International Validation of the SORG Machine-learning Algorithm for Predicting the Survival of Patients with Extremity Metastases Undergoing Surgical Treatment

Abstract

Background The Skeletal Oncology Research Group machine-learning algorithms (SORG-MLAs) estimate 90day 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. Questions/purposes (1) Is the SORG-MLA generalizable to Taiwanese patients for predicting 90-day and 1-year Each author certifies that there are no funding or commercial associations (consultancies, stock ownership, equity interest, patent/licensing arrangements, etc.) that might pose a conflict of interest in connection with the submitted article related to the author or any immediate family members. All ICMJE Conflict of Interest Forms for authors and Clinical Orthopaedics and Related Research® editors and board members are on file with the publication and can be viewed on request.

Cardiac Substructures Dosimetric Predictors for Cardiac Toxicity After Definitive Radiotherapy in Esophageal Cancer

“…Additionally, the Brier score of a prediction model must be compared with the null model that ignored all the covariates, with a Brier score lower than the null model indicating better algorithm performance. 27 In this study, all the constructed prediction models had lower Brier scores than the corresponding null models. A significant difference was defined as a two-sided P value of ≤ 0.05.…”

Survival Prediction in Patients Treated Surgically for Metastases of the Appendicular Skeleton—An External Validation of 2013-SPRING Model

“…In order to aid decision making, some models to predict survival in bone metastasis patients with impending or pathological fractures have been developed in the past [ 13 , 15 , 16 ], including the 2013-SPRING model [ 17 ]. Their validation—both internally and externally—is necessary in order to evaluate whether models tend to over- or underestimate remaining prognosis [ 20 ].…”

“…In order to estimate prognosis of bone metastasis patients with impending or pathological fractures prior to surgery, some prognostic models have been developed in the past. These include the (modified) Bauer Score for spinal lesions [ 11 , 12 ], the SORG Machine-Learning Algorithm (SORG-MLA) [ 13 ] and OPTIModel [ 14 ] for both spinal and extremity metastases, as well as the PATHFx model [ 15 ], the 2008-SPRING model [ 16 ] and its updated version, the 2013-SPRING model for extremity lesions [ 17 ]. The latter model uses readily available clinical variables in order to estimate patients’ survival probability within 3, 6 and 12 months after surgery.…”

Can a Bayesian belief network for survival prediction in patients with extremity metastases (PATHFx) be externally validated in an Asian cohort of 356 surgically treated patients?

“…It has also been recently updated to the 3rd version (3) to provide predictions for patients treated both operatively and with radiation only. However, some studies suggested PSSs could perform differently between ethnogeographically distinct cohorts (4,5,(8)(9)(10)(11)(12), and they should have been validated before being applied onto a specific population. The PATHFx offers survival predictions at 6 different time points: 1, 3, 6, 12, 18, and 24 months.…”