Application of interpretable machine learning algorithms to predict distant metastasis in osteosarcoma
Abstract Background Osteosarcoma is well‐established as the most common bone cancer in children and adolescents. Patients with localized disease have different prognoses and management than those with metastasis at the time of diagnosis. The purpose of this study was to explore potential risk factor...
Main Authors: | Bing‐li Bai, Zong‐yi Wu, She‐ji Weng, Qing Yang |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2023-02-01
|
Series: | Cancer Medicine |
Subjects: | |
Online Access: | https://doi.org/10.1002/cam4.5225 |
Similar Items
-
Characteristics and Prognostic Factors in Gastric Adenocarcinoma Patients with Distant Metastasis
by: Tien Manh Hoang, et al.
Published: (2022-01-01) -
The prediction of distant metastasis risk for male breast cancer patients based on an interpretable machine learning model
by: Xuhai Zhao, et al.
Published: (2023-04-01) -
Prediction model of ocular metastasis from primary liver cancer: Machine learning‐based development and interpretation study
by: Jin‐Qi Sun, et al.
Published: (2023-10-01) -
Risk factors for distant metastasis and prognosis in stage T1 esophageal cancer: A population-based study
by: Kai Zhu, et al.
Published: (2023-01-01) -
Clinical models to predict lymph nodes metastasis and distant metastasis in newly diagnosed early esophageal cancer patients: A population‐based study
by: Hong Chen, et al.
Published: (2023-03-01)