Automated Gross Tumor Volume (GTV) Contouring in High-Grade Gliomas Using a Deep Learning Approach

Abstract

Rationale and Objectives

Accurate contouring of the Gross Tumor Volume (GTV) in High-Grade Gliomas (HGGs) is a cornerstone of effective Radiation Therapy (RT) planning, as it influences tumor control and spares normal tissue, thereby directly impacting treatment precision. However, the standard manual approach to GTV contouring requires considerable time and is prone to inter-observer variability. Accordingly, this study presents a deep learning framework for automatic GTV contouring in HGG cases.

Materials and Methods

A modified 3D U-Net architecture was employed and trained on 469 subjects sourced from the Brain Tumor Segmentation (BraTS) 2018–2019 challenges, with multi-sequence magnetic resonance imaging (MRI) to enhance feature learning. The GTV was delineated following the European Society for Radiotherapy and Oncology (ESTRO) and the European Association of Neuro-Oncology (EANO) guidelines, based on the contrast-enhancing region of the tumor on post-contrast T1-weighted images, excluding edema. This corresponds to the enhancing tumor and necrotic core labels in our dataset. The segmentation accuracy was assessed using the Dice Similarity Coefficient (DSC) and the 95th-percentile Hausdorff Distance (HD95).

Results

The proposed model yielded a DSC of 91.70% ± 4.62% (mean ± standard deviation) and an HD95 of 2.43 ± 1.30 mm, indicating a high degree of overlap with minimal boundary deviation.

Conclusion

The results of our study highlight the potential of deep learning as a promising and efficient solution for GTV contouring in HGGs, supporting RT planning, improving clinical workflow, and enhancing treatment accuracy.