Evidence map›Paper›PMID 40177245›Full record

ArticleFrontiers in oncology2025

Revolutionizing oncology care: pioneering AI models to foresee pneumonia-related mortality.

Qunzhe Ding, Yi Zhang, Zihao Zhang, Peijie Huang, Rui Tian, Zhigang Zhou, Ruilan Wang, Yun Xie

Abstract read
In one paragraph

Article in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Qunzhe Ding *School of Information Management, Wuhan University, Wuhan, Hubei, China.
Yi Zhang *Department of Rheumatology and Immunology, Changzheng Hospital, Naval Military Medical University, Shanghai, China.
Zihao ZhangGeorgetown University Medical Center Department of Oncology, Washington D.C., CO, United States.
Peijie HuangDepartment of Critical Care Medicine, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Songjiang, Shanghai, China.
Rui TianGeorgetown University Medical Center Department of Oncology, Washington D.C., CO, United States.
Zhigang ZhouDepartment of Critical Care Medicine, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Songjiang, Shanghai, China.
Ruilan WangDepartment of Critical Care Medicine, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Songjiang, Shanghai, China.
Yun XieDepartment of Critical Care Medicine, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Songjiang, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Pneumonia is a leading cause of morbidity and mortality among patients with cancer, and survival time is a primary concern. Despite their importance, there is a dearth of accurate predictive models in clinical settings. This study aimed to determine the incidence of pneumonia as a cause of death in patients with cancer, analyze trends and risk factors associated with mortality, 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.

Indexed as

AI modelscancermortalitypneumoniaSEER

Identifiers

PMID40177245
PMCPMC11961870

What Socratic holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.