What question did this study set out to answer?

The study aims to evaluate an AI-based tool for measuring tumor volumes and its effectiveness in treatment assessments for high-grade gliomas.

February 20, 2026Open Access

AI-Based Volumetric Measurements for Longitudinal Clinical Assessment of Treatment Response in High-Grade Gliomas: Validation Across Institutional and Public Datasets

Key Points

The study aims to evaluate an AI-based tool for measuring tumor volumes and its effectiveness in treatment assessments for high-grade gliomas.
Applied the FDA-cleared AI tool Neosoma HGG to quantify tumor volumes in 263 subjects.
Compared AI-derived volumes to manual expert assessments and other AI measurements.
Used RANO criteria to evaluate therapeutic responses against multidisciplinary tumor board diagnoses.
Analyzed segmentation times using mixed-model regression.
Consistent contrast-enhancing tumor volumes across AI platforms.
Neosoma HGG significantly reduced segmentation time (pre-operative: 210.5s, post-operative: 179s, vs 15s for AI).
Moderate agreement with MDTB diagnoses for progressive disease (k = 0.45, p < 0.00001).
Significant variability in T2-FLAIR-derived volumes among AI platforms due to over-segmentation.

Abstract

Abstract Background High-grade gliomas (HGGs) require ongoing imaging to guide treatment, traditionally relying on labor-intensive and variable manual MRI measurements. While FDA-cleared artificial intelligence (AI) tools offer automated tumor volume segmentation, their clinical utility in decision-making remains understudied. This study assesses the utility and limitations of an FDA-cleared AI-based tool across public and institutional datasets, comparing its output with multidisciplinary tumor board (MDTB) assessments. Methods We applied the FDA-cleared, AI-based tool Neosoma HGG to quantify tumor volumes in 214 subjects from public datasets and 49 from an institutional cohort. AI-derived volumes were compared to expert manual and other AI-based measurements. Therapeutic response assessments using RANO criteria were evaluated against MDTB diagnoses. Segmentation times were analyzed using mixed-model regression. Results We analyzed 1,648 MRI sequences of 95 HGG patients across three datasets. Contrast-enhancing (CE) tumor volumes were consistent across AI platforms, and Neosoma HGG significantly reduced segmentation time (pre-operative:210.5s, post-operative: 179s vs 15 s, p 0.0001). AI-informed disease state assessments showed an overall moderate agreement with MDTB diagnoses for progressive disease (k = 0.45, p 0.00001). Key discrepancies arose from limitation of Neosoma HGG in distinguishing pseudo-progression from tumor progression. T2-FLAIR-derived volumes varied significantly between AI platforms (p 0.001), with discordances largely due to over-segmentation beyond the tumor region. Conclusion AI-based volumetric segmentation has the potential to improve efficiency and standardization in monitoring HGG, especially for CE tumor burden. However, moderate concordance with MDTB assessments and difficulties with FLAIR imaging underscore its current limitations. AI should serve as a clinical decision support tool, with further refinement needed to improve specificity and integrate multimodal imaging data.

Read Full Paperexternally

Perguntar à IA

Bookmark

View Full Paper