Prediction of Pseudoprogression Versus Progression Using Machine Learning Algorithm in Glioblastoma

B.S Jang,S.H Jeon,I.H Kim,I.A Kim

doi:10.1016/j.ijrobp.2018.06.172

Abstract

We aimed to investigate the feasibility of machine learning (ML) algorithm to distinguish pseudoprogression (PsPD) from progression (PD) in patients with glioblastoma (GBM). We recruited the patients diagnosed as primary GBM who received gross total resection (GTR) and concurrent chemoradiotherapy in two institutions from April 2010 to April 2017 and presented suspicious contrast-enhanced lesion on brain magnetic resonance imaging (MRI) during follow-up. Patients from two institutions were allocated to training (N = 59) and testing (N = 19) datasets, respectively. We developed a convolutional neural network combined with a long short-term memory ML structure. MRI data, which was 9 axial post-contrast T1-weighted images in our study, and clinical features were incorporated (Model 1). In the testing set, the trained Model 1 resulted in AUC of 0.83, AUPRC of 0.87, and F1-score of 0.74 using optimal threshold. The performance was superior to that of Model 2 (CNN-LSTM model with MRI data alone) and Model 3 (random forest model with clinical feature alone). The developed algorithm involving MRI data and clinical features could help making decision during follow-up of patients with GBM treated with GTR and concurrent CCRT.

Highlights

Purpose/Objective(s): Accurate delineation of tumor volumes and organs-at-risk is critical in radiation therapy treatment planning
Patients were divided into training, validation, and test datasets according to a 70/20/10 split, and four additional patients served as a second test dataset for comparison against manual segmentation by resident physicians
On the resident test dataset, the mean Dice similarity coefficient was 82% for the prostate, 63% for the seminal vesicles, 85% for the rectum, and 86% for the bladder. These results were comparable to the contours generated by four radiation oncology residents of all training levels (PGY 2-5), who had a mean Dice similarity coefficient of 78% for the prostate, 67% for the seminal vesicles, 87% for the rectum, and 91% for the bladder (Table 1)

Summary

Automatic Segmentation Using Convolutional Neural Networks in Prostate Cancer

Commonly segmented organs include the prostate, seminal vesicles, bladder, and rectum. The purpose of this study was to evaluate a deep convolutional neural network algorithm for automatic segmentation in prostate cancer. On the resident test dataset, the mean Dice similarity coefficient was 82% for the prostate, 63% for the seminal vesicles, 85% for the rectum, and 86% for the bladder These results were comparable to the contours generated by four radiation oncology residents of all training levels (PGY 2-5), who had a mean Dice similarity coefficient of 78% for the prostate, 67% for the seminal vesicles, 87% for the rectum, and 91% for the bladder (Table 1). Conclusion: Convolutional neural networks can achieve auto-segmentation results in prostate cancer that are comparable to human manual segmentation by radiation oncology residents.

Seminal Vesicles

Findings

Recall Rate