, Luca
AU - Ielpo, Benedetto
N1 - © 2024 Elsevier Ltd, BASO ∼ The Association for Cancer Surgery, and the European Society of Surgical Oncology. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
PY - 2024
Y1 - 2024
N2 - INTRODUCTION: Distal Cholangiocarcinoma (dCCA) represents a challenge in hepatobiliary oncology, that requires nuanced post-resection prognostic modeling. Conventional staging criteria may oversimplify dCCA complexities, prompting the exploration of novel prognostic factors and methodologies, including machine learning algorithms. This study aims to develop a machine learning predictive model for recurrence after resected dCCA.MATERIAL AND METHODS: This retrospective multicentric observational study included patients with dCCA from 13 international centers who underwent curative pancreaticoduodenectomy (PD). A LASSO-regularized Cox regression model was used to feature selection, examine the path of the coefficient and create a model to predict recurrence. Internal and external validation and model performance were assessed using the C-index score. Additionally, a web application was developed to enhance the clinical use of the algorithm.RESULTS: Among 654 patients, LNR (Lymph Node Ratio) 15, neural invasion, N stage, surgical radicality, and differentiation grade emerged as significant predictors of disease-free survival (DFS). The model showed the best discrimination capacity with a C-index value of 0.8 (CI 95 %, 0.77%-0.86 %) and highlighted LNR15 as the most influential factor. Internal and external validations showed the model's robustness and discriminative ability with an Area Under the Curve of 92.4 % (95 % CI, 88.2%-94.4 %) and 91.5 % (95 % CI, 88.4%-93.5 %), respectively. The predictive model is available at https://imim.shinyapps.io/LassoCholangioca/.CONCLUSIONS: This study pioneers the integration of machine learning into prognostic modeling for dCCA, yielding a robust predictive model for DFS following PD. The tool can provide information to both patients and healthcare providers, enhancing tailored treatments and follow-up.
AB - INTRODUCTION: Distal Cholangiocarcinoma (dCCA) represents a challenge in hepatobiliary oncology, that requires nuanced post-resection prognostic modeling. Conventional staging criteria may oversimplify dCCA complexities, prompting the exploration of novel prognostic factors and methodologies, including machine learning algorithms. This study aims to develop a machine learning predictive model for recurrence after resected dCCA.MATERIAL AND METHODS: This retrospective multicentric observational study included patients with dCCA from 13 international centers who underwent curative pancreaticoduodenectomy (PD). A LASSO-regularized Cox regression model was used to feature selection, examine the path of the coefficient and create a model to predict recurrence. Internal and external validation and model performance were assessed using the C-index score. Additionally, a web application was developed to enhance the clinical use of the algorithm.RESULTS: Among 654 patients, LNR (Lymph Node Ratio) 15, neural invasion, N stage, surgical radicality, and differentiation grade emerged as significant predictors of disease-free survival (DFS). The model showed the best discrimination capacity with a C-index value of 0.8 (CI 95 %, 0.77%-0.86 %) and highlighted LNR15 as the most influential factor. Internal and external validations showed the model's robustness and discriminative ability with an Area Under the Curve of 92.4 % (95 % CI, 88.2%-94.4 %) and 91.5 % (95 % CI, 88.4%-93.5 %), respectively. The predictive model is available at https://imim.shinyapps.io/LassoCholangioca/.CONCLUSIONS: This study pioneers the integration of machine learning into prognostic modeling for dCCA, yielding a robust predictive model for DFS following PD. The tool can provide information to both patients and healthcare providers, enhancing tailored treatments and follow-up.
UR - http://www.scopus.com/inward/record.url?scp=85193819353&partnerID=8YFLogxK
U2 - 10.1016/j.ejso.2024.108375
DO - 10.1016/j.ejso.2024.108375
M3 - Journal article
C2 - 38795677
SN - 0748-7983
VL - 50
JO - European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
JF - European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
IS - 7
M1 - 108375
ER -