Evidence map›Paper›PMID 42152798›Full record

ArticleCancer medicine2026

A Population-Based Nomogram for Predicting Overall Survival in Pulmonary Sarcomatoid Carcinoma With Real-World External Validation.

Shuwen Cui, Yimeng Li, Xiaoyu Zhang, Yuanyuan Fan, Kaidiriye Setiwalidi, Yingming Zhang, Huan Gao, Xiaoqiang Zheng, Zhiping Ruan, Chunli Li and 4 more

Abstract readValidation Study
In one paragraph

Article in Cancer medicine, 2026. 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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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

14 authors.

Shuwen CuiDepartment of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, People's Republic of China.ORCID https://orcid.org/0009-0006-4184-2943
Yimeng LiDepartment of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, People's Republic of China.
Xiaoyu ZhangDepartment of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, People's Republic of China.ORCID https://orcid.org/0009-0006-9894-7543
Yuanyuan FanDepartment of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, People's Republic of China.
Kaidiriye SetiwalidiDepartment of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, People's Republic of China.
Yingming ZhangDepartment of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, People's Republic of China.
Huan GaoDepartment of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, People's Republic of China.
Xiaoqiang ZhengDepartment of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, People's Republic of China.
Zhiping RuanDepartment of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, People's Republic of China.ORCID https://orcid.org/0000-0001-9467-7466
Chunli LiDepartment of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, People's Republic of China.ORCID https://orcid.org/0000-0001-8488-1674
Xuan LiangDepartment of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, People's Republic of China.
Tao TianDepartment of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, People's Republic of China.
Yu YaoDepartment of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, People's Republic of China.ORCID https://orcid.org/0000-0003-3618-2363
Xiao FuDepartment of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, People's Republic of China.

Funding

Shaanxi Administration of Traditional Chinese Medicine SZY-KJCYC-2025-ZY-018the Foundations of the First Affiliated Hospital of Xi'an Jiaotong University 2024-MS-5the Foundations of the First Affiliated Hospital of Xi'an Jiaotong University 2024-QN-43
6 · The paper itself

Abstract

backgroundPulmonary sarcomatoid carcinoma (PSC) is a rare and highly aggressive tumor. This study aimed to investigate the prognostic factors of PSC and develop a new risk classifier to predict patients' overall survival (OS).

methodsThis retrospective study consisted of 675 PSC patients registered in the Surveillance, Epidemiology, and End Results (SEER) database, and 90 patients from the First Affiliated Hospital of Xi'an Jiaotong University. The univariate and multivariate Cox regression analyses were adopted to select the best prognostic variables. A nomogram was developed to predict the OS of these patients. The nomogram was assessed using receiver operating characteristic (ROC) curves, the concordance index (C-index), and calibration curves. Decision curve analysis (DCA) was adopted to assess its net clinical benefit. Additionally, a Lasso-Cox regression model was utilized to explore prognostic factors in real-world patients with PSC.

resultsThe multivariate analysis revealed that sex, T, N, and M stages, surgery, radiation, chemotherapy, and liver metastasis were independent prognostic factors of OS for patients with PSC, and a nomogram was developed to predict the OS of these patients. The ROC curves and the estimated C-index displayed that the nomogram yielded excellent performance in predicting OS. Calibration curves demonstrated satisfactory consistency between the actual and predicted OS. DCA illustrated that the nomogram would provide significant net clinical benefits. Consistent findings were discovered in the internal and external validation cohorts. Lasso-Cox regression models revealed that surgery and immunotherapy were key protective factors for OS, while ECOG ≥ 2, underlying medical history, and distant metastasis constituted major risk factors for OS.

conclusionsThe constructed nomogram is a reliable risk classifier to achieve personalized survival probability prediction for patients with PSC. Real-world data suggest that immunotherapy is associated with significantly prolonged survival in patients with PSC.

Indexed as

CarcinosarcomaLung NeoplasmsNomogramsAdultAgedFemaleHumansMaleMiddle AgedPrognosisRetrospective StudiesRisk FactorsROC CurveSEER Programantiangiogenic drugsimmune checkpoint inhibitors (ICIs)nomogramoverall survival (OS)prognostic factorpulmonary sarcomatoid carcinoma (PSC)

Identifiers

PMID42152798
PMCPMC13184639

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