Artificial intelligence-based volumetric measurements for longitudinal clinical assessment of treatment response in high-grade gliomas: Validation across institutional and public datasets

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 1648 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 <.0001). AI-informed disease state assessments showed an overall moderate agreement with MDTB diagnoses for progressive disease (k = 0.45, P <.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 <.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.