Innovative and Promising Strategies to Enhance Effectiveness of Immunotherapy for CNS Tumors: Where Are We?

Central Nervous System Metastasis in Neuroblastoma: From Three Decades Clinical Experience to New Considerations in the Immunotherapy Era

Central nervous system (CNS) metastatic spread in neuroblastoma (NB) is rare and occurs more often at relapse/progression. We report on CNS involvement in high risk (HR) NB over 25 years. For this retrospective study, we reviewed the CNS imaging of all the patients treated at Bambino Gesù Children Hospital from 1 July 1996 to 30 June 2022. A total of 128 patients with HR NB were diagnosed over 26 years. Out of 128 patients, CNS metastatic spread occurred in 6 patients: 3 patients presented a metastatic spread at diagnosis, while in 3 patients, CNS was involved at relapse. Overall, the rate of occurrence of CNS spread is 4.7% with the same distribution at diagnosis and at relapse, namely 2.3%. Interestingly, CNS spread at diagnosis was observed only before 2012, whereas CNS was observed at relapse only after 2012, in the immunotherapy era. CNS metastases presented similar imaging features at diagnosis and at relapse, with a peculiar hemorrhagic aspect and mainly hemispheric localization in patients with bone skull involvement at the time of diagnosis. The outcome is dismal, and 3 out of 6 patients died for progressive disease.

Opportunities for improving brain cancer treatment outcomes through imaging-based mathematical modeling of the delivery of radiotherapy and immunotherapy

Immunotherapy has become a fourth pillar in the treatment of brain tumors and, when combined with radiation therapy, may improve patient outcomes and reduce the neurotoxicity. As with other combination therapies, the identification of a treatment schedule that maximizes the synergistic effect of radiation- and immune-therapy is a fundamental challenge. Mechanism-based mathematical modeling is one promising approach to systematically investigate therapeutic combinations to maximize positive outcomes within a rigorous framework. However, successful clinical translation of model-generated combinations of treatment requires patient-specific data to allow the models to be meaningfully initialized and parameterized. Quantitative imaging techniques have emerged as a promising source of high quality, spatially and temporally resolved data for the development and validation of mathematical models. In this review, we will present approaches to personalize mechanism-based modeling frameworks with patient data, and then discuss how these techniques could be leveraged to improve brain cancer outcomes through patient-specific modeling and optimization of treatment strategies.