The Role of Quantitative 18F-FDG PET/CT in the Differential Diagnosis of NBIA Subtypes: Atlas- Based Phenotyping

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Arça B., Işık E. G., Has Şimşek D., Özkan Z. G., Yapıcı Obuz Z., Şanlı Y.

Annual Congress of the European Association of Nuclear Medicine (EANM'26), Vienna, Austria, 17 - 21 October 2026, pp.460-461, (Summary Text)

Abstract

Aim/Introduction: Neurodegeneration with brain iron

accumulation (NBIA) comprises a heterogeneous group

of disorders with overlapping clinical and genetic features.

Although cranial MRI is indicative for diagnosis, subtypespecific

functional and metabolic phenotypes remain

insufficiently defined in the literature. This study aims to

identify general and subtype-specific metabolic signatures

using quantitative 18F-FDG PET/CT with atlas-based

segmentation and to evaluate their potential discriminatory

value in NBIA. Materials and Methods: Brain 18F-FDG PET/

CT images of 55 clinically/genetically confirmed NBIA patients

were analyzed via atlas-based automated segmentation

(MIM Neuro). Regional Z-scores were calculated for

anatomical regions including the basal ganglia, thalamus,

brainstem, and cerebellum, normalized to an age-matched

reference database. Regional metabolic differences between

subtypes were assessed using Kruskal-Wallis and Mann-

Whitney U tests. The correlation between clinical onset age

and metabolic involvement was analyzed via Spearman’s

rank correlation. Results: The cohort (mean age: 28±14

years) included MPAN (n=13), PKAN (n=11), PLAN (n=8),

Kufor-Rakeb (n=5), BPAN (n=3), neuroferritinopathy (n=1),

aceruloplasminemia (n=1), and unspecified NBIA (n=13).

Across the entire cohort, the caudate nucleus emerged as

the most consistently affected region (mean Z-score: -2.55,

p<0.001). In subgroup analyses, MPAN exhibited the most

severe striatal degeneration; caudate metabolism (Z: -5.99)

was significantly lower than in PKAN (p<0.001) and PLAN

(p=0.01). PKAN was characterized by predominant cerebellar preserved midbrain (Z: +1.13) and cerebellar metabolism

(Z: -0.31), enabling differentiation from PKAN. Kufor-Rakeb

showed deep cerebellar (Z: -2.71), mesencephalic (Z:

-2.19), and thalamic (Z: -1.89) hypometabolism. Paradoxical

hypermetabolism was observed in the globus pallidus (Z:

+2.58) and midbrain (Z: +2.33) in a small subgroup (BPAN),

whereas the neuroferritinopathy case showed isolated

deep cerebellar hypometabolism (Z: -4.66). No significant

hemispheric lateralization was observed (p>0.05). Age of

onset showed a moderate positive correlation with metabolic

activity in the globus pallidus (r=0.56) and cerebellum

(r=0.49), linking earlier clinical onset to more severe

metabolic damage. Conclusion: This study represents

one of the largest atlas-based FDG PET analyses in NBIA

to date and provides a functional-metabolic counterpart

to a spectrum primarily defined by structural imaging.

Our findings demonstrate that NBIA involves widespread

cerebello-striatal networks rather than being confined to the

basal ganglia. Quantitative 18F-FDG PET/CT reveals clinically

relevant metabolic signatures that may improve subtype

differentiation, particularly in diagnostically challenging

cases, and may serve as a non-invasive complementary

tool in clinical practice.