Keeping your best options open with AI-based treatment planning in prostate and cervix brachytherapy.

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.

Parameterless Gene-Pool Optimal Mixing Evolutionary Algorithms.

When it comes to solving optimization problems with evolutionary algorithms (EAs) in a reliable and scalable manner, detecting and exploiting linkage information, that is, dependencies between variables, can be key. In this paper, we present the latest version of, and propose substantial enhancements to, the gene-pool optimal mixing evolutionary algorithm (GOMEA): an EA explicitly designed to estimate and exploit linkage information. We begin by performing a large-scale search over several GOMEA design choices to understand what matters most and obtain a generally best-performing version of the algorithm.

Automatic landmark correspondence detection in medical images with an application to deformable image registration.

Purpose: Deformable image registration (DIR) can benefit from additional guidance using corresponding landmarks in the images. However, the benefits thereof are largely understudied, especially due to the lack of automatic landmark detection methods for three-dimensional (3D) medical images.

Approach: We present a deep convolutional neural network (DCNN), called DCNN-Match, that learns to predict landmark correspondences in 3D images in a self-supervised manner.

Towards artificial intelligence-based automated treatment planning in clinical practice: A prospective study of the first clinical experiences in high-dose-rate prostate brachytherapy.

Purpose: This prospective study evaluates our first clinical experiences with the novel ``BRachytherapy via artificial Intelligent GOMEA-Heuristic based Treatment planning'' (BRIGHT) applied to high-dose-rate prostate brachytherapy.

Methods And Materials: Between March 2020 and October 2021, 14 prostate cancer patients were treated in our center with a 15Gy HDR-brachytherapy boost. BRIGHT was used for bi-objective treatment plan optimization and selection of the most desirable plans from a coverage-sparing trade-off curve.

Validation and Comparison of Radiograph-Based Organ Dose Reconstruction Approaches for Wilms Tumor Radiation Treatment Plans.

Purpose: Our purpose was to validate and compare the performance of 4 organ dose reconstruction approaches for historical radiation treatment planning based on 2-dimensional radiographs.

Methods And Materials: We considered 10 patients with Wilms tumor with planning computed tomography images for whom we developed typical historic Wilms tumor radiation treatment plans, using anteroposterior and posteroanterior parallel-opposed 6 MV flank fields, normalized to 14.4 Gy.

Interactive Education on Sleep Hygiene with a Social Robot at a Pediatric Oncology Outpatient Clinic: Feasibility, Experiences, and Preliminary Effectiveness.

Objectives: Children with cancer often experience sleep problems, which are associated with many negative physical and psychological health outcomes, as well as with a lower quality of life. Therefore, interventions are strongly required to improve sleep in this population. We evaluated interactive education with respect to sleep hygiene with a social robot at a pediatric oncology outpatient clinic regarding the feasibility, experiences, and preliminary effectiveness.

Robust optimization for HDR prostate brachytherapy applied to organ reconstruction uncertainty.

Purpose: Recently, we introduced a bi-objective optimization approach based on dose-volume indices to automatically create clinically good HDR prostate brachytherapy plans. To calculate dose-volume indices, a reconstruction algorithm is used to determine the 3D organ shape from 2D contours, inevitably containing settings that influence the result. We augment the optimization approach to quickly find plans that are robust to differences in 3D reconstruction.

Bi-objective optimization of catheter positions for high-dose-rate prostate brachytherapy.

Purpose: Bi-objective simultaneous optimization of catheter positions and dwell times for high-dose-rate (HDR) prostate brachytherapy, based directly on dose-volume indices, has shown promising results. However, optimization with the state-of-the-art evolutionary algorithm MO-RV-GOMEA so far required several hours of runtime, and resulting catheter positions were not always clinically feasible. The aim of this study is to extend the optimization model and apply GPU parallelization to achieve clinically acceptable computation times.

Machine learning for the prediction of pseudorealistic pediatric abdominal phantoms for radiation dose reconstruction.

Current phantoms used for the dose reconstruction of long-term childhood cancer survivors lack individualization. We design a method to predict highly individualized abdominal three-dimensional (3-D) phantoms automatically. We train machine learning (ML) models to map (2-D) patient features to 3-D organ-at-risk (OAR) metrics upon a database of 60 pediatric abdominal computed tomographies with liver and spleen segmentations.

Achieving Highly Scalable Evolutionary Real-Valued Optimization by Exploiting Partial Evaluations.

It is known that to achieve efficient scalability of an Evolutionary Algorithm (EA), dependencies (also known as linkage) must be properly taken into account during variation. In a Gray-Box Optimization (GBO) setting, exploiting prior knowledge regarding these dependencies can greatly benefit optimization. We specifically consider the setting where partial evaluations are possible, meaning that the partial modification of a solution can be efficiently evaluated.

Automatic generation of three-dimensional dose reconstruction data for two-dimensional radiotherapy plans for historically treated patients.

Performing large-scale three-dimensional radiation dose reconstruction for patients requires a large amount of manual work. We present an image processing-based pipeline to automatically reconstruct radiation dose. The pipeline was designed for childhood cancer survivors that received abdominal radiotherapy with anterior-to-posterior and posterior-to-anterior field set-up.

GPU-accelerated bi-objective treatment planning for prostate high-dose-rate brachytherapy.

Purpose: The purpose of this study is to improve upon a recently introduced bi-objective treatment planning method for prostate high-dose-rate (HDR) brachytherapy (BT), both in terms of resulting plan quality and runtime requirements, to the extent that its execution time is clinically acceptable.

Methods: Bi-objective treatment planning is done using a state-of-the-art multiobjective evolutionary algorithm, which produces a large number of potential treatment plans with different trade-offs between coverage of the target volumes and sparing organs at risk. A graphics processing unit (GPU) is used for large-scale parallelization of dose calculations and the calculation of the dose-volume (DV) indices of potential treatment plans.

