Recursive Partitioning Analysis (RPA) of Prognostic Factors for Overall Survival in Patients with Spinal Metastasis: A New System for Stratified Treatment

Yang, Xiong‐gang; Wang, Feng; Feng, Jiangtao; Hu, Yongcheng; Lun, Dengxing; Hua, Kun‐chi; Liu, Yong-Heng; Zhang, Hao; Yang, Li

doi:10.1016/j.wneu.2019.02.183

Cited by 6 publications

(3 citation statements)

References 33 publications

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“…16 In a smaller study of 181 patients, Combs et al found KPS and primary diagnosis to be most predictive of prognosis in patient underdoing adjuvant radiation after surgical resection. 17 Previous studies have demonstrated RPA to be predictive of OS in patients receiving RT for metastatic disease [16][17][18][19] ; however, these studies have focused on treatment of brain metastases, triaging surgical versus conservative management of spinal disease, or the use of SBRT as the only radiation modality. To our knowledge, our study is the first to use RPA to evaluate OS in patient received RT for metastatic disease to the spine.…”

Section: Discussionmentioning

confidence: 99%

A Prognostic Index for Predicting Survival of Patients Undergoing Radiation Therapy for Spine Metastasis Using Recursive Partitioning Analysis

Walker

Bassale

Shukla

et al. 2022

Journal of Palliative Medicine

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Background: Physicians' estimate of life expectancy in patients with spine metastasis frequently impacts treatment decisions regarding surgery, radiation techniques, dose, and fractionation. Objective: We aimed to identify predictors of survival and generate a stratification schema that could guide clinical decision making. Materials and Methods: We identified 269 patients who have undergone surgery and/or radiation for spine metastasis from 2002 to 2013 at an academic medical institution in the United States. A univariate survival analysis was carried out using the Kaplan-Meier method. Differences in survival by histology were assessed using the logrank test. Multivariate analysis was performed using the Cox proportional hazards model, then using the same variables, recursive partitioning analysis (RPA) was conducted to determine risk groups associated with survival. Results: The median overall survival was 4.76 months. Twenty percent, 40%, and 57% of patients died within one, three, and six months of radiation treatment, respectively. RPA analysis resulted in three classes; class I included patients with Karnofsky Performance Status (KPS) ‡80. Class II included patients with KPS <80 and radioresistant or favorable histologies. Class III included all other histologies. Median survival in months was 11.4, 6.3, and 2.0, respectively. Conclusion:We developed a stratification schema predictive of survival in patients with spine metastasis. This RPA classification should be validated in independent patient populations from several institutions and may ultimately identify patients who are good candidates for more complex treatment regimens, such as stereotactic body radiotherapy.

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Section: Discussionmentioning

confidence: 99%

A Prognostic Index for Predicting Survival of Patients Undergoing Radiation Therapy for Spine Metastasis Using Recursive Partitioning Analysis

Walker

Bassale

Shukla

et al. 2022

Journal of Palliative Medicine

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“…Models were mainly developed using registry data (n = 21/62, 34%) and validated using retrospective cohorts (n = 4/14, 29%). Consecutive sampling was specified in only eight studies (13%) [49][50][51][52][53][54][55][56], random sampling was used in one study [57] and one study sampled individuals by screening their entire database for eligible individuals [58]. For most studies, however, sampling methods were not reported (n = 52/62, 85%).…”

Section: Study Design Features Data Source Sampling Treatment Details...mentioning

confidence: 99%

“…Of the 11 studies reporting calibration, three studies modelled for a time to event outcome. One study presented 3-and 5-year survival calibration plots [86], one study presented a linear regression and plot of the predicted and actual survival time [52], and one study presented a 1-year calibration plot [77].…”

