The delivery of radiotherapy (RT) involves the use of rather expensive resources and multi-disciplinary staff. As the number of cancer patients receiving RT increases, timely delivery becomes increasingly difficult due to the complexities related to, among others, variable patient inflow, complex patient routing, and the joint planning of multiple resources. Operations research (OR) methods have been successfully applied to solve many logistics problems through the development of advanced analytical models for improved decision making. This paper presents the state of the art in the application of OR methods for logistics optimization in RT, at various managerial levels. A literature search was performed in six databases covering several disciplines, from the medical to the technical field. Papers included in the review were published in peer-reviewed journals from 2000 to 2015. Data extraction includes the subject of research, the OR methods used in the study, the extent of implementation according to a six-stage model and the (potential) impact of the results in practice. From the 33 papers included in the review, 18 addressed problems related to patient scheduling (of which 12 focus on scheduling patients on linear accelerators), 8 focus on strategic decision making, 5 on resource capacity planning, and 2 on patient prioritization. Although calculating promising results, none of the papers reported a full implementation of the model with at least a thorough pre-post performance evaluation, indicating that, apart from possible reporting bias, implementation rates of OR models in RT are probably low. The literature on OR applications in RT covers a wide range of approaches from strategic capacity management to operational scheduling levels, and shows that considerable benefits in terms of both waiting times and resource utilization are likely to be achieved. Various fields can be further developed, for instance optimizing the coordination between the available capacity of different imaging devices or developing scheduling models that consider the RT chain of operations as a whole rather than the treatment machines alone.

During a course of radiotherapy for head-and-neck-cancer (HNC), non-rigid anatomical changes can be observed on daily Cone Beam CT (CBCT). To objectify responses to these changes, we use a decision support system (traffic light protocol). Action levels orange and red may lead to re-planning. The purpose of this study was to evaluate how often re-planning was done for non-rigid anatomical changes, which anatomical changes led to re-planning and in which subgroups of patients treatment adaptation was deemed necessary. A consecutive series of 388 HNC patients were retrospectively selected using the digital log of CBCT scans. The logs were analyzed for the number of new plans on an original planning CT scan (O-pCT) or a new pCT scan (N-pCT). Reasons for re-planning were categorized into: target volume increase/decrease, body contour decrease/increase and local shift of target volume. Subgroup analysis was performed to investigate relative differences of re-planning between treatment modalities. For 33 patients the treatment plan was adapted due to anatomical changes, resulting in 37 new plans in total. Re-planning on a N-pCT with complete re-delineation was done 22 times. In fifteen cases a new plan was created after adjustment of contours on the O-pCT. Main reasons for re-planning were target volume increase, body contour decrease and local shifts of target volume. Most re-planning (23%) was seen in patients treated with chemoradiotherapy. Visual detection of anatomical changes on CBCT during treatment of HNC, results in re-planning in 1 out of 10 patients.

External-beam radiotherapy treatments are delivered by a linear accelerator (linac) in a series of high-energy radiation sessions over multiple days. With the increase in the incidence of cancer and the use of radiotherapy (RT), the problem of automatically scheduling RT sessions while satisfying patient preferences regarding the time of their appointments becomes increasingly relevant. While most literature focuses on timeliness of treatments, several Dutch RT centers have expressed their need to include patient preferences when scheduling appointments for irradiation sessions. In this study, we propose a mixed-integer linear programming (MILP) model that solves the problem of scheduling and sequencing RT sessions considering time window preferences given by patients. The MILP model alone is able to solve the problem to optimality, scheduling all sessions within the desired window, in reasonable time for small size instances up to 66 patients and 2 linacs per week. For larger centers, we propose a heuristic method that pre-assigns patients to linacs to decompose the problem in subproblems (clusters of linacs) before using the MILP model to solve the subproblems to optimality in a sequential manner. We test our methodology using real-world data from a large Dutch RT center (8 linacs). Results show that, combining the heuristic with the MILP model, the problem can be solved in reasonable computation time with as few as 2.8% of the sessions being scheduled outside the desired time window.

Background: In contemporary positron emission tomography (PET)/computed tomography (CT) scanners, PET attenuation correction is performed by means of a CT-based attenuation map. Respiratory motion can however induce offsets between the PET and CT data. Studies have demonstrated that these offsets can cause errors in quantitative PET measures. The purpose of this study is to quantify the effects of respiration-induced CT differences on the attenuation correction of pulmonary 18-fluordeoxyglucose (FDG) 3D PET/CT in a patient population and to investigate contributing factors. Methods: For 32 lung cancer patients, 3D-CT, 4D-PET and 4D-CT data were acquired. The 4D FDG PET data were attenuation corrected (AC) using a free-breathing 3D-CT (3D-AC), the end-inspiration CT (EI-AC), the end-expiration CT (EE-AC) or phase-by-phase (P-AC). After reconstruction and AC, the 4D-PET data were averaged. In the 4D_avg data, we measured maximum tumour standardised uptake value (SUV)_max in the tumour, SUV_mean in a lung volume of interest (VOI) and average SUV (SUV_mean) in a muscle VOI. On the 4D-CT, we measured the lung volume differences and CT number changes between inhale and exhale in the lung VOI. Results: Compared to P-AC, we found −2.3% (range −9.7% to 1.2%) lower tumour SUV_max in EI-AC and 2.0% (range −0.9% to 9.5%) higher SUV_max in EE-AC. No differences in the muscle SUV were found. The use of 3D-AC led to respiration-induced SUV_max differences up to 20% compared to the use of P-AC. SUV_mean differences in the lung VOI between EI-AC and EE-AC correlated to average CT differences in this region (ρ = 0.83). SUVmax differences in the tumour correlated to the volume changes of the lungs (ρ = −0.55) and the motion amplitude of the tumour (ρ = 0.53), both as measured on the 4D-CT. Conclusions: Respiration-induced CT variations in clinical data can in extreme cases lead to SUV effects larger than 10% on PET attenuation correction. These differences were case specific and correlated to differences in CT number in the lungs.