Neuroradiology
October 14, 2026 Β· via Europe PMC
Paediatric thalamic tumours: a predictive MR phenotype.
Gaur P, Sepulveda F, De Vita E, +7 more, Mankad K
AI summary β for doctors, generated from the abstract only
Qualitative MR features and ADC histogram measures, especially the 10th percentile ADC, may help distinguish diffuse midline glioma from pilocytic astrocytoma in children with thalamic tumors.
What was studied
Retrospective single-center review (2000 to 2024) of 34 children with thalamic tumors, evaluating margins, enhancement, cysts and haemorrhage plus ADC histograms from manually drawn tumor masks, against histopathology and molecular subtype.
Key findings
- Diffuse midline gliomas made up 24% and pilocytic astrocytomas 28% of the cohort; the remainder was a varied mix of other tumor types
- ADC p10 was significantly lower in DMG than PA (877 Β± 152 vs 1053 Β± 152 x10^-6 mm2/s; p = 0.042), while ADC mean was not significantly different (p = 0.068)
- PAs were well defined and microcystic with brush-like enhancement; DMGs were ill-defined with patchy enhancement and frequent susceptibility artefact
Clinical relevance
Could inform pre-surgical planning, such as choosing between maximal safe resection and a biopsy-first approach.
Who should care
Limitations
Small cohort spanning many tumor types, so the DMG vs PA comparison involves very few patients; p = 0.042 is modest and no diagnostic accuracy or validation is reported in the abstract.
A suggestive MR phenotype for thalamic tumor type, but small numbers mean it needs confirmation.
This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates β verify against the original source before acting on it.
Abstract
PURPOSE
To utilise MR phenotyping in accurately predicting paediatric thalamic tumour type via qualitative MR analysis and quantitative diffusion histogram analysis, with the aim of optimising pre-surgical planning and patient outcomes.
METHODS
Patients with tumours epicentred in the thalamus were identified through an electronic patient record search (2000 to 2024). Radiological features evaluated included tumour margins, homogeneity, cystic components, T1 and T2 signal, contrast enhancement, and haemorrhage. Histopathological tumour type, WHO grade, and molecular subtype were recorded. Tumour masks incorporating both solid and cystic components were manually drawn on T2-weighted b0 images and ADC histograms were constructed and analysed.
RESULTS
34 patients (12 male, 22 female) were included, with a mean age of 10.4 years (range 2.4 months to 16 years). Diffuse midline gliomas (DMG) accounted for 24% and pilocytic astrocytomas (PA) for 28% of the cohort. Further tumour types included low-grade astrocytomas (9%), DNETs, PXAs, and embryonal tumours (8.8% each), with germinoma, glioblastoma, supratentorial ependymoma, ganglioglioma, lymphoma, and ATRT each comprising 3%. ADC p10 was significantly lower in DMG than PA (877βΒ±β152 vs. 1053βΒ±β152βΓβ10β»βΆ mmΒ²/s; tβ=β2.25, dfβ=β13, pβ=β0.042, Cohen's dβ=β1.16). ADC mean showed a large but non-significant difference (pβ=β0.068). PAs were well-defined and microcystic with brush-like enhancement. DMGs were ill-defined with patchy enhancement, lower ADC values, and frequent susceptibility artefact.
CONCLUSION
Distinctive qualitative and quantitative MR features, particularly ADC p10, can guide the neuroradiologist in predicting tumour type and informing the decision between safe maximal resection and biopsy-first surgical pathways.
DOI
10.1007/s00234-026-04222-2
PMID
42873296
Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.
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