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Radiomics is an emerging translational field of medicine based on the extraction of highdimensional data from radiological images, with the purpose to reach reliable models to be applied into clinical practice for the purposes of diagnosis, prognosis and evaluation of disease response to treatment. We aim to provide the basic information on radiomics to radiologists and clinicians who are focused on breast cancer care, encouraging cooperation with scientists to mine data for a better application in clinical practice. We investigate the workflow and clinical application of radiomics in breast cancer care, as well as the outlook and challenges based on recent studies. Currently, radiomics has the potential ability to distinguish between benign and malignant breast lesions, to predict breast cancer's molecular subtypes, the response to neoadjuvant chemotherapy and the lymph node metastases. Even though radiomics has been used in tumor diagnosis and prognosis, it is still in the research phase and some challenges need to be faced to obtain a clinical translation. In this review, we discuss the current limitations and promises of radiomics for improvement in further research. [ABSTRACT FROM AUTHOR]

The need for radiotherapy personalization is now widely recognized, however, it would require considerations not only on the probability of control and survival of the tumor, but also on the possible toxic effects, on the quality of the expected life and the economic efficiency of the treatment. In this paper, we propose a simulation tool that can be integrated into a decision support system that allows selection of the most suitable irradiation regimen. We used a macroscale mathematical model, which includes active and necrotic tumor dynamics and the role of oxygenation to simulate the effects of different hypo-/hyper-fractional regimens using retrospective data of seven virtual patients from as many cervical cancer patients used for its training in a previous study. The results confirmed the heterogeneous response across the patients as a function of treatment regimen and suggested the tumor growth rate as a main factor in the final tumor regression. In addition to the maximum regression, another criterion was suggested to select the most suitable regimen (minimum number of fractions to achieve a regression of 80%) minimizing the toxicity and maximizing the cost-effectiveness ratio. Despite the lack of direct validation, the simulation results are in agreement with the literature findings that suggest the need for hypo-fractionated regimens in case of aggressive tumor phenotypes. Finally, the paper suggests a possible exploitation of the model within a tool to support clinical decisions. [ABSTRACT FROM AUTHOR]

Background and purpose 3D printing is rapidly evolving and further assessment of materials and technique is required for clinical applications. We evaluated 3D printed boluses with acrylonitrile butadiene styrene (ABS) and polylactide (PLA) at different infill percentage. Material and methods A low-cost 3D printer was used. The influence of the air inclusion within the 3D printed boluses was assessed thoroughly both with treatment planning system (TPS) and with physical measurements. For each bolus, two treatment plans were calculated with Monte Carlo algorithm, considering the computed tomography (CT) scan of the 3D printed bolus or modelling the 3D printed bolus as a virtual bolus structure with a homogeneous density. Depth dose measurements were performed with Gafchromic films. Results High infill percentage corresponds to high density and high homogeneity within bolus material. The approximation of the bolus in the TPS as a homogeneous material is satisfying for infill percentages greater than 20%. Measurements performed with PLA boluses are more comparable to the TPS calculated profiles. For boluses printed at 40% and 60% infill, the discrepancies between calculated and measured dose distribution are within 5%. Conclusions 3D printing technology allows modulating the shift of the build-up region by tuning the infill percentage of the 3D printed bolus in order to improve superficial target coverage. [ABSTRACT FROM AUTHOR]