How do patient characteristics and anatomical features correlate to accuracy of organ dose reconstruction for Wilms' tumor radiation treatment plans when using a surrogate patient's CT scan?

In retrospective radiation treatment (RT) dosimetry, a surrogate anatomy is often used for patients without 3D CT. To gain insight in what the crucial aspects in a surrogate anatomy are to enable accurate dose reconstruction, we investigated the relation of patient characteristics and internal anatomical features with deviations in reconstructed organ dose using surrogate patient's CT scans. Abdominal CT scans of 35 childhood cancer patients (age: 2.

Sensitivity of dose-volume indices to computation settings in high-dose-rate prostate brachytherapy treatment plan evaluation.

Purpose: To investigate the variation in computed dose-volume (DV) indices for high-dose-rate (HDR) prostate brachytherapy that can result from typical differences in computation settings in treatment planning systems (TPSs).

Methods: Five factors were taken into account: number of dose-calculation points, radioactive source description, interpolation between delineated contours, intersections between delineated organ contours, and organ shape at the top and bottom contour using either full or partial slice thickness. Using in-house developed software, the DV indices of the treatment plans of 26 patients were calculated with different settings, and compared to a baseline setting that closely followed the default settings of the TPS used in our medical center.

Simplex-based navigation tool for a posteriori selection of the preferred deformable image registration outcome from a set of trade-off solutions obtained with multiobjective optimization for the case of breast MRI.

Multiobjective optimization approaches for deformable image registration (DIR) remove the need for manual adjustment of key parameters and provide a set of solutions that represent high-quality trade-offs between objectives of interest. Choosing a desired outcome a posteriori is potentially far more insightful as differences between solutions can be immediately visualized. The purpose of this work is to investigate whether such an approach allows clinical experts to intuitively select their preferred DIR outcome.

Evaluation of bi-objective treatment planning for high-dose-rate prostate brachytherapy-A retrospective observer study.

Purpose: Bi-objective treatment planning for high-dose-rate prostate brachytherapy is a novel treatment planning method with two separate objectives that represent target coverage and organ-at-risk sparing. In this study, we investigated the feasibility and plan quality of this method by means of a retrospective observer study.

Methods And Materials: Current planning sessions were recorded to configure a bi-objective optimization model and to assess its applicability to our clinical practice.

Are age and gender suitable matching criteria in organ dose reconstruction using surrogate childhood cancer patients' CT scans?

Purpose: The purpose of this work was to assess the feasibility of using surrogate CT scans of matched patients for organ dose reconstructions for childhood cancer (CC) survivors, treated in the past with only 2D imaging data available instead of 3D CT data, and in particular using the current literature standard of matching patients based on similarity in age and gender.

Methods: Thirty-one recently treated CC patients with abdominal CT scans were divided into six age- and gender-matched groups. From each group, two radiotherapy plans for Wilms' tumor were selected as reference plans and applied to the age- and gender-matched patients' CTs in the respective group.

On the feasibility of automatically selecting similar patients in highly individualized radiotherapy dose reconstruction for historic data of pediatric cancer survivors.

Purpose: The aim of this study is to establish the first step toward a novel and highly individualized three-dimensional (3D) dose distribution reconstruction method, based on CT scans and organ delineations of recently treated patients. Specifically, the feasibility of automatically selecting the CT scan of a recently treated childhood cancer patient who is similar to a given historically treated child who suffered from Wilms' tumor is assessed.

Methods: A cohort of 37 recently treated children between 2- and 6-yr old are considered.

The feasibility of manual parameter tuning for deformable breast MR image registration from a multi-objective optimization perspective.

Deformable image registration is typically formulated as an optimization problem involving a linearly weighted combination of terms that correspond to objectives of interest (e.g. similarity, deformation magnitude).

Exploiting Linkage Information and Problem-Specific Knowledge in Evolutionary Distribution Network Expansion Planning.

This article tackles the Distribution Network Expansion Planning (DNEP) problem that has to be solved by distribution network operators to decide which, where, and/or when enhancements to electricity networks should be introduced to satisfy the future power demands. Because of many real-world details involved, the structure of the problem is not exploited easily using mathematical programming techniques, for which reason we consider solving this problem with evolutionary algorithms (EAs). We compare three types of EAs for optimizing expansion plans: the classic genetic algorithm (GA), the estimation-of-distribution algorithm (EDA), and the Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA).

GAMBIT: A Parameterless Model-Based Evolutionary Algorithm for Mixed-Integer Problems.

Learning and exploiting problem structure is one of the key challenges in optimization. This is especially important for black-box optimization (BBO) where prior structural knowledge of a problem is not available. Existing model-based Evolutionary Algorithms (EAs) are very efficient at learning structure in both the discrete, and in the continuous domain.

Benchmarking parameter-free AMaLGaM on functions with and without noise.

We describe a parameter-free estimation-of-distribution algorithm (EDA) called the adapted maximum-likelihood Gaussian model iterated density-estimation evolutionary algorithm (AMaLGaM-ID[Formula: see text]A, or AMaLGaM for short) for numerical optimization. AMaLGaM is benchmarked within the 2009 black box optimization benchmarking (BBOB) framework and compared to a variant with incremental model building (iAMaLGaM). We study the implications of factorizing the covariance matrix in the Gaussian distribution, to use only a few or no covariances.

Towards a real-time minimally-invasive vascular intervention simulation system.

Recently, foundations rooted in physics have been laid down for the goal of simulating the propagation of a guide wire inside the vasculature. At the heart of the simulation lies the fundamental task of energy minimization. The energy comes from interaction with the vessel wall and the bending of the guide wire.