How Reliable Are Volumetric Techniques for High-Grade Gliomas? A Comparison Study of Different Available Tools

Zeppa, Pietro; Neitzert, Luca; Mammi, Marco; Monticelli, Matteo; Altieri, Roberto; Castaldo, Margherita; Cofano, Fabio; Borrè, A; Zenga, Francesco; Melcarne, Antonio; Garbossa, Diego

doi:10.1093/neuros/nyaa282

Cited by 23 publications

(22 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This possibility may help address concerns about overestimation of enhancing tumor volume when using BraTumIA 1.2, previously noted in preoperative patients, 15 which was confirmed in a more recent study. 16 Reliability measured by ICC, concordance measured by CCC, and overlap measured by Dice coefficients were similar between the 2 software versions.…”

Section: Discussionmentioning

confidence: 75%

Repeatability of Automated Image Segmentation with BraTumIA in Patients with Recurrent Glioblastoma

Khalaf

Desjardins

Vredenburgh

et al. 2021

AJNR Am J Neuroradiol

View full text Add to dashboard Cite

BACKGROUND AND PURPOSE: Despite high interest in machine-learning algorithms for automated segmentation of MRIs of patients with brain tumors, there are few reports on the variability of segmentation results. The purpose of this study was to obtain benchmark measures of repeatability for a widely accessible software program, BraTumIA (Versions 1.2 and 2.0), which uses a machine-learning algorithm to segment tumor features on contrast-enhanced brain MR imaging. MATERIALS AND METHODS:Automatic segmentation of enhancing tumor, tumor edema, nonenhancing tumor, and necrosis was performed on repeat MR imaging scans obtained approximately 2 days apart in 20 patients with recurrent glioblastoma. Measures of repeatability and spatial overlap, including repeatability and Dice coefficients, are reported.RESULTS: Larger volumes of enhancing tumor were obtained on later compared with earlier scans (mean, 26.3 versus 24.2 mL for BraTumIA 1.2; P , .05; and 24.9 versus 22.9 mL for BraTumIA 2.0, P , .01). In terms of percentage change, repeatability coefficients ranged from 31% to 46% for enhancing tumor and edema components and from 87% to 116% for nonenhancing tumor and necrosis. Dice coefficients were highest (.0.7) for enhancing tumor and edema components, intermediate for necrosis, and lowest for nonenhancing tumor and did not differ between software versions. Enhancing tumor and tumor edema were smaller, and necrotic tumor larger using BraTumIA 2.0 rather than 1.2. CONCLUSIONS:Repeatability and overlap metrics varied by segmentation type, with better performance for segmentations of enhancing tumor and tumor edema compared with other components. Incomplete washout of gadolinium contrast agents could account for increasing enhancing tumor volumes on later scans.

show abstract

Section: Discussionmentioning

confidence: 75%

Repeatability of Automated Image Segmentation with BraTumIA in Patients with Recurrent Glioblastoma

Khalaf

Desjardins

Vredenburgh

et al. 2021

AJNR Am J Neuroradiol

View full text Add to dashboard Cite

show abstract

“…The Brain Tumor Image Analysis tool (BraTumIA) has been developed to segment three brain tumor compartments using four scan sequences [33,75] and has been shown to have good agreement with manual tumor volumes on preoperative scans. The Pearson's correlation coefficient between manual and BraTumIA tumor volumes was 0.8 based on 19 patients [75] and 0.88 based on 58 patients [76], albeit with a systematic overestimation. In addition, the BraTS challenge has been held yearly since 2012, which aims to improve disease diagnosis, treatment planning, monitoring, and clinical trials by means of reliable tumor segmentation.…”

Section: Discussionmentioning

confidence: 87%

Glioblastoma Surgery Imaging—Reporting and Data System: Standardized Reporting of Tumor Volume, Location, and Resectability Based on Automated Segmentations

Kommers

Bouget

Pedersen

et al. 2021

Cancers

View full text Add to dashboard Cite

Treatment decisions for patients with presumed glioblastoma are based on tumor characteristics available from a preoperative MR scan. Tumor characteristics, including volume, location, and resectability, are often estimated or manually delineated. This process is time consuming and subjective. Hence, comparison across cohorts, trials, or registries are subject to assessment bias. In this study, we propose a standardized Glioblastoma Surgery Imaging Reporting and Data System (GSI-RADS) based on an automated method of tumor segmentation that provides standard reports on tumor features that are potentially relevant for glioblastoma surgery. As clinical validation, we determine the agreement in extracted tumor features between the automated method and the current standard of manual segmentations from routine clinical MR scans before treatment. In an observational consecutive cohort of 1596 adult patients with a first time surgery of a glioblastoma from 13 institutions, we segmented gadolinium-enhanced tumor parts both by a human rater and by an automated algorithm. Tumor features were extracted from segmentations of both methods and compared to assess differences, concordance, and equivalence. The laterality, contralateral infiltration, and the laterality indices were in excellent agreement. The native and normalized tumor volumes had excellent agreement, consistency, and equivalence. Multifocality, but not the number of foci, had good agreement and equivalence. The location profiles of cortical and subcortical structures were in excellent agreement. The expected residual tumor volumes and resectability indices had excellent agreement, consistency, and equivalence. Tumor probability maps were in good agreement. In conclusion, automated segmentations are in excellent agreement with manual segmentations and practically equivalent regarding tumor features that are potentially relevant for neurosurgical purposes. Standard GSI-RADS reports can be generated by open access software.

show abstract

“…In a recent study, manual segmentation using Horos was compared against alternative techniques and software in the assessment of pre- and post-operative tumor volumes of high-grade gliomas in humans. It was found that the manual and semi-automated software showed high agreement in preoperative volumetric assessment and were adequate for post-operative volumetric assessment ( 26 ). From this conclusion, we were confident that the software tool could be used to measure the pre- and post-operative cystic volumes accurately.…”

Section: Discussionmentioning

confidence: 99%

Marsupialisation of 12 odontogenic cysts in Boxer dogs: Retrospective case series

Haseler¹,

Tundo²,

Southerden³

2023

Front. Vet. Sci.

View full text Add to dashboard Cite

Marsupialisation of odontogenic cysts is a minimally invasive treatment method used in human dentistry. Marsupialisation decompresses the cyst and promotes remodeling of alveolar bone and shrinkage of the cyst. In this retrospective study we look at the effectiveness of marsupialisation at reducing the size of odontogenic cysts in dogs. The case series consists of six Boxer dogs with 12 odontogenic cysts. Each case underwent a high resolution CT scan prior to treatment and at follow-up. Each CT scan was reviewed, the volume of each cyst calculated using manual segmentation and the reduction in cyst volume calculated. There was a marked reduction in cystic volume of 66.6% over a mean of 138 days. This shows that the use of marsupialisation effective method of reducing cyst volume.

show abstract

How Reliable Are Volumetric Techniques for High-Grade Gliomas? A Comparison Study of Different Available Tools

Abstract: Abstract BACKGROUND Gliomas are the most common malignant primary brain tumors. Assessment of the tumor volume represents a crucial point in preoperative and postoperative evaluation. OBJECTIVE To compare pre- and postoperative tumor volumes obtained with an autom… Show more

Cited by 23 publications

References 16 publications

Repeatability of Automated Image Segmentation with BraTumIA in Patients with Recurrent Glioblastoma

Repeatability of Automated Image Segmentation with BraTumIA in Patients with Recurrent Glioblastoma

Glioblastoma Surgery Imaging—Reporting and Data System: Standardized Reporting of Tumor Volume, Location, and Resectability Based on Automated Segmentations

Marsupialisation of 12 odontogenic cysts in Boxer dogs: Retrospective case series

Contact Info

Product

Resources

About