Encrypted login | home

Program Information

Comparison of Survival-Time Prediction Models After Radiotherapy for High-Grade Glioma Patients Based On Clinical and DVH Features

no image available
T Magome

T Magome*, A Haga , H Igaki , N Sekiya , Y Masutani , A Sakumi , A Mukasa , K Nakagawa , The University of Tokyo Hospital, Tokyo, Japan


TH-E-BRF-5 Thursday 1:00PM - 2:50PM Room: Ballroom F

Although many outcome prediction models based on dose-volume information have been proposed, it is well known that the prognosis may be affected also by multiple clinical factors. The purpose of this study is to predict the survival time after radiotherapy for high-grade glioma patients based on features including clinical and dose-volume histogram (DVH) information.

A total of 35 patients with high-grade glioma (oligodendroglioma: 2, anaplastic astrocytoma: 3, glioblastoma: 30) were selected in this study. All patients were treated with prescribed dose of 30 - 80 Gy after surgical resection or biopsy from 2006 to 2013 at The University of Tokyo Hospital. All cases were randomly separated into training dataset (30 cases) and test dataset (5 cases). The survival time after radiotherapy was predicted based on a multiple linear regression analysis and artificial neural network (ANN) by using 204 candidate features. The candidate features included the 12 clinical features (tumor location, extent of surgical resection, treatment duration of radiotherapy, etc.), and the 192 DVH features (maximum dose, minimum dose, D95, V60, etc.). The effective features for the prediction were selected according to a step-wise method by using 30 training cases. The prediction accuracy was evaluated by a coefficient of determination (R²) between the predicted and actual survival time for the training and test dataset.

In the multiple regression analysis, the value of R² between the predicted and actual survival time was 0.460 for the training dataset and 0.375 for the test dataset. On the other hand, in the ANN analysis, the value of R² was 0.806 for the training dataset and 0.811 for the test dataset.

Although a large number of patients would be needed for more accurate and robust prediction, our preliminary result showed the potential to predict the outcome in the patients with high-grade glioma.

Funding Support, Disclosures, and Conflict of Interest: This work was partly supported by the JSPS Core-to-Core Program(No. 23003) and Grant-in-aid from the JSPS Fellows.

Contact Email: