A machine learning-based model for clinical prediction of distal metastasis in chondrosarcoma: a multicenter, retrospective study

PeerJ. 2023 Dec 18:11:e16485. doi: 10.7717/peerj.16485. eCollection 2023.

Abstract

Background: The occurrence of distant metastases (DM) limits the overall survival (OS) of patients with chondrosarcoma (CS). Early diagnosis and treatment of CS remains a great challenge in clinical practice. The aim of this study was to investigate metastatic factors and develop a risk stratification model for clinicians' decision-making.

Methods: Six machine learning (ML) algorithms, including logistic regression (LR), plain Bayesian classifier (NBC), decision tree (DT), random forest (RF), gradient boosting machine (GBM) and extreme gradient boosting (XGBoost). A 10-fold cross-validation was performed for each model separately, multicenter data was used as external validation, and the best (highest AUC) model was selected to build the network calculator.

Results: A total of 1,385 patients met the inclusion criteria, including 82 (5.9%) patients with metastatic CS. Multivariate logistic regression analysis showed that the risk of DM was significantly higher in patients with higher pathologic grades, T-stage, N-stage, and non-left primary lesions, as well as those who did not receive surgery and chemotherapy. The AUC of the six ML algorithms for predicting DM ranged from 0.911-0.985, with the extreme gradient enhancement algorithm (XGBoost) having the highest AUC. Therefore, we used the XGB model and uploaded the results to an online risk calculator for estimating DM risk.

Conclusions: In this study, combined with adequate SEER case database and external validation with data from multicenter institutions in different geographic regions, we confirmed that CS, T, N, laterality, and grading of surgery and chemotherapy were independent risk factors for DM. Based on the easily available clinical risk factors, machine learning algorithms built the XGB model that predicts the best outcome for DM. An online risk calculator helps simplify the patient assessment process and provides decision guidance for precision medicine and long-term cancer surveillance, which contributes to the interpretability of the model.

Keywords: Chondrosarcoma; Distant metastases; Machine learning; Risk factor.

Publication types

  • Multicenter Study

MeSH terms

  • Bayes Theorem
  • Bone Neoplasms* / diagnosis
  • Bone Neoplasms* / pathology
  • Chondrosarcoma* / diagnosis
  • Chondrosarcoma* / pathology
  • Humans
  • Machine Learning
  • Neoplasm Metastasis
  • Retrospective Studies

Grants and funding

This work was supported by grants from National Natural Science Foundation of China (82260944), the Key research and development programs of Guangxi, China (2021AB09011) and the Innovation Project of Guangxi Graduate Education of GXUCM, (YCBXJ2023006). The Guangxi Science and Technology Major Program, Grant No. GUIKEAA23023035 supported the APC of this article. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.