Predictive Modeling of Long-Term Prognosis After Resection in Typical Pulmonary Carcinoid: A Machine Learning Perspective

Abstract

Typical Pulmonary Carcinoid (TPC) is defined by its slow growth, frequently necessitating surgical intervention. Despite this, the long-term outcomes following tumor resection are not well understood. This study examined the factors impacting Overall Survival (OS) in patients with TPC, leveraging data from the Surveillance, Epidemiology, and End Results database spanning from 2000 to 2018. We employed Lasso-Cox analysis to identify prognostic features and developed various models using Random Forest, XGBoost, and Cox regression algorithms. Subsequently, we assessed model performance using metrics such as Area Under the Curve (AUC), calibration plot, Brier score, and Decision Curve Analysis (DCA). Among the 2687 patients, we identified five clinical features significantly affecting OS. Notably, the Random Forest model exhibited strong performance, achieving 5- and 7-year AUC values of 0.744/0.757 in the training set and 0.715/0.740 in the validation set, respectively, outperforming other models. Additionally, we developed a web-based platform aimed at facilitating easy access to the model. This study presents a machine learning model and a web-based support system for healthcare professionals, assisting in personalized treatment decisions for patients with TPC post-tumor resection.

Keywords:

• Typical pulmonary carcinoid
• machine learning
• prognosis
• survival
• primary tumor
• resection

Acknowledgments

The authors thank Mrs. Yunru Fan and Dr. Alexandra Lam for coordinating and supporting the development and preparation of the manuscript.

Author contributions

Study concepts: Jian Huang and Min Liang; Study design: Caiyan Liu; Data acquisition: Jian Huang; Data analysis and interpretation: Min Liang and Mafeng Chen; Manuscript preparation: Min Liang and Caiyan Liu.

Compliance with ethics guidelines

The study adhered to the principles outlined in the Declaration of Helsinki (2013 revision) to maintain ethical integrity throughout the research process. This article utilized open-access databases and did not involve original research with human participants or animals. To uphold patient privacy and ethical standards, each author has officially confirmed compliance with the SEER research data agreement. Since the data used in this study is from public databases and patient information is anonymized, informed consent was not required.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Authorship

All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship for this article, take responsibility for the integrity of the work as a whole, and have given their approval for this version to be published.

Data availability statement

The datasets generated during and analyzed during the current study are available in the Surveillance, Epidemiology, and End Results (SEER) repository[https://seer.cancer.gov/data/].

Additional information

Funding

The funding was provided by the High-level Hospital Construction Project of Maoming People’s Hospital, the Medical Research Fund of Guangdong Province(A2024528), the Research Project of Maoming Science and Technology Bureau (Grant No. 2021121), and the Outstanding Young Talents Program of Maoming People’s hospital (#SY2021021). This study was supported by the High-level Hospital Construction Project of Maoming People’s Hospital.