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Proton therapy is a radiation technique that yields less dose in normal tissues than photon therapy. In the Netherlands, proton therapy is reimbursed if the reduced dose to normal tissues is predicted to translate into a prespecified reduction in toxicity, based on nationally approved validated models. The aim of this paper is to present the development of a national indication protocol for proton therapy (NIPP) for model-based selection of breast cancer patients and to report on first clinical experiences. A national proton therapy working group for breast cancer (PWG-BC) screened the literature for prognostic models able to estimate the individual risk of specific radiation-induced side-effects. After critical appraisal and selection of suitable models, a NIPP for breast cancer was written and subjected to comments by all stakeholders. The approved NIPP was subsequently introduced to select breast cancer patients who would benefit most from proton therapy. The model of Darby et al. (N Engl J Med 2013; 368:987–82) was the only model fulfilling the criteria prespecified by the PWG-BC. The model estimates t he relative risk of an acute coronary event (ACE) based on the mean heart dose. The absolute lifetime risk of ACE <80 years was calculated by applying this model to the Dutch absolute incidence of ACE for female and male patients, between 40 and 70 years at breast cancer radiotherapy, with/without cardiovascular risk factors. The NIPP was approved for reimbursement in January 2019. Based on a threshold value of a 2% absolute lower risk on ACE for proton therapy compared with photons, 268 breast cancer patients have been treated in the Netherlands with proton therapy between February 2019 and January 2021. The NIPP includes a model that allows the estimation of the absolute risk on ACE <80 years based on mean heart dose. In the first 2 years, 268 breast cancer patients have been treated with proton therapy in The Netherlands. • Selection protocol was determined for proton therapy for breast cancer patients. • Prediction of lifetime risk of acute coronary events based on mean heart dose. • About 3–4% of breast cancer patients are expected to benefit from proton therapy. [ABSTRACT FROM AUTHOR]

Mathematical modeling constitutes an emerging area of oncological research aiming to predict spatial and temporal evolution of tumors, by describing many different phenomena which occur at different scales. Among these, modeling at the macroscopic scale has a great potential of application, when diagnostic imaging evaluation is used to identify the metabolic tumor volume undergoing proliferation. With breast carcinoma one of the most common cancer occurrences, the personal involvement for the patient and the cost for the national health system vary considerably with the adopted treatment. When a neoadjuvant (volume reducing) drug therapy is a direct option, the choice of drug may dictate the physical burden and the surgical strategy for subsequent lumpectomy/mastectomy. This paper employs a mass transfer modeling approach in order to gain deeper insight into breast carcinoma proliferation and therapy at the tissue scale. The aim is to develop a predictive, quantitative method for each given patient. Model flexibility is demonstrated, to facilitate model replication for large patient cohorts. The proposed procedure may serve as a basis for a decision support system for surgeons and towards personalized treatment optimization of breast tumors. [ABSTRACT FROM AUTHOR]