Development and validation of a machine learning-based postoperative prognostic model for plasma cell neoplasia with spinal lesions as initial clinical manifestations: a single-center cohort study.

Background: Spinal multiple myeloma (MM) and solitary plasmacytoma of bone (SPB), both plasma cell neoplasms, greatly affect patients’ quality of life due to spinal involvement. Accurate prediction of surgical outcomes is crucial for personalized patient care, but systematic treatment guidelines and predictive models are lacking.This study aimed to develop and validate a machine learning (ML)-based model to predict postoperative outcomes and identify prognostic factors for patients with spinal MM and SPB.A retrospective analysis was conducted on patients diagnosed with MM or SPB from 2011 to 2015, followed by prospective data collection from 2016 to 2017. Patient demographics, tumor characteristics, clinical treatments, and laboratory results were analyzed as input features. Four types of ML algorithms were employed for model development. The performance was assessed using discrimination and calibration measures, and the Shapley Additive exPlanations (SHAP) method was applied for model interpretation.A total of 169 patients were included, with 119 for model training and 50 for validation. The Gaussian Naïve Bayes (GNB) model exhibited superior predictive accuracy and stability. Prospective validation on the 50 patients revealed an area under the curve (AUC) of 0.863, effectively distinguishing between 5-year survivors and non-survivors. Key prognostic factors identified included International Staging System (ISS) stage, Durie–Salmon (DS) stage, targeted therapy, and age.The GNB model has the best performance and high reliability in predicting postoperative outcomes. Variables such as ISS stage and DS stage were significant in influencing patient prognosis. This study enhances the ability to identify patients at risk of poor outcomes, thereby aiding clinical decision-making.Objective: Spinal multiple myeloma (MM) and solitary plasmacytoma of bone (SPB), both plasma cell neoplasms, greatly affect patients’ quality of life due to spinal involvement. Accurate prediction of surgical outcomes is crucial for personalized patient care, but systematic treatment guidelines and predictive models are lacking.This study aimed to develop and validate a machine learning (ML)-based model to predict postoperative outcomes and identify prognostic factors for patients with spinal MM and SPB.A retrospective analysis was conducted on patients diagnosed with MM or SPB from 2011 to 2015, followed by prospective data collection from 2016 to 2017. Patient demographics, tumor characteristics, clinical treatments, and laboratory results were analyzed as input features. Four types of ML algorithms were employed for model development. The performance was assessed using discrimination and calibration measures, and the Shapley Additive exPlanations (SHAP) method was applied for model interpretation.A total of 169 patients were included, with 119 for model training and 50 for validation. The Gaussian Naïve Bayes (GNB) model exhibited superior predictive accuracy and stability. Prospective validation on the 50 patients revealed an area under the curve (AUC) of 0.863, effectively distinguishing between 5-year survivors and non-survivors. Key prognostic factors identified included International Staging System (ISS) stage, Durie–Salmon (DS) stage, targeted therapy, and age.The GNB model has the best performance and high reliability in predicting postoperative outcomes. Variables such as ISS stage and DS stage were significant in influencing patient prognosis. This study enhances the ability to identify patients at risk of poor outcomes, thereby aiding clinical decision-making.Methods: Spinal multiple myeloma (MM) and solitary plasmacytoma of bone (SPB), both plasma cell neoplasms, greatly affect patients’ quality of life due to spinal involvement. Accurate prediction of surgical outcomes is crucial for personalized patient care, but systematic treatment guidelines and predictive models are lacking.This study aimed to develop and validate a machine learning (ML)-based model to predict postoperative outcomes and identify prognostic factors for patients with spinal MM and SPB.A retrospective analysis was conducted on patients diagnosed with MM or SPB from 2011 to 2015, followed by prospective data collection from 2016 to 2017. Patient demographics, tumor characteristics, clinical treatments, and laboratory results were analyzed as input features. Four types of ML algorithms