A global systematic analysis and statistical evaluation of prognostic prediction models for non-small cell lung cancer

Xiaowen Xu; Leyi Chen; Maojie Xue; Aoxuan Wang; Qihan Fan; Yuqing Yang; Juanjuan Fan; Tianhong Han; Yichen Pan; Shiyu Xiong; Yuan Chen; Zhujun Chen; Zi Wang; Jingtong Xu; Zixing Zhang; Yuchen Zhang; Yu Chen Zhao; Longyao Zhang; Zhongwen Zhang; Ning Xie; Yanru Wang; Xiang Wang; Xuan Wang; Manel Esteller; Yi Li; Rayjean J. Hung; Christopher I. Amos; David C. Christiani; Feng Chen; Yang Zhao; Yongyue Wei; Ruyang Zhang

doi:10.59717/j.xinn-med.2026.100195

The Innovation Medicine > 2026 Vol. 4 > No. 1 > 100195

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A global systematic analysis and statistical evaluation of prognostic prediction models for non-small cell lung cancer

1.
Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
2.
Department of Clinical Medicine, School of the first Clinical Medicine, Nanjing Medical University, Nanjing 211166, China
3.
Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
4.
Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
5.
Department of Occupational Medicine and Environmental Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
6.
Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston MA 02115, USA
7.
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston MA 02115, USA
8.
Josep Carreras Leukaemia Research Institute, Barcelona, Catalonia 08021, Spain
9.
Centro de Investigacion Biomedica en Red Cancer, Madrid 28029, Spain
10.
Institució Catalana de Recerca i Estudis Avançats, Barcelona, Catalonia 08010, Spain
11.
Physiological Sciences Department, School of Medicine and Health Sciences, University of Barcelona, Barcelona, Catalonia 08007, Spain
12.
Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
13.
Lunenfeld-Tanenbaum Research Institute, Sinai Health, and University of Toronto, Toronto, ON M5G 1X5, Canada
14.
The Institute for Clinical and Translational Research, Baylor College of Medicine, Houston TX 77030, USA
15.
Pulmonary and Critical Care Division, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston MA 02114, USA
16.
China International Cooperation Center (CICC) for Environment and Human Health, Nanjing Medical University, Nanjing 211166, China
17.
Jiangsu Key Lab of Cancer Biomarkers, Prevention, and Treatment, Cancer Center, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
18.
The Key Laboratory of Modern Toxicology of Ministry of Education, Nanjing Medical University, Nanjing 211166, China
19.
Center for Public Health and Epidemic Preparedness & Response, Peking University, Beijing 100191, China
20.
Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
21.
Information Center, The Second People's Hospital of Changzhou, The Third Affiliated Hospital of Nanjing Medical University, Nanjing 213164, China
22.
Changzhou Medical Center, Nanjing Medical University, Changzhou 213164, China
23.
Laboratory for Digital Intelligence & Health Governance, Nanjing Medical University, Nanjing 211166, China
24.
These authors contributed equally
25.
The senior author who supervised the work

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Corresponding authors: zhangruyang@njmu.edu.cn (R.Z.); ywei@pku.edu.cn (Y.W.); zhaoyang@njmu.edu.cn (Y.Z.); fengchen@njmu.edu.cn (F.C.); Yang Zhao, E-mail: zhaoyang@njmu.edu.cn ; Yongyue Wei, E-mail: ywei@pku.edu.cn ; Ruyang Zhang, E-mail: zhangruyang@njmu.edu.cn
Received Date: September 15, 2025

Accepted Date: January 16, 2026

Published Online: January 17, 2026

The Innovation Medicine 4(1), https://doi.org/10.59717/j.xinn-med.2026.100195 | Cite this article

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1. We performed the first global systematical analysis of 268 prognostic prediction models for NSCLC.
  
  Existing models exhibit mediocre discrimination with average AUC around 0.7.
  
  Only 33.2% of existing models were externally validated, and 75.3% of these exhibit high risk of bias.
  
  Adherence to TRIPOD and BMJ 13-step guidelines may improve model reliability and clinical translation.
  
  The community should highlight model fairness, model updating and translation into real-world clinical practice.

ABSTRACT

ABSTRACT

Prognostic prediction models can aid clinical decision-making for high-risk non-small cell lung cancer (NSCLC) patients. We conducted a systematic search across multiple databases and evaluated eligible studies using PRISMA and PROBAST checklists. Of 28,833 references screened, 233 studies describing 268 models were included. Among them, 89 underwent external validation; 67 (75.28%) were classified with high risk for bias. Most models were developed in North America (45.06%). The median area under the receiver operating characteristic curve (AUC) for 1-, 3-, and 5-year predictions were 0.759, 0.720, and 0.691, respectively. The most common predictors were age (54.85%) and stage (58.21%). And, 47.01% of models were sex-specific. Recently, easily accessible radiomic features have been increasingly used in statistical modeling compared to molecular omics. Models integrating radiomic and clinical features showed potential for improved performance (1-, 3-, and 5-year AUCs: 0.931, 0.985, 0.942). Model discrimination varied across different studies, and there is a lack of model calibration and clinical utility. This review highlights advances and limitations in NSCLC prognostic models. To enhance model reliability and generalizability, it remains crucial to adhere to the TRIPOD guideline to perform prediction model study, and to emphasize comprehensive model validation and bias-reduction strategies. Following a step-by-step guide to develop and validate clinical prediction models by Efthimiou, et al. (BMJ, 2024), along with a multifaceted, multidisciplinary and multi-regional approach, is the key way to facilitate the development and clinical application of qualified models.

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Xu X., Chen L., Xue M., et al. (2026). A global systematic analysis and statistical evaluation of prognostic prediction models for non-small cell lung cancer. The Innovation Medicine 4:100195. https://doi.org/10.59717/j.xinn-med.2026.100195

Xu X., Chen L., Xue M., et al. (2026). A global systematic analysis and statistical evaluation of prognostic prediction models for non-small cell lung cancer. The Innovation Medicine 4:100195. https://doi.org/10.59717/j.xinn-med.2026.100195

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A global systematic analysis and statistical evaluation of prognostic prediction models for non-small cell lung cancer

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