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A deep learning model for accurately predicting cancer-specific survival in patients with primary bone sarcoma of the extremity: a population-based study

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Abstract

Purpose

Primary bone and joint sarcomas of the long bone are relatively rare neoplasms with poor prognosis. An efficient clinical tool that can accurately predict patient prognosis is not available. The current study aimed to use deep learning algorithms to develop a prediction model for the prognosis of patients with long bone sarcoma.

Methods

Data of patients with long bone sarcoma in the extremities was collected from the Surveillance, Epidemiology, and End Results Program database from 2004 to 2014. Univariate and multivariate analyses were performed to select possible prediction features. DeepSurv, a deep learning model, was constructed for predicting cancer-specific survival rates. In addition, the classical cox proportional hazards model was established for comparison. The predictive accuracy of our models was assessed using the C-index, Integrated Brier Score, receiver operating characteristic curve, and calibration curve.

Results

Age, tumor extension, histological grade, tumor size, surgery, and distant metastasis were associated with cancer-specific survival in patients with long bone sarcoma. According to loss function values, our models converged successfully and effectively learned the survival data of the training cohort. Based on the C-index, area under the curve, calibration curve, and Integrated Brier Score, the deep learning model was more accurate and flexible in predicting survival rates than the cox proportional hazards model.

Conclusion

A deep learning model for predicting the survival probability of patients with long bone sarcoma was constructed and validated. It is more accurate and flexible in predicting prognosis than the classical CoxPH model.

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Data availability

Publicly available datasets were analyzed in this study. The datasets used and analyzed in the study are available from the corresponding author on reasonable request.

Abbreviations

C-index:

Concordance index

CSS:

Cancer-specific survival

ROC:

Receiver operating characteristic

AUC:

Area under the curve

SEER:

Surveillance, Epidemiology, and End Results

IBS:

Integrated Brier Score

ML:

Machine learning

CoxPH:

Cox proportional hazards

AI:

Artificial intelligence

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Funding

This work was supported by National Natural Science Foundation of China (No. 31971272), Sanming Project of Medicine in Shenzhen (No. SZSM201911011) and International Science and Technology Cooperation Key Program project of Shaanxi Province (No.2023-GHZD-25).

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Author notes

  1. Debin Cheng, Dong Liu and Xian Li have contributed equally to this work.

Authors and Affiliations

  1. Department of Orthopaedics, Xijing Hospital, The Fourth Military Medical University, Xi’an, 710032, China

    Debin Cheng, Dong Liu, Zhenzhou Mi, Zhao Zhang, Weidong Tao, Jingyi Dang, Dongze Zhu, Jun Fu & Hongbin Fan

  2. Department of Orthopaedics, Shenzhen University General Hospital, Shenzhen, 518052, China

    Xian Li

Authors
  1. Debin Cheng
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  2. Dong Liu
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  3. Xian Li
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  5. Zhao Zhang
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Contributions

Data curation, ZM; Formal analysis, DC; Funding acquisition, HF; Investigation, XL, ZZ, JD, DZ; Methodology, DC, DL, ZZ, and JF; Project administration, HF; Resources, DC, ZM; Software, DC and WT; Supervision, HF; Validation, DL and XL; Visualization, HF; Writing–original draft, DC; Writing–review & editing, HF.

Corresponding author

Correspondence to Hongbin Fan.

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Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Ethical approval

Because the SEER database is a publicly available database of de-identified patient data, no ethics committee review was required for its use in this project.

Informed consent

For this type of study, formal consent is not required.

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Cheng, D., Liu, D., Li, X. et al. A deep learning model for accurately predicting cancer-specific survival in patients with primary bone sarcoma of the extremity: a population-based study. Clin Transl Oncol 26, 709–719 (2024). https://doi.org/10.1007/s12094-023-03291-6

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