I-mIBG therapy in relapsed/refractory neuroblastoma: A weapon from the future past.

Neuroblastoma (NB) is the most common extracranial solid tumor in children, with variable outcomes ranging from spontaneous remission to high-risk cases often leading to relapse or refractory disease. Approximately 50 % of patients with NB have high-risk features, often experiencing relapse or refractory disease despite intensive treatments and the prognosis remains poor, with long-term event-free survival (EFS) rates below 10 %,Radioactive iodine-labeled meta-iodobenzylguanidine (¹³¹I-mIBG) therapy, leveraging NB cells' radiosensitivity and expression of the norepinephrine transporter (NET), has shown promise in treating relapsed or refractory NB. Since 1985, ¹³¹I-mIBG has been studied to determine the maximum tolerated dose and side effects, with recent trials exploring its use in front-line treatment.

Multimodal radionuclide shuntography for device patency: New procedural tips for anuncommon historical technique.

Cerebrospinal fluid (CSF) shunting is an established long-term treatment option for hydrocephalus, and is one of the most commonly performed neurosurgical procedures in western countries.Despite advances in CSF shunt design and management, its failure rates remain high and is most commonly due to obstruction and infection.Cerebrospinal fluidshunt failure diagnosis should be prompt and accurate in establishing timely if its revision is appropriate.

Fit of biokinetic data in molecular radiotherapy: a machine learning approach.

Background: In literature are reported different analytical methods (AM) to choose the proper fit model and to fit data of the time-activity curve (TAC). On the other hand, Machine Learning algorithms (ML) are increasingly used for both classification and regression tasks. The aim of this work was to investigate the possibility of employing ML both to classify the most appropriate fit model and to predict the area under the curve (τ).