Section: Model Performancementioning

confidence: 99%

Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review

Dhiman

Navarro

et al. 2022

BMC Med Res Methodol

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Background Describe and evaluate the methodological conduct of prognostic prediction models developed using machine learning methods in oncology. Methods We conducted a systematic review in MEDLINE and Embase between 01/01/2019 and 05/09/2019, for studies developing a prognostic prediction model using machine learning methods in oncology. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, Prediction model Risk Of Bias ASsessment Tool (PROBAST) and CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) to assess the methodological conduct of included publications. Results were summarised by modelling type: regression-, non-regression-based and ensemble machine learning models. Results Sixty-two publications met inclusion criteria developing 152 models across all publications. Forty-two models were regression-based, 71 were non-regression-based and 39 were ensemble models. A median of 647 individuals (IQR: 203 to 4059) and 195 events (IQR: 38 to 1269) were used for model development, and 553 individuals (IQR: 69 to 3069) and 50 events (IQR: 17.5 to 326.5) for model validation. A higher number of events per predictor was used for developing regression-based models (median: 8, IQR: 7.1 to 23.5), compared to alternative machine learning (median: 3.4, IQR: 1.1 to 19.1) and ensemble models (median: 1.7, IQR: 1.1 to 6). Sample size was rarely justified (n = 5/62; 8%). Some or all continuous predictors were categorised before modelling in 24 studies (39%). 46% (n = 24/62) of models reporting predictor selection before modelling used univariable analyses, and common method across all modelling types. Ten out of 24 models for time-to-event outcomes accounted for censoring (42%). A split sample approach was the most popular method for internal validation (n = 25/62, 40%). Calibration was reported in 11 studies. Less than half of models were reported or made available. Conclusions The methodological conduct of machine learning based clinical prediction models is poor. Guidance is urgently needed, with increased awareness and education of minimum prediction modelling standards. Particular focus is needed on sample size estimation, development and validation analysis methods, and ensuring the model is available for independent validation, to improve quality of machine learning based clinical prediction models.

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Novel machine‐learning prediction tools for overall survival of patients with chondrosarcoma: Based on recursive partitioning analysis

Yang,

Bao

et al. 2024

Cancer Medicine

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BackgroundChondrosarcoma (CHS), a bone malignancy, poses a significant challenge due to its heterogeneous nature and resistance to conventional treatments. There is a clear need for advanced prognostic instruments that can integrate multiple prognostic factors to deliver personalized survival predictions for individual patients. This study aimed to develop a novel prediction tool based on recursive partitioning analysis (RPA) to improve the estimation of overall survival for patients with CHS.MethodsData from the Surveillance, Epidemiology, and End Results (SEER) database were analyzed, including demographic, clinical, and treatment details of patients diagnosed between 2000 and 2018. Using C5.0 algorithm, decision trees were created to predict survival probabilities at 12, 24, 60, and 120 months. The performance of the models was assessed through confusion scatter plot, accuracy rate, receiver operator characteristic (ROC) curve, and area under ROC curve (AUC).ResultsThe study identified tumor histology, surgery, age, visceral (brain/liver/lung) metastasis, chemotherapy, tumor grade, and sex as critical predictors. Decision trees revealed distinct patterns for survival prediction at each time point. The models showed high accuracy (82.40%–89.09% in training group, and 82.16%–88.74% in test group) and discriminatory power (AUC: 0.806–0.894 in training group, and 0.808–0.882 in test group) in both training and testing datasets. An interactive web‐based shiny APP (URL: https://yangxg1209.shinyapps.io/chondrosarcoma_survival_prediction/) was developed, simplifying the survival prediction process for clinicians.ConclusionsThis study successfully employed RPA to develop a user‐friendly tool for personalized survival predictions in CHS. The decision tree models demonstrated robust predictive capabilities, with the interactive application facilitating clinical decision‐making. Future prospective studies are recommended to validate these findings and further refine the predictive model.

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Recursive Partitioning Analysis (RPA) of Prognostic Factors for Overall Survival in Patients with Spinal Metastasis: A New System for Stratified Treatment

Cited by 6 publications

References 33 publications

A Prognostic Index for Predicting Survival of Patients Undergoing Radiation Therapy for Spine Metastasis Using Recursive Partitioning Analysis

A Prognostic Index for Predicting Survival of Patients Undergoing Radiation Therapy for Spine Metastasis Using Recursive Partitioning Analysis

Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review

Novel machine‐learning prediction tools for overall survival of patients with chondrosarcoma: Based on recursive partitioning analysis

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