Open AccessInternational Validation of the SORG Machine-learning Algorithm for Predicting the Survival of Patients with Extremity Metastases Undergoing Surgical Treatment
Author(s) -
TingEn Tseng,
ChiaChe Lee,
HungKuan Yen,
Olivier Q. Groot,
ChunHan Hou,
Shin-Ying Lin,
Michiel E R Bongers,
MingHsiao Hu,
Aditya V. Karhade,
Jia-Chi Ko,
Yi-Hsiang Lai,
Jing-Jen Yang,
JorritJan Verlaan,
RongSen Yang,
Joseph H. Schwab,
Wei-Hsin Lin
Publication year - 2021
Publication title -
clinical orthopaedics and related research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.178
H-Index - 204
eISSN - 1528-1132
pISSN - 0009-921X
DOI - 10.1097/corr.0000000000001969
Subject(s) - medicine , cohort , intramedullary rod , algorithm , surgery , oncology , computer science
The Skeletal Oncology Research Group machine-learning algorithms (SORG-MLAs) estimate 90-day and 1-year survival in patients with long-bone metastases undergoing surgical treatment and have demonstrated good discriminatory ability on internal validation. However, the performance of a prediction model could potentially vary by race or region, and the SORG-MLA must be externally validated in an Asian cohort. Furthermore, the authors of the original developmental study did not consider the Eastern Cooperative Oncology Group (ECOG) performance status, a survival prognosticator repeatedly validated in other studies, in their algorithms because of missing data.