Therapeutic applications of radioactive sources: From image-guided brachytherapyto radio-guided surgical resection

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 medi- cal 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 tomog- raphy, 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 experi- ences at the netherlands cancer institute

Adaptive objective configuration in bi-objective evolutionary optimization for cervical cancer brachytherapy treatment planning

The Multi-Objective Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm (MO-RV-GOMEA) has been proven effective and eficient in solving real-world problems. A prime example is optimizing treatment plans for prostate cancer brachytherapy, an internal form of radiation treatment, for which equally important clinical aims from a base protocol are grouped into two objectives and bi-objectively optimized. This use of MO-RV-GOMEA was recently successfully introduced into clinical practice. Brachytherapy can also play an important role in treating cervical cancer. However, using the same approach to optimize treatment plans often does not immediately lead to clinically desirable results. Concordantly, medical experts indicate that they use additional aims beyond the cervix base protocol. Moreover, these aims have different priorities and can be patient-specifically adjusted. For this reason, we propose a novel adaptive objective configuration method to use with MO-RV-GOMEA so that we can accommodate additional aims of this nature. Based on results using only the base protocol, in consultation with medical experts, we configured key additional aims. We show how, for 10 patient cases, the new approach achieves the intended result, properly taking into account the additional aims. Consequently, plans resulting from the new approach are preferred by medical specialists in 8/10 cases

Obtaining smoothly navigable approximation sets in bi-objective multi-modal optimization

Even if a Multi-modal Multi-Objective Evolutionary Algorithm (MMOEA) is designed to find solutions well spread over all locally optimal approximation sets of a Multi-modal Multi-objective Optimization Problem (MMOP), there is a risk that the found set of solutions is not smoothly navigable because the solutions belong to various niches, reducing the insight for decision makers. To tackle this issue, a new MMOEAs is proposed: the Multi-Modal Bézier Evolutionary Algorithm (MM-BezEA), which produces approximation sets that cover individual niches and exhibit inherent decision-space smoothness as they are parameterized by Bézier curves. MM-BezEA combines the concepts behind the recently introduced BezEA and MO-HillVallEA to find all locally optimal approximation sets. When benchmarked against the MMOEAs MO_Ring_PSO_SCD and MO-HillVallEA on MMOPs with linear Pareto sets, MM-BezEA was found to perform best in terms of best hypervolume