AI and Cancer Research ── Brain ── 2026-10-12
Brain tumours: treatment, trials and AI
- Abstract
BACKGROUND: Molecular features are fundamental to both the diagnosis and prognosis of gliomas. Specific molecular features are generally unavailable at the time of index surgery; however, upfront knowledge of molecular characteristics could meaningfully inform surgical strategy. AIMS/OBJECTIVES: To determine the feasibility of a machine learning model trained on fluorescence spectral features to classify IDH mutation and MGMT promoter methylation status in glioma tissue. METHODS: Tissue samples were collected during 5-ALA-guided surgery and interrogated with a fibre probe to record fluorescence spectra. The fluorescence data were blinded and randomised and were trimmed to include only relevant wavelengths. Molecular data (IDH status and MGMT methylation) were recorded for each tumour specimen and incorporated into a machine learning algorithm. The algorithm was trained on the fluorescence data of half the patients exhibiting each label, and the model was then tested on the other half. RESULTS: Twenty-seven patients were recruited to the study, in whom over 8000 spectra were measured. IDH-mutant samples were identified with 87% accuracy. The algorithm was unable to reliably classify MGMT methylation. CONCLUSIONS: In this proof-of-concept study, machine learning applied to fluorescence spectra shows promise for real-time intraoperative prediction of IDH status. Further work integrating IDH and 1p/19q status in larger cohorts is warranted.
Journal IF-equivalent: 0.9 (OpenAlex 2-year mean citedness, value as of 2026-10-11, retrieved 2026-10-12; not the official Clarivate IF)Reference: Dablouk MO, Buckley K, Faul S, O’Sullivan MGJ. Intraoperative fluorescence spectroscopy for IDH classification in glioma: a feasibility study. British Journal of Neurosurgery. 2026 Oct 9 [Epub ahead of print]. doi:10.1080/02688697.2026.2746372.Checked: Abstract only - Abstract
BACKGROUND AND PURPOSE: Segmentation of the cerebral ventricular system (VS) is central to radiotherapy, both for whole-ventricular irradiation in intracranial germ cell tumors (GCTs) and protection of the periventricular region. Complex, variable VS anatomy makes manual delineation challenging and hampers auto-segmentation model development. We reported the evaluation and workflow integration of a deep-learning (DL) model for VS and brainstem segmentation on pediatric magnetic resonance imaging (MRI). MATERIALS AND METHODS: An nnU-Net was trained on MRIs from 76 unique brain tumor cases ( n = 73/71 for T1/T2). The evaluation used two independent retrospective cohorts: (i) GCTs evaluated on planning computed tomography (CT) images after MRI-to-CT registration mapping ( n = 13/14), and (ii) brain tumors evaluated on MRI ( n = 14). Performance was assessed using the Dice similarity coefficient (DSC) and Hausdorff distances (HD) against manual contours. Prospective deployment in routine clinical planning assessed usability and workflow integration. RESULTS: In the GCT-CT cohort, brainstem segmentations achieved median DSCs of >0.85 and HDs 95 of ≤5.0/6.4 mm, while VS segmentations achieved DSCs of >0.70 and HDs 95 of ≤5.3/4.3 mm, after rigid/deformable mapping to planning CT. In the MR cohort, performance on T1-/T2-weighted MRI reached DSCs of ≥0.90 for both structures, and HDs 95 of ≤3.0/3.4 mm and ≤ 1.3/2.2 mm for the brainstem and VS, respectively. The DL-segmentation workflow reduced workflow time by >50% compared with manual contouring. CONCLUSIONS: Lower performance in the GCT-CT cohort likely reflects noisier CT-based references, interobserver variability, and uncorrected geometric/anatomical mismatch. The model's accuracy supports radiotherapy planning in pediatric brain tumors through risk-structure delineation and target-volume segmentation in GCTs.
Journal IF-equivalent: 1.9 (OpenAlex 2-year mean citedness, value as of 2026-10-11, retrieved 2026-10-12; not the official Clarivate IF)Reference: Heinzelmann F, Hörst F, Heine L, Fragemann J, Rempe M, Luijten G, et al. Deep learning segmentation of the cerebral ventricular system and brainstem for pediatric radiotherapy planning. Phys Imaging Radiat Oncol. 2026 Sep;41:101086. doi:10.1016/j.phro.2026.101086; PMCID: PMC13651686.Checked: Full text checked