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An Artificial Intelligence Model for Predicting 1‐Year Survival of Bone Metastases in Non‐Small‐Cell Lung Cancer Patients Based on XGBoost Algorithm

Huang, Zhangheng, Hu, Chuan, Chi, Changxing, Jiang, Zhe, Tong, Yuexin, Zhao, Chengliang·2020·BioMed Research International
DOI 10.1155/2020/3462363PMC PMC7338972
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Abstract

Non‐small‐cell lung cancer (NSCLC) patients often develop bone metastases (BM), and the overall survival for these patients is usually perishing. However, a model with high accuracy for predicting the survival of NSCLC with BM is still lacking. Here, we aimed to establish a model based on artificial intelligence for predicting the 1‐year survival rate of NSCLC with BM by using extreme gradient boosting (XGBoost), a large‐scale machine learning algorithm. We selected NSCLC patients with BM between 2010 and 2015 from the Surveillance, Epidemiology, and End Results database. In total, 5973 cases were enrolled and divided into the training (n = 4183) and validation (n = 1790) sets. XGBoost, random forest, support vector machine, and logistic algorithms were used to generate predictive models. Receiver operating characteristic curves were used to evaluate and compare the predictive performance of each model. The parameters including tumor size, age, race, sex, primary site, histological subtype, grade, laterality, T stage, N stage, surgery, radiotherapy, chemotherapy, distant metastases to other sites (lung, brain, and liver), and marital status were selected to construct all predictive models. The XGBoost model had a better performance in both training and validation sets as compared with other models in terms of accuracy. Our data suggested that the XGBoost model is the most precise and personalized tool for predicting the 1‐year survival rate for NSCLC patients with BM. This model can help the clinicians to design more rational and effective therapeutic strategies.

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  • Predictive systems like the ITEN model provide tailored recommendations for systemic therapies, for example in patients with bone metastases, with the aim of improving survival.

    “Within this context, predictive systems like the ITEN model (impact of treatment evolution in non-small-cell lung cancer) provide tailored recommendations for systemic therapies, for example in patients presenting with bone metastases, with the aim of improving survival.”

    From Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications (2026) · cites this paper as [57]

    The Discussion (page not provided) says the authors’ XGBoost model provides personalized survival predictions for patients with non-small-cell lung cancer and bone metastases, which could improve personalized treatment. It does not identify the model as ITEN or say it provides tailored recommendations for systemic therapies or improves survival; it also notes that specific treatment information was unavailable. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • The ITEN model's alignment with previously published evidence supports its robustness in forecasting survival in individuals with non-small-cell lung cancer.

    “The ITEN model’s alignment with previously published evidence further supports its robustness in forecasting survival in individuals with non-small-cell lung cancer [57].”

    From Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications (2026) · cites this paper as [57]

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Cited by

Sorted by citations

Authors
Huang, Z., Hu, C., Chi, C., Jiang, Z., Tong, Y., & Zhao, C.
ORCID
Zhao, Chengliang
Venue
BioMed Research International
Publisher
Wiley
Published
2020-01
Volume
2020
Issue
1
ISSN
  • 2314-6133
  • 2314-6141
DOI
10.1155/2020/3462363
PMC
PMC7338972
Cited by
34
References
0
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor, 179 d)

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