The main goal of the field is to develop personalized approaches of treatment with use of improved tissue and tumor models, in oncology (personalized radiotherapy, personalized therapy of advanced cancers) and neurology (personalized management of neurocognitive brain disorders).
Immune-checkpoint inhibitors (ICI) are effective in the treatment of patients with advanced cancers, such as melanoma; however, patient response rates remain low, and a high prevalence of high-grade immune-related adverse events (irAE) have been reported. Typically, the first response assessment by molecular imaging is made 3-4 months after ICI start; however, small studies have shown that earlier assessment may benefit these patients. This opens an interesting scientific question, whether molecular imaging can provide an earlier, actionable assessment of disease response and irAE development and consequently accelerate clinician decision making to change or maintain treatment.
Despite significant improvements in survival in patients with metastatic cancer using current systemic therapies, most patients eventually develop disease progression. The development of treatment resistance is the main reason for disease progression. What is under-appreciated is that many patients experiencing progression may have the majority of individual lesions that continue to respond to therapy. Understanding the spatial-temporal dynamics of metastatic burden is critically important.
Theranostics is a promising approach to the management of metastatic diseases that combines paired molecular targeted diagnostic and therapeutic radionuclides for both imaging and therapy, also clinically adopted in Slovenia. The main clinical program is focused on gastroenteropancreatic neuroendocrine tumors (GEP-NETs) with the theranostics pair of 177Lu-DOTATATE (treatment) and 68Ga-DOTATATE (imaging). While very successful in some patients, the effectiveness of a theranostics treatment can be hindered by inter-lesion response heterogeneity or mixed response among metastatic lesions within a single patient. The development of predictive biomarkers for the successful management of patients is of critical importance.
The high precision of proton therapy (PT) comes as a double-edged sword: highly conformal dose distributions have to be delivered robustly to address the high sensitivity of PT to uncertainties. While the number of clinical PT centers and PT patients has significantly increased over the last decades, the influence of uncertainties has to be further minimized to exploit the full benefit of PT. Adapting PT plans in real-time has the potential to provide truly personalized treatments allowing for better target control and less toxicity. Currently, (i) time-consuming, largely manual stepwise treatment workflows, (ii) inflexibility of commercial PT equipment, and (iii) high diversity in the PT landscape prohibit moving towards a wide clinical implementation of adaptive PT approaches.
