Oncological treatments are changing rapidly due to the advent of several targeted anticancer drugs and regimens. The primary new area of research in oncological medicine is the implementation of a ...combination of novel therapies and standard care. In this scenario, radioimmunotherapy is one of the most promising fields, as proven by the exponential growth of publications in this context during the last decade.
This review provides an overview of the synergistic use of radiotherapy and immunotherapy and addresses questions like the importance of this subject, aspects clinicians look for in patients to administer this combined therapy, individuals who would benefit the most from this treatment, how to achieve abscopal effect and when does radio-immunotherapy become standard clinical practice.
Answers to these queries generate further issues that need to be addressed and solved. The abscopal and bystander effects are not utopia, rather physiological phenomena that occur in our bodies. Nevertheless, substantial evidence regarding the combination of radioimmunotherapy is lacking. In conclusion, joining forces and finding answers to all these open questions is of paramount importance.
•AUC of dose–response models is sensitive to the observational dose-range of the fitted data•AUC value of NTCP models is driven by intrinsic characteristics of the model and by the clinical setting ...of the data used to develop them.•AUC is problematic when used to compare the discriminative performance of NTCP models.
Normal tissue complication probability (NTCP) models are probabilistic models that describe the risk of radio-induced toxicity in tissues or organs. In the field of radiotherapy, the area under the ROC curve (AUC) is widely used to estimate the performance in risk prediction of NTCP models.
In this work, we derived an analytical expression of the AUC for the logistic NTCP model in the case of both symmetrical and asymmetrical dose (to the normal tissue) windows around D50. Using numerical simulations, we studied the behavior of the AUC in general clinical settings, enforcing non-logistic NTCP models (Lyman-Kutcher-Burman and LogEUD) and including risk factors beyond the dose. We validated our findings using real-world radiotherapy data sets of prostate cancer patients.
Our analytical expression of the AUC made explicit the dependence on both the steepness of the logistic curve (β) and the dose window width (w), showing that an increase of w pushes AUC towards higher values. Increasing values of the AUC with increasing values of w were consistently observed across simulated data sets with diverse clinical settings from published studies and real clinical data sets.
Our results reveal that the AUC of NTCP models inherits intrinsic characteristics from the clinical setting of the data set on which the models are developed, and warn against the use of the AUC to compare the performance of models constructed upon data from trials in which substantially different dose ranges were administered or accounting for different risk factors beyond the dose.
•Optimal RT outcomes requires accurate robust predictive radiation oncology models.•Present challenges in model and data quality must be addressed to improve RO models.•Mechanistic understanding of ...radiobiological concepts can inform RO models.•Integrating novel computational and modeling methods will advance model development.•Interdisciplinary RO model development leverages the expertise of all stakeholders.
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.
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•We designed a national survey to quantify interest in scientific activities of MPEs.•34.7% of the respondents dedicated <10% of their working time to research activities.•Time spent ...on scientific activities was not enough for 67.5% of the respondents.•The lack of time was the most frequent (77%) barrier to scientific activities.
The “FutuRuS” working group of the Italian Association of Medical Physics and Health Physics (AIFM) designed a survey (SicAS) to get feedback from its members regarding their interests and their experience in taking part in scientific activities and events, with the objective of focusing future efforts of the AIFM towards increasing the scientific activity of the medical physics expert (MPE).
SicAS was sent out in March 2022 to all AIFM members by newsletter and official communication. SicAS was structured into three sections: personal information and institution of affiliation information, involvement in scientific activities, interest in and commitment to scientific activities. Responses were collected in a fully anonymised mode from the Google Forms platform and analysed with descriptive statistics.
Out of 1289 members (active at the end of 2021), 467 responded to the Survey (response rate of 36%). The Survey results highlighted that AIFM members ranked the involvement of the MPE in scientific activities as highly relevant to the profession. However, 34.7% indicated devoting less than 10% of their working time to scientific activities. 67.5% of the respondents were dissatisfied with the time spent on scientific activities. The primary barrier was the lack of time (77%), followed by a lack of mentoring (32%).
SicAS highlighted the need for AIFM initiatives to support members’ scientific activities. National societies should help develop and support networks between members, create links among universities, hospitals, research institutions and industries, and provide guidelines and learning platforms for enhancing the MPEs’ involvement in scientific activities.
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