A machine learning predictive model for recurrence of resected distal cholangiocarcinoma: Development and validation of predictive model using artificial intelligence

Perez, Marc;Palnaes Hansen, Carsten;Burdio, Fernando;Sanchez-Velázquez, Patricia;Giuliani, Antonio;Lancellotti, Francesco;de Liguori-Carino, Nicola;Malleo, Giuseppe;Marchegiani, Giovanni;Podda, Mauro;Pisanu, Adolfo;De Luca, Giuseppe Massimiliano;Anselmo, Alessandro;Siragusa, Leandro;Kobbelgaard Burgdorf, Stefan;Tschuor, Christoph;Cacciaguerra, Andrea Benedetti;Koh, Ye Xin;Masuda, Yoshio;Hao Xuan, Mark Yeo;Seeger, Nico;Breitenstein, Stefan;Grochola, Filip Lukasz;Di Martino, Marcello;Secanella, Luis;Busquets, Juli;Dorcaratto, Dimitri;Mora-Oliver, Isabel;Ingallinella, Sara;Salvia, Roberto;Abu Hilal, Mohammad;Aldrighetti, Luca;Ielpo, Benedetto

2024-01-01

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Abstract

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.