Front. Surg.

Sec. Surgical Oncology

Volume 11 - 2024 | doi: 10.3389/fsurg.2024.1489040

Provisionally accepted

Min Liang 1*

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

You have multiple emails registered with Frontiers:

Please enter your email address:

If you already have an account, please login

You don't have a Frontiers account ? You can register here

Introduction: The prognostic landscape of stage III Lung Adenosquamous Carcinoma (ASC) following primary tumor resection remains underexplored. A thoughtfully developed prognostic model has the potential to guide clinicians in patient counseling and the formulation of effective therapeutic strategies. Methods: Utilizing data from the Surveillance, Epidemiology, and End Results database spanning 2000 to 2018, this study identified independent prognostic factors influencing Overall Survival (OS) in ASC using Boruta analysis. Employing Gradient Boosting, Random Forest, and Neural Network algorithms, predictive models were constructed. Model performance was assessed through key metrics, including Area Under the Receiver Operating Characteristic Curve (AUC), calibration plot, Brier score, and Decision Curve Analysis (DCA). Results: Among 241 eligible patients, seven clinical parameters-age, sex, primary tumor size, N stage, primary tumor site, chemotherapy, and systemic therapy-were identified as significant predictors of OS. Advanced age, male gender, larger tumor size, absence of chemotherapy, and lack of systemic therapy were associated with poorer survival. The Random Forest model outperformed others, achieving 3-and 5-year AUCs of 0.80/0.79 (training) and 0.74/0.65 (validation). It also demonstrated better calibration, lower Brier scores (training: 0.189/0.171; validation: 0.207/0.199), and more favorable DCA. SHAP values enhanced model interpretability by highlighting the impact of each parameter on survival predictions. To facilitate clinical application, the Random Forest model was deployed on a web-based server for accessible prognostic assessments. Conclusions: This study presents a robust machine learning model and a web-based tool that assist healthcare practitioners in personalized clinical decision-making and treatment optimization for ASC patients following primary tumor resection.

Keywords: machine learning, prognosis, Survival, adenosquamous carcinoma, Primary tumor, resection

Received: 31 Aug 2024; Accepted: 30 Sep 2024.

Copyright: © 2024 Liang, Li, Xie and Huang. 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: Min Liang, Maoming People's Hospital, Maoming, China

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.