
94% of researchers rate our articles as excellent or good
Learn more about the work of our research integrity team to safeguard the quality of each article we publish.
Find out more
ORIGINAL RESEARCH article
Front. Oncol.
Sec. Surgical Oncology
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1520512
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
Background: Pneumonia is a leading cause of morbidity and mortality among patients with cancer, and survival time is a primary concern. This study aimed to determine the incidence of pneumonia as a cause of death in patients with cancer and develop corresponding predictive models. Methods: We included 26,938 cancer patients in the United States who died from pneumonia between 1973 and 2020, as identified through the Surveillance, Epidemiology, and End Results (SEER) program. Cox regression analysis was used to ascertain the prognostic factors for patients with cancer. The CatBoost model was constructed to predict survival rates via a cross-validation method. Additionally, our model was validated using a cohort of cancer patients from our institution and deployed via a free-access software interface. Results: The most common cancers resulting in pneumonia-related deaths were prostate (n=7300) and breast (n=5107) cancers, followed by lung and bronchus (n=2839) cancers. The top four cancer systems were digestive (n=5882), endocrine (n=5242), urologic (n=5198), and hematologic (n=3104) systems. The majority of patients were over 70 years old (57.7%), and 54.4% were male. Our CatBoost model demonstrated high precision and accuracy, outperforming other models in predicting the survival of cancer patients with pneumonia (6-month AUC=0.8384,1-year AUC=0.8255,2-year AUC=0.8039, and 3-year AUC=0.7939). The models also revealed robust performance in an external independent dataset (6-month AUC=0.689; 1-year AUC=0.838; 2-year AUC=0.834; and 3-year AUC=0.828). According to the SHAP explanation analysis, the top five factors affecting prognosis were surgery, stage, age, site, and sex; surgery was the most significant factor in both the short-term (6 months and 1 year) and long-term (2 years and 3 years) prognostic models; surgery improved patient prognosis for digestive and endocrine tumor sites with respect to both short-and long-term outcomes but decreased the prognosis of urological and hematologic tumors. Conclusion: Pneumonia remains a major cause of illness and death in patients with cancer, particularly those with digestive system cancers. The early identification of risk factors and timely intervention may help mitigate the negative impact on patients' quality of life and prognosis, improve outcomes, and prevent early deaths caused by infections, which are often preventable.
Keywords: SEER, Pneumonia, Cancer, Mortality, AI models
Received: 31 Oct 2024; Accepted: 26 Feb 2025.
Copyright: © 2025 Ding, Zhang, Zhang, Huang, Tian, Zhou, Wang and Xie. 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:
Yun Xie, Shanghai General Hospital, Shanghai, 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.
Research integrity at Frontiers
Learn more about the work of our research integrity team to safeguard the quality of each article we publish.