Cancers
Automated RANO 2.0-Aligned Response Assessment for Glioblastoma Clinical Trials: External Evaluation Against Expert Annotations
By Siakallis L and Bisdas S.
Abstract
Background/objectives
Reliable and reproducible imaging-response assessment is essential for endpoint evaluation in glioblastoma clinical trials. The Response Assessment in Neuro-Oncology (RANO) criteria provide a rule-based framework for standardisation, but their implementation can still yield variable outputs between readers and across timepoints and trial sites. This partly reflects differences in lesion definition, measurement geometry, longitudinal tracking, and new-lesion adjudication. We externally evaluated an automated pipeline for auditable, RANO 2.0-aligned imaging-only response assessment using pre-existing segmentation masks.
Methods
This retrospective feasibility and pilot evaluation used longitudinal glioblastoma magnetic resonance imaging (MRI) studies from the public single-centre LUMIERE dataset. Dataset-provided tumour segmentation masks were retained unchanged. An expert neuroradiologist visually reviewed the masks against the corresponding MRI images, and timepoints with segmentations clearly non-representative of tumour tissue were excluded. Deterministic rule-based processing performed lesion definition, admissibility-filtered measurement extraction, longitudinal tracking, new-lesion detection, and provisional response classification. Expert RANO imaging annotations and bidimensional measurements served as comparators rather than complete clinical RANO 2.0 adjudications. Analyses assessed agreement in the sum of products of diameters (SPD), longitudinal burden change, response classification, progression timing, new-lesion agreement, and failure modes, with patient-level clustered bootstrap resampling.