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1.
Q J Nucl Med Mol Imaging ; 65(3): 190-201, 2021 Sep.
Artículo en Inglés | MEDLINE | ID: mdl-34105339

RESUMEN

It is well known nowadays that radioactivity can destroy the living cells it interacts with. It is therefore unsurprising that radioactive sources, such as iodine-125, were historically developed for treatment purposes within radiation oncology with the goal of damaging malignant cells. However, since then, new techniques have been invented that make creative use of the same radioactivity properties of these sources for medical applications. Here, we review two distinct kinds of therapeutic uses of radioactive sources with applications to prostate, cervical, and breast cancer: brachytherapy and radioactive seed localization. In brachytherapy (BT), the radioactive sources are used for internal radiation treatment. Current approaches make use of real-time image guidance, for instance by means of magnetic resonance imaging, ultrasound, computed tomography, and sometimes positron emission tomography, depending on clinical availability and cancer type. Such image-guided BT for prostate and cervical cancer presents a promising alternative and/or addition to external beam radiation treatments or surgical resections. Radioactive sources can also be used for radio-guided tumor localization during surgery, for which the example of iodine-125 seed use in breast cancer is given. Radioactive seed localization (RSL) is increasingly popular as an alternative tumor localization technique during breast cancer surgery. Advantages of applying RSL include added flexibility in the clinical scheduling logistics, an increase in tumor localization accuracy, and higher patient satisfaction; safety measures do however have to be employed. We exemply the implementation of RSL in a clinic through our experiences at the Netherlands Cancer Institute.


Asunto(s)
Braquiterapia , Neoplasias de la Mama , Radiactividad , Humanos , Masculino
2.
Brachytherapy ; 23(2): 188-198, 2024.
Artículo en Inglés | MEDLINE | ID: mdl-38296658

RESUMEN

PURPOSE: Without a clear definition of an optimal treatment plan, no optimization model can be perfect. Therefore, instead of automatically finding a single "optimal" plan, finding multiple, yet different near-optimal plans, can be an insightful approach to support radiation oncologists in finding the plan they are looking for. METHODS AND MATERIALS: BRIGHT is a flexible AI-based optimization method for brachytherapy treatment planning that has already been shown capable of finding high-quality plans that trade-off target volume coverage and healthy tissue sparing. We leverage the flexibility of BRIGHT to find plans with similar dose-volume criteria, yet different dose distributions. We further describe extensions that facilitate fast plan adaptation should planning aims need to be adjusted, and straightforwardly allow incorporating hospital-specific aims besides standard protocols. RESULTS: Results are obtained for prostate (n = 12) and cervix brachytherapy (n = 36). We demonstrate the possible differences in dose distribution for optimized plans with equal dose-volume criteria. We furthermore demonstrate that adding hospital-specific aims enables adhering to hospital-specific practice while still being able to automatically create cervix plans that more often satisfy the EMBRACE-II protocol than clinical practice. Finally, we illustrate the feasibility of fast plan adaptation. CONCLUSIONS: Methods such as BRIGHT enable new ways to construct high-quality treatment plans for brachytherapy while offering new insights by making explicit the options one has. In particular, it becomes possible to present to radiation oncologists a manageable set of alternative plans that, from an optimization perspective are equally good, yet differ in terms of coverage-sparing trade-offs and shape of the dose distribution.


Asunto(s)
Braquiterapia , Neoplasias de la Próstata , Masculino , Femenino , Humanos , Próstata , Neoplasias de la Próstata/radioterapia , Dosificación Radioterapéutica , Cuello del Útero , Braquiterapia/métodos , Planificación de la Radioterapia Asistida por Computador/métodos , Inteligencia Artificial
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