Reaching the target dose with one single 131 I-mIBG administration in high-risk neuroblastoma: The determinant impact of the primary tumour.

BACKGROUND: 131 I-metaiodobenzylguanidine (131 I-mIBG) effectiveness in children with metastasised neuroblastoma (NB) is linked to the effective dose absorbed by the target; a target of 4 Gy whole-body dose threshold has been proposed. Achieving this dose often requires administering 131 I-mIBG twice back-to-back, which may cause haematological toxicity. In this study, we tried identifying the factors predicting the achievement of 4 Gy whole-body dose with a single radiopharmaceutical administration. MATERIALS AND METHODS: Children affected by metastatic NB and treated with a high 131 I-mIBG activity (>450 MBq (megabecquerel)/kg) were evaluated retrospectively. Kinetics measurements were carried out at multiple time points to estimate the whole-body dose, which was compared with clinical and activity-related parameters. RESULTS: Seventeen children (12 females, median age 3 years, age range: 1.5-6.9 years) were included. Eleven of them still bore the primary tumour. The median whole-body dose was 2.88 Gy (range: 1.63-4.22 Gy). Children with a 'bulky' primary (>30 mL) received a higher whole-body dose than those with smaller or surgically removed primaries (3.42 ± 0.74 vs. 2.48 ± 0.65 Gy, respectively, p = .016). Conversely, the correlation between activity/kg and the whole-body dose was moderate (R: 0.42, p = .093). In the multivariate analysis, the volume of the primary tumour was the most relevant predictor of the whole-body dose (p = .002). CONCLUSIONS: These data suggest that the presence of a bulky primary tumour can significantly prolong the 131 I-mIBG biological half-life, effectively increasing the absorbed whole-body dose. This information could be used to model the administered activity, allowing to attain the target dose without needing a two-step radiopharmaceutical administration.

Diagnostic and Dosimetry Features of [64Cu]CuCl2 in High-Grade Paediatric Infiltrative Gliomas.

PURPOSE OF THE REPORT: Paediatric diffuse high-grade gliomas (PDHGG) are rare central nervous system neoplasms lacking effective therapeutic options. Molecular imaging of tumour metabolism might identify novel diagnostic/therapeutic targets. In this study, we evaluated the distribution and the dosimetry aspects of [64Cu]CuCl2 in PDHGG subjects, as copper is a key element in cellular metabolism whose turnover may be increased in tumour cells. MATERIAL AND METHODS: Paediatric patients with PDHGG were prospectively recruited. [64Cu]CuCl2 PET/CT was performed 1 h after tracer injection; if the scan was positive, it was repeated 24 and 72 h later. Lesion standardised uptake value (SUV) and target-to-background ratio (TBR) were calculated. Tumour and organ dosimetry were computed using the MIRD algorithm. Each patient underwent an MRI scan, including FLAIR, T2-weighted and post-contrast T1-weighted imaging. RESULTS: Ten patients were enrolled (median age 9, range 6-16 years, 6 females). Diagnoses were diffuse midline gliomas (n = 8, 5 of which with H3K27 alterations) and diffuse hemispheric gliomas (n = 2). Six patients had visible tracer uptake (SUV: 1.0 ± 0.6 TBR: 5 ± 3.1). [64Cu]CuCl2 accumulation was always concordant with MRI contrast enhancement and was higher in the presence of radiological signs of necrosis. SUV and TBR progressively increased on the 24- and 72-h acquisitions (p < 0.05 and p < 0.01, respectively). The liver and the abdominal organs received the highest non-target dose. CONCLUSIONS: [64Cu]CuCl2 is a well-tolerated radiotracer with reasonably favourable dosimetric properties, showing selective uptake in tumour areas with visible contrast enhancement and necrosis, thus suggesting that blood-brain barrier damage is a pre-requisite for its distribution to the intracranial structures. Moreover, tracer uptake showed an accumulating trend over time. These characteristics could deserve further analysis, to determine whether this radiopharmaceutical might have a possible therapeutic role as well.

The role of the deep convolutional neural network as an aid to interpreting brain [18F]DOPA PET/CT in the diagnosis of Parkinson's disease.

OBJECTIVES: To test the performance of a 3D convolutional neural network (CNN) in analysing brain [18F]DOPA PET/CT in order to identify patients with nigro-striatal neurodegeneration. We evaluated the robustness of the 3D CNN by testing it against a manual regional analysis of the striata by using a striatal-to-occipital ratio (SOR). METHODS: We analyzed patients who had undergone [18F]DOPA PET/CT from 2016 to 2018. Two examiners interpreted PET/CT images as positive or negative. Only patients with at least 2 years of follow-up and an ascertained neurological diagnosis were included. A 3D CNN was developed to evaluate [18F]DOPA PET/CT and refine the diagnosis of movement disorder. This system required training and testing, which were carried out on 2/3 and 1/3 of patients, respectively. A regional analysis was also conducted by drawing region of interest on T1-weighted 3D MRI scans, on which the [18F]DOPA PET images were first co-registered. RESULTS: Ninety-eight patients were enrolled: 43 presented nigro-striatal degeneration and 55 negative cases used as controls. After training on 69 patients, the diagnostic performance of the 3D CNN was then calculated in 29 patients. Sensitivity, specificity, negative predictive value, positive predictive value and accuracy were 100%, 89%, 100%, 85% and 93%, respectively. When we compared the 3D CNN results with the SOR analysis, we found that the two patients falsely classified as positive by the 3D CNN procedure showed SOR values ≤ 5th percentile of the negative cases' distribution. CONCLUSIONS: 3D CNNs are able to interpret [18F]DOPA PET/CT properly, revealing patients affected by Parkinson's disease. KEY POINTS: • [18F]DOPA PET/CT is a sensitive diagnostic tool to identify patients with nigro-striatal neurodegeneration. • A semiquantitative evaluation of the images allows a more confident interpretation of the PET findings. • 3D convolutional neural network allows an accurate interpretation of 18F-DOPA PET/CT images, revealing patients affected by Parkinson's disease.