ORIGINAL RESEARCH article

Front. Oncol.

Sec. Gastrointestinal Cancers: Hepato Pancreatic Biliary Cancers

Volume 15 - 2025 | doi: 10.3389/fonc.2025.1594200

This article is part of the Research TopicIntrahepatic Cholangiocarcinoma: Emerging Insights from Pathobiology to Clinical Translation – Innovative Strategies, Challenges, and OpportunitiesView all articles

Who benefits from adjuvant chemotherapy? Identification of early recurrence in intrahepatic cholangiocarcinoma patients after curative resection using machine learning algorithms

Provisionally accepted
  • The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China

The final, formatted version of the article will be published soon.

It is vital to enhance the identification of early recurrence in intrahepatic cholangiocarcinoma (ICC) patients after curative-intent resection and to determine which patients could benefit from adjuvant chemotherapy (ACT). This study aimed to evaluate the effectiveness of machine learning algorithms in detecting early recurrence in ICC patients and select those who would benefit from ACT to improve prognosis.The study analyzed 254 intrahepatic cholangiocarcinoma (ICC) patients who underwent curative-intent resection to identify early recurrence predictors. Through logistic regression and feature importance analysis, we determined key risk factors and subsequently developed machine learning models utilizing the top five predictors for early recurrence prediction. The predictive performance was validated across area under the ROC curve (AUC).Early recurrence was an independent prognostic risk factor for overall survival (OS) in ICC patients after curative resection (P<0.001). The feature importance ranking based on machine learning algorithms showed that AJCC 8th edition N stage, number of tumors, T stage, perineural invasion, and CA125 as the top five variables associated with early recurrence, which was consistent with the independent risk factors of multivariate logistic regression model. Using the aforementioned five variables, we developed four machine learning prediction models, including logistic regression, support vector machine, LightGBM, and random forest. In the training set, the AUC values were 0.849, 0.860, 0.852, and 0.850, respectively. In the testing set, the AUC values were 0.804, 0.807, 0.841, and 0.835, respectively. Among the various prediction models, LightGBM demonstrated superior performance compared to other models in the testing set, exhibiting higher sensitivity, specificity, and accuracy. The effectiveness of ACT on prognosis for different recurrence times, as predicted by the LightGBM model, indicated that ACT could significantly prolong median OS and RFS for ICC patients predicted to experience early recurrence in both the training and testing sets (P<0.05). Conversely, for ICC patients predicted to have late recurrence, ACT did not improve OS and RFS (P>0.05).The prediction models established in this study demonstrate good predictive capability and can be used to identify patients who may benefit from ACT.

Keywords: intrahepatic cholangiocarcinoma, Recurrence, prognosis, machine learning, Adjuvant chemotherapy

Received: 15 Mar 2025; Accepted: 22 May 2025.

Copyright: © 2025 Li, Liu, Ma, Tang, Chen, Zhang and GENG. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: ZHIMIN GENG, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China

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