were employed for model development. The performance was assessed using discrimination and calibration measures, and the Shapley Additive exPlanations (SHAP) method was applied for model interpretation.A total of 169 patients were included, with 119 for model training and 50 for validation. The Gaussian Naïve Bayes (GNB) model exhibited superior predictive accuracy and stability. Prospective validation on the 50 patients revealed an area under the curve (AUC) of 0.863, effectively distinguishing between 5-year survivors and non-survivors. Key prognostic factors identified included International Staging System (ISS) stage, Durie–Salmon (DS) stage, targeted therapy, and age.The GNB model has the best performance and high reliability in predicting postoperative outcomes. Variables such as ISS stage and DS stage were significant in influencing patient prognosis. This study enhances the ability to identify patients at risk of poor outcomes, thereby aiding clinical decision-making.Results: Spinal multiple myeloma (MM) and solitary plasmacytoma of bone (SPB), both plasma cell neoplasms, greatly affect patients’ quality of life due to spinal involvement. Accurate prediction of surgical outcomes is crucial for personalized patient care, but systematic treatment guidelines and predictive models are lacking.This study aimed to develop and validate a machine learning (ML)-based model to predict postoperative outcomes and identify prognostic factors for patients with spinal MM and SPB.A retrospective analysis was conducted on patients diagnosed with MM or SPB from 2011 to 2015, followed by prospective data collection from 2016 to 2017. Patient demographics, tumor characteristics, clinical treatments, and laboratory results were analyzed as input features. Four types of ML algorithms were employed for model development. The performance was assessed using discrimination and calibration measures, and the Shapley Additive exPlanations (SHAP) method was applied for model interpretation.A total of 169 patients were included, with 119 for model training and 50 for validation. The Gaussian Naïve Bayes (GNB) model exhibited superior predictive accuracy and stability. Prospective validation on the 50 patients revealed an area under the curve (AUC) of 0.863, effectively distinguishing between 5-year survivors and non-survivors. Key prognostic factors identified included International Staging System (ISS) stage, Durie–Salmon (DS) stage, targeted therapy, and age.The GNB model has the best performance and high reliability in predicting postoperative outcomes. Variables such as ISS stage and DS stage were significant in influencing patient prognosis. This study enhances the ability to identify patients at risk of poor outcomes, thereby aiding clinical decision-making.Conclusions: Spinal multiple myeloma (MM) and solitary plasmacytoma of bone (SPB), both plasma cell neoplasms, greatly affect patients’ quality of life due to spinal involvement. Accurate prediction of surgical outcomes is crucial for personalized patient care, but systematic treatment guidelines and predictive models are lacking.This study aimed to develop and validate a machine learning (ML)-based model to predict postoperative outcomes and identify prognostic factors for patients with spinal MM and SPB.A retrospective analysis was conducted on patients diagnosed with MM or SPB from 2011 to 2015, followed by prospective data collection from 2016 to 2017. Patient demographics, tumor characteristics, clinical treatments, and laboratory results were analyzed as input features. Four types of ML algorithms were employed for model development. The performance was assessed using discrimination and calibration measures, and the Shapley Additive exPlanations (SHAP) method was applied for model interpretation.A total of 169 patients were included, with 119 for model training and 50 for validation. The Gaussian Naïve Bayes (GNB) model exhibited superior predictive accuracy and stability. Prospective validation on the 50 patients revealed an area under the curve (AUC) of 0.863, effectively distinguishing between 5-year survivors and non-survivors. Key prognostic factors identified included International Staging System (ISS) stage, Durie–Salmon (DS) stage, targeted therapy, and age.The GNB model has the best performance and high reliability in predicting postoperative outcomes. Variables such as ISS stage and DS stage were significant in influencing patient prognosis. This study enhances the ability to identify patients at risk of poor outcomes, thereby aiding clinical decision-making. [ABSTRACT FROM AUTHOR]