Development and comparison of 1-year survival models in patients with primary bone sarcomas: External validation of a Bayesian belief network model and creation and external validation of a new gradient boosting machine model
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Development and comparison of 1-year survival models in patients with primary bone sarcomas : External validation of a Bayesian belief network model and creation and external validation of a new gradient boosting machine model. / Holm, Christina E.; Grazal, Clare F.; Raedkjaer, Mathias; Baad-Hansen, Thomas; Nandra, Rajpal; Grimer, Robert; Forsberg, Jonathan A.; Petersen, Michael Moerk; Soerensen, Michala Skovlund.
I: Sage Open Medicine, Bind 10, 20503121221076387, 02.2022.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Development and comparison of 1-year survival models in patients with primary bone sarcomas
T2 - External validation of a Bayesian belief network model and creation and external validation of a new gradient boosting machine model
AU - Holm, Christina E.
AU - Grazal, Clare F.
AU - Raedkjaer, Mathias
AU - Baad-Hansen, Thomas
AU - Nandra, Rajpal
AU - Grimer, Robert
AU - Forsberg, Jonathan A.
AU - Petersen, Michael Moerk
AU - Soerensen, Michala Skovlund
PY - 2022/2
Y1 - 2022/2
N2 - Background: Bone sarcomas often present late with advanced stage at diagnosis and an according, varying short-term survival. In 2016, Nandra et al. generated a Bayesian belief network model for 1-year survival in patients with bone sarcomas. The purpose of this study is: (1) to externally validate the prior 1-year Bayesian belief network prediction model for survival in patients with bone sarcomas and (2) to develop a gradient boosting machine model using Nandra et al.'s cohort and evaluate whether the gradient boosting machine model outperforms the Bayesian belief network model when externally validated in an independent Danish population cohort.Material and Methods: The training cohort comprised 3493 patients newly diagnosed with bone sarcoma from the institutional prospectively maintained database at the Royal Orthopaedic Hospital, Birmingham, UK. The validation cohort comprised 771 patients with newly diagnosed bone sarcoma included from the Danish Sarcoma Registry during January 1, 2000-June 22, 2016. We performed area under receiver operator characteristic curve analysis, Brier score and decision curve analysis to evaluate the predictive performance of the models.Results: External validation of the Bayesian belief network 1-year prediction model demonstrated an area under receiver operator characteristic curve of 68% (95% confidence interval, 62%-73%). Area under receiver operator characteristic curve of the gradient boosting machine model demonstrated: 75% (95% confidence interval: 70%-80%), overall model performance by the Brier score was 0.09 (95% confidence interval: 0.077-0.11) and decision curve analysis demonstrated a positive net benefit for threshold probabilities above 0.5. External validation of the developed gradient boosting machine model demonstrated an area under receiver operator characteristic curve of 63% (95% confidence interval: 57%-68%), and the Brier score was 0.14 (95% confidence interval: 0.12-0.16).Conclusion: External validation of the 1-year Bayesian belief network survival model yielded a poor outcome based on a Danish population cohort validation. We successfully developed a gradient boosting machine 1-year survival model. The gradient boosting machine did not outperform the Bayesian belief network model based on external validation in a Danish population-based cohort.
AB - Background: Bone sarcomas often present late with advanced stage at diagnosis and an according, varying short-term survival. In 2016, Nandra et al. generated a Bayesian belief network model for 1-year survival in patients with bone sarcomas. The purpose of this study is: (1) to externally validate the prior 1-year Bayesian belief network prediction model for survival in patients with bone sarcomas and (2) to develop a gradient boosting machine model using Nandra et al.'s cohort and evaluate whether the gradient boosting machine model outperforms the Bayesian belief network model when externally validated in an independent Danish population cohort.Material and Methods: The training cohort comprised 3493 patients newly diagnosed with bone sarcoma from the institutional prospectively maintained database at the Royal Orthopaedic Hospital, Birmingham, UK. The validation cohort comprised 771 patients with newly diagnosed bone sarcoma included from the Danish Sarcoma Registry during January 1, 2000-June 22, 2016. We performed area under receiver operator characteristic curve analysis, Brier score and decision curve analysis to evaluate the predictive performance of the models.Results: External validation of the Bayesian belief network 1-year prediction model demonstrated an area under receiver operator characteristic curve of 68% (95% confidence interval, 62%-73%). Area under receiver operator characteristic curve of the gradient boosting machine model demonstrated: 75% (95% confidence interval: 70%-80%), overall model performance by the Brier score was 0.09 (95% confidence interval: 0.077-0.11) and decision curve analysis demonstrated a positive net benefit for threshold probabilities above 0.5. External validation of the developed gradient boosting machine model demonstrated an area under receiver operator characteristic curve of 63% (95% confidence interval: 57%-68%), and the Brier score was 0.14 (95% confidence interval: 0.12-0.16).Conclusion: External validation of the 1-year Bayesian belief network survival model yielded a poor outcome based on a Danish population cohort validation. We successfully developed a gradient boosting machine 1-year survival model. The gradient boosting machine did not outperform the Bayesian belief network model based on external validation in a Danish population-based cohort.
KW - Artificial intelligence
KW - bone sarcoma
KW - machine learning
KW - prediction
KW - survival
KW - SOFT-TISSUE SARCOMAS
KW - HIGH-GRADE OSTEOSARCOMA
KW - PROGNOSTIC-FACTORS
KW - PREDICTION MODELS
KW - CLINICAL-TRIAL
KW - EXTREMITY
KW - CHEMOTHERAPY
KW - PERFORMANCE
KW - PROTEIN
U2 - 10.1177/20503121221076387
DO - 10.1177/20503121221076387
M3 - Journal article
C2 - 35154743
VL - 10
JO - SAGE Open Medicine
JF - SAGE Open Medicine
SN - 2050-3121
M1 - 20503121221076387
ER -
ID: 316404343