Purpose To develop a knowledge‐based automated planning pipeline that generates treatment plans without feature engineering, using deep neural network architectures for predicting three‐dimensional (3D) dose. Methods Our knowledge‐based automated planning (KBAP) pipeline consisted of a knowledge‐based planning (KBP) method that predicts dose for a contoured computed tomography (CT) image followed by two optimization models that learn objective function weights and generate fluence‐based plans, respectively. We developed a novel generative adversarial network (GAN)‐based KBP approach, a 3D GAN model, which predicts dose for the full 3D CT image at once and accounts for correlations between adjacent CT slices. Baseline comparisons were made against two state‐of‐the‐art deep learning–based KBP methods from the literature. We also developed an additional benchmark, a two‐dimensional (2D) GAN model which predicts dose to each axial slice independently. For all models, we investigated the impact of multiplicatively scaling the predictions before optimization, such that the predicted dose distributions achieved all target clinical criteria. Each KBP model was trained on 130 previously delivered oropharyngeal treatment plans. Performance was tested on 87 out‐of‐sample previously delivered treatment plans. All KBAP plans were evaluated using clinical planning criteria and compared to their corresponding clinical plans. KBP prediction quality was assessed using dose‐volume histogram (DVH) differences from the corresponding clinical plans. Results The best performing KBAP plans were generated using predictions from the 3D GAN model that were multiplicatively scaled. These plans satisfied 77% of all clinical criteria, compared to the clinical plans, which satisfied 67% of all criteria. In general, multiplicatively scaling predictions prior to optimization increased the fraction of clinical criteria satisfaction by 11% relative to the plans generated with nonscaled predictions. Additionally, these KBAP plans satisfied the same criteria as the clinical plans 84% and 8% more frequently as compared to the two benchmark methods, respectively. Conclusions We developed the first knowledge‐based automated planning framework using a 3D generative adversarial network for prediction. Our results, based on 217 oropharyngeal cancer treatment plans, demonstrated superior performance in satisfying clinical criteria and generated more realistic plans as compared to the previous state‐of‐the‐art approaches.

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W e investigate the scheduling practices of a multidisciplinary, multistage, outpatient health care program. Patients undergo a series of assessments before being eligible for elective surgery. Such systems often suffer from high rates of attrition and appointment no-shows leading to capacity underutilization and treatment delays. We propose a new scheduling model where the clinic assigns patients to an appointment day but postpones the decision of which assessments patients undergo pending the observation of who arrives. In doing so, the clinic gains flexibility to improve system performance. We formulate the scheduling problem as a Markov decision process and use approximate dynamic programming to solve it. We apply our approach to a dataset collected from a bariatric surgery program at a large tertiary hospital in Toronto, Canada. We examine the quality of our solutions via structural results and compare them with heuristic scheduling practices using a discrete-event simulation. By allowing multiple assessments, delaying their scheduling, and by optimizing over an appointment book, we show significant improvements in patient throughput, clinic profit, use of overtime, and staff utilization.

We determine how prediction methods combine with optimization methods in two-stage knowledge-based planning (KBP) pipelines to produce radiation therapy treatment plans. We trained two dose prediction methods, a generative adversarial network (GAN) and a random forest (RF) with the same 130 treatment plans. The models were applied to 87 out-of-sample patients to create two sets of predicted dose distributions that were used as input to two optimization models. The first optimization model, inverse planning (IP), estimates weights for dose-objectives from a predicted dose distribution and generates new plans using conventional inverse planning. The second optimization model, dose mimicking (DM), minimizes the sum of one-sided quadratic penalties between the predictions and the generated plans using several dose-objectives. Altogether, four KBP pipelines (GAN-IP, GAN-DM, RF-IP, and RF-DM) were constructed and benchmarked against the corresponding clinical plans using clinical criteria; the error of both prediction methods was also evaluated. The best performing plans were GAN-IP plans, which satisfied the same criteria as their corresponding clinical plans (78%) more often than any other KBP pipeline. However, GAN did not necessarily provide the best prediction for the second-stage optimization models. Specifically, both the RF-IP and RF-DM plans satisfied all clinical criteria 25% and 15% more often than GAN-DM plans (the worst performing planning), respectively. GAN predictions also had a higher mean absolute error (3.9 Gy) than those from RF (3.6 Gy). We find that state-of-the-art prediction methods when paired with different optimization algorithms, produce treatment plans with considerable variation in quality.

Background:Increasing rates of obesity have led to growing demand for bariatric surgery. This has implications for wait times, particularly in publicly funded programs. This study examined the impact of patient and operational factors on wait times in a multidisciplinary bariatric surgery program. Methods:A retrospective study was conducted involving patients who were referred to a tertiary care centre (University Health Network, Toronto Western Hospital, Toronto) for bariatric surgery between June 2008 and July 2011. Patient characteristics, dates of clinical assessments and records describing operational changes were collected. Univariable analysis and multivariable log-linear and parametric time-to-event regressions were performed to determine whether patient and operational covariates were associated with the wait time for bariatric surgery (i.e., length of preoperative evaluation). Results:Of the 1664 patients included in the analysis, 724 underwent surgery with a mean wait time of 440 (standard deviation 198) days and a median wait time of 445 (interquartile range 298−533) days. Wait times ranged from 3 months to 4 years. Univariable and multivariable analyses showed that patients with active substance use (β = 0.3482, p = 0.02) and individuals who entered the program in more recent operational periods (β = 0.2028, p < 0.001) had longer wait times. Additionally, the median time-to-surgery increased over 3 discrete operational periods (characterized by specific program changes related to scheduling and staffing levels, and varying referral rates and defined surgical targets; p < 0.001).Interpretation: Some patients could be identified at referral as being at risk for longer wait times. We also found that previous operational decisions significantly increased the wait time in the program since its inception. Therefore, careful consideration must be devoted to process-level decision-making for multistage bariatric surgical programs, because managerial and procedural changes can affect timely access to treatment. Abstract Research CMAJ OPEN E332CMAJ OPEN, 3(3)