Optimization of treatment strategy by using a machine learning model to predict survival time of patients with malignant glioma after radiotherapy

Mizutani, T.; Magome, Taiki; Igaki, Hiroshi; Haga, Akihiro; Nawa, K.; Sekiya, Noritaka; Nakagawa, Keiichi

doi:10.1093/jrr/rrz066

Cited by 12 publications

(14 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Mizutani et al employed scans of 35 patients with malignant glioma, identifying 12 clinical features and 192 dose-volume histogram (DVH) features and used SVM to predict OS times after RT. They found that prediction accuracy was significantly improved with the combined use of clinical and DVH features compared with the separate use of each feature (66). Qiu et al compared RSF and traditional CPH to predict tumor progression after particle beam radiotherapy in 82 HGG patients and found that CPH demonstrated a better performance in terms of integrated C-index as compared to the RSF model (18).…”

Section: Imaging and Response To Treatmentmentioning

confidence: 99%

Survival Prediction in Gliomas: Current State and Novel Approaches

Zhao

Krauze

2021

Gliomas

View full text Add to dashboard Cite

Gliomas are neurologically devastating tumors with generally poor outcomes. Traditionally, survival prediction in glioma is studied from clinical features using statistical approaches. With the rapid development of artificial intelligence approaches encompassing machine learning and deep learning, there has been a keen interest among researchers to apply these methods to survival prediction in glioma allowing for integrated processes that encompass pathology, histology, molecular, imaging, and clinical features. This chapter provides an overview of the emerging computational approaches that have the potential to revolutionize survival prediction in glioma. Machine learning and deep learning techniques, including support vector machine, random forest, convolutional neural network, and radiomics, are discussed.

show abstract

Section: Imaging and Response To Treatmentmentioning

confidence: 99%

Survival Prediction in Gliomas: Current State and Novel Approaches

Zhao

Krauze

2021

Gliomas

View full text Add to dashboard Cite

show abstract

“…Machine learning (ML), a branch of artificial intelligence, has been employed to predict prognosis in a variety of cancer types. Noticeably, series of studies applying ML algorithms to predict the survival of HGG under standard photon-based radiotherapy have reported good performance in recent years (8)(9)(10)(11)(12)(13). However, it is still controversial that which methods among ML algorithms and conventional modeling can achieve better performance in survival analysis, particularly in terms of time-to-event censored data (14)(15)(16).…”

Section: Introductionmentioning

confidence: 99%

A Comparison Study of Machine Learning (Random Survival Forest) and Classic Statistic (Cox Proportional Hazards) for Predicting Progression in High-Grade Glioma after Proton and Carbon Ion Radiotherapy

et al. 2020

View full text Add to dashboard Cite

Background: Machine learning (ML) algorithms are increasingly explored in glioma prognostication. Random survival forest (RSF) is a common ML approach in analyzing time-to-event survival data. However, it is controversial which method between RSF and traditional cornerstone method Cox proportional hazards (CPH) is better fitted. The purpose of this study was to compare RSF and CPH in predicting tumor progression of high-grade glioma (HGG) after particle beam radiotherapy (PBRT). Methods: The study enrolled 82 consecutive HGG patients who were treated with PBRT at Shanghai Proton and Heavy Ion Center between 6/2015 and 11/2019. The entire cohort was split into the training and testing set in an 80/20 ratio. Ten variables from patient-related, tumor-related and treatment-related information were utilized for developing CPH and RSF for predicting progression-free survival (PFS). The model performance was compared in concordance index (C-index) for discrimination (accuracy), brier score (BS) for calibration (precision) and variable importance for interpretability. Results: The CPH model demonstrated a better performance in terms of integrated Cindex (62.9%) and BS (0.159) compared to RSF model (C-index = 61.1%, BS = 0.174). In the context of variable importance, CPH model indicated that age (P = 0.024), WHO grade (P = 0.020), IDH gene (P = 0.019), and MGMT promoter status (P = 0.040) were significantly correlated with PFS in the univariate analysis; multivariate analysis showed that age (P = 0.041), surgical completeness (P = 0.084), IDH gene (P = 0.057), and MGMT promoter (P = 0.092) had a significant or trend toward the relation with PFS. RSF showed

show abstract

“…First, there are many reports on the survival analysis of patients with HGG based on clinicopathological factors and data mining of imaging variables. However, only a limited number of studies have investigated the importance of DVH for survival 36 . In this study, clinical and DVH features were combined to construct a prognostic prediction model, and we found that radiation dose information can affect prognosis.…”

Section: Discussionmentioning

confidence: 93%

A machine learning‐based survival prediction model of high grade glioma by integration of clinical and dose‐volume histogram parameters

Chen

Zheng

et al. 2021

Cancer Medicine

View full text Add to dashboard Cite

Purpose Glioma is the most common type of primary brain tumor in adults, and it causes significant morbidity and mortality, especially in high‐grade glioma (HGG) patients. The accurate prognostic prediction of HGG is vital and helpful for clinicians when developing therapeutic strategies. Therefore, we propose a machine learning‐based survival prediction model by analyzing clinical and dose‐volume histogram (DVH) parameters, to improve the performance of the risk model in HGG patients. Methods Eight clinical variables and 39 DVH parameters were extracted for each patient, who received radiotherapy for HGG with active follow‐up. Ninety‐five patients were randomly divided into training and testing cohorts, and we employed random survival forest (RSF), support vector machine (SVM), and Cox proportional hazards (CPHs) models to predict survival. Calibration plots, concordance indexes, and decision curve analyses were used to evaluate the calibration, discrimination, and clinical utility of these three models. Results The RSF model showed the best performance among the three models, with concordance indexes of 0.824 and 0.847 in the training and testing sets, respectively, followed by the SVM (0.792/0.823) and CPH (0.821/0.811) models. Specifically, in the RSF model, we identified age, gross tumor volume (GTV), grade, Karnofsky performance status (KPS), isocitrate dehydrogenase (IDH), and D99 as important variables associated with survival. The AUCs of the testing set were 92.4%, 87.7%, and 84.0% for 1‐, 2‐, and 3‐year survival, respectively. According to this model, HGG patients can be divided into high‐ and low‐risk groups. Conclusion The machine learning‐based RSF model integrating both clinical and DVH variables is an improved and useful tool for predicting the survival of HGG patients.

show abstract

Optimization of treatment strategy by using a machine learning model to predict survival time of patients with malignant glioma after radiotherapy

Cited by 12 publications

References 32 publications

Survival Prediction in Gliomas: Current State and Novel Approaches

Survival Prediction in Gliomas: Current State and Novel Approaches

A Comparison Study of Machine Learning (Random Survival Forest) and Classic Statistic (Cox Proportional Hazards) for Predicting Progression in High-Grade Glioma after Proton and Carbon Ion Radiotherapy

A machine learning‐based survival prediction model of high grade glioma by integration of clinical and dose‐volume histogram parameters

Contact Info

Product

Resources

About