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Observational Study
. 2024 Jul;50(7):108375.
doi: 10.1016/j.ejso.2024.108375. Epub 2024 May 9.

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

Marc Perez  1 , Carsten Palnaes Hansen  2 , Fernando Burdio  3 , Patricia Sanchez-Velázquez  4 , Antonio Giuliani  5 , Francesco Lancellotti  6 , Nicola de Liguori-Carino  7 , Giuseppe Malleo  8 , Giovanni Marchegiani  9 , Mauro Podda  10 , Adolfo Pisanu  11 , Giuseppe Massimiliano De Luca  12 , Alessandro Anselmo  13 , Leandro Siragusa  14 , Stefan Kobbelgaard Burgdorf  15 , Christoph Tschuor  16 , Andrea Benedetti Cacciaguerra  17 , Ye Xin Koh  18 , Yoshio Masuda  19 , Mark Yeo Hao Xuan  20 , Nico Seeger  21 , Stefan Breitenstein  22 , Filip Lukasz Grochola  23 , Marcello Di Martino  24 , Luis Secanella  25 , Juli Busquets  26 , Dimitri Dorcaratto  27 , Isabel Mora-Oliver  28 , Sara Ingallinella  29 , Roberto Salvia  30 , Mohammad Abu Hilal  31 , Luca Aldrighetti  32 , Benedetto Ielpo  33
Affiliations
Observational Study

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

Marc Perez et al. Eur J Surg Oncol. 2024 Jul.

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.

Keywords: Distal cholangiocarcinoma; Lymph node ratio; Machine learning; Pancreatoduodenectomy; Prognosis.

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