A Fully Automated Post-Surgical Brain Tumor Segmentation Model for Radiation Treatment Planning and Longitudinal Tracking

• Type of Material: Article
• Authors: Karthik K. Ramesh, Emory University; Karen M. Xu, Emory University; Anuradha G. Trivedi, Emory University; Vicki Huang, Emory University; Vahid Khalilzad Sharghi, Siemens Healthineers; Lawrence R. Kleinberg, Johns Hopkins University; Eric A. Mellon, University of Miami; Hui-Kuo Shu, Emory University; Hyunsuk Shim, Emory University; Brent Weinberg, Emory University
• Language: English
• Date: 2023-08-01
• Publisher: MDPI
• Publication Version: Final Published Version
• Copyright Statement: © 2023 by the authors.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.3390/cancers15153956
• Title of Journal or Parent Work: CANCERS
• Volume: 15
• Issue: 15
• Grant/Funding Information: This work is supported by the NIH U01CA264039 (H.S., E.A.M.), NIH R01CA214557 (H.S., L.R.K., H-G.S.), and pre-doctoral fellowship NIH F31CA247564 (K.K.R.).
• Keywords: Oncology; longitudinal tracking; deep learning; GLIOBLASTOMA; Science & Technology; radiation treatment; RADIOTHERAPY; CONCOMITANT; Life Sciences & Biomedicine; SURVIVAL; glioblastoma; ARTIFICIAL-INTELLIGENCE; ADJUVANT TEMOZOLOMIDE; SYSTEM; segmentation
• Research Categories: Health Sciences, Rehabilitation and Therapy; Health Sciences, Oncology

Abstract

Glioblastoma (GBM) has a poor survival rate even with aggressive surgery, concomitant radiation therapy (RT), and adjuvant chemotherapy. Standard-of-care RT involves irradiating a lower dose to the hyperintense lesion in T2-weighted fluid-attenuated inversion recovery MRI (T2w/FLAIR) and a higher dose to the enhancing tumor on contrast-enhanced, T1-weighted MRI (CE-T1w). While there have been several attempts to segment pre-surgical brain tumors, there have been minimal efforts to segment post-surgical tumors, which are complicated by a resection cavity and postoperative blood products, and tools are needed to assist physicians in generating treatment contours and assessing treated patients on follow up. This report is one of the first to train and test multiple deep learning models for the purpose of post-surgical brain tumor segmentation for RT planning and longitudinal tracking. Post-surgical FLAIR and CE-T1w MRIs, as well as their corresponding RT targets (GTV1 and GTV2, respectively) from 225 GBM patients treated with standard RT were trained on multiple deep learning models including: Unet, ResUnet, Swin-Unet, 3D Unet, and Swin-UNETR. These models were tested on an independent dataset of 30 GBM patients with the Dice metric used to evaluate segmentation accuracy. Finally, the best-performing segmentation model was integrated into our longitudinal tracking web application to assign automated structured reporting scores using change in percent cutoffs of lesion volume. The 3D Unet was our best-performing model with mean Dice scores of 0.72 for GTV1 and 0.73 for GTV2 with a standard deviation of 0.17 for both in the test dataset. We have successfully developed a lightweight post-surgical segmentation model for RT planning and longitudinal tracking.