A joint physics and radiobiology DREAM team vision - Towards better response prediction models to advance radiotherapy.

Radiotherapy developed empirically through experience balancing tumour control and normal tissue toxicities. Early simple mathematical models formalized this practical knowledge and enabled effective cancer treatment to date. Remarkable advances in technology, computing, and experimental biology now create opportunities to incorporate this knowledge into enhanced computational models. The ESTRO DREAM (Dose Response, Experiment, Analysis, Modelling) workshop brought together experts across disciplines to pursue the vision of personalized radiotherapy for optimal outcomes through advanced modelling. The ultimate vision is leveraging quantitative models dynamically during therapy to ultimately achieve truly adaptive and biologically guided radiotherapy at the population as well as individual patient-based levels. This requires the generation of models that inform response-based adaptations, individually optimized delivery and enable biological monitoring to provide decision support to clinicians. The goal is expanding to models that can drive the realization of personalized therapy for optimal outcomes. This position paper provides their propositions that describe how innovations in biology, physics, mathematics, and data science including AI could inform models and improve predictions. It consolidates the DREAM team's consensus on scientific priorities and organizational requirements. Scientifically, it stresses the need for rigorous, multifaceted model development, comprehensive validation and clinical applicability and significance. Organizationally, it reinforces the prerequisites of interdisciplinary research and collaboration between physicians, medical physicists, radiobiologists, and computational scientists throughout model development. Solely by a shared understanding of clinical needs, biological mechanisms, and computational methods, more informed models can be created. Future research environment and support must facilitate this integrative method of operation across multiple disciplines.

A global sensitivity analysis approach applied to a multiscale model of microvascular flow.

A global sensitivity analysis of a multiscale computational model of microvascular flow is presented. A total of 140 simulations have been completed and analyzed varying 6 input parameters and considering their effects on 7 output variables. Interestingly, the vascular network topology has been found as a determinant factor for both vasculature-related and interstitium-related quantities. Regarding the firsts, the vascular network topology has obtained a score of 5.5/6 and 6/6 for average and spatial distribution respectively (where 6 is the maximum and 1 is the minimum). On the other hand, considering interstitium-related quantities, the score is 4/6 and 5/6 for average and spatial distribution respectively. These results suggest that the network topology has a significant influence on the outcome of the computational analysis.

Lichen (Xanthoria parietina) biomonitoring of trace element contamination and air quality assessment in Pisa Province (Tuscany, Italy).

In the northern part of Pisa Province (Tuscany, Italy), the use of lichens as both airborne trace element biomonitors and air quality bioindicators is described. The following elements were analysed in Xanthoria parietina: As, Cd, Cr, Ni, Pb, V, Zn and Hg, and the results are compared with those we previously obtained in Livorno Province (Tuscany) using the same lichen species. The results identify spots of different environmental metal contamination and air quality. Median values of Pb, V and Ni concentrations were much lower than those of Livorno Province, with maximum values even nine times lower. Arsenic contamination was also lower, while Cd, Hg and Zn levels were similar in the two areas. In Pisa Province, the highest levels of contamination were recorded for Zn, Pb, Cr and Ni, and a degree of agreement was found between air quality and metal concentrations in lichens. The air quality in Pisa Province is better than in Livorno Province, even if the different climatic and orographic features of the two areas may influence the presence of lichen species and thus an assessment of air quality.