Qingrong Wu

BACKGROUND: Artificial intelligence (AI) based radiotherapy treatment planning tools have gained interest in automating the treatment planning process. It is essential to understand their overall robustness in various clinical scenarios. This is an existing gap between many AI based tools and their actual clinical deployment. This study works to fill the gap for AI based treatment planning by investigating a clinical robustness assessment (CRA) tool for the AI based planning methods using a phantom simulation approach. METHODS: A cylindrical phantom was created in the treatment planning system (TPS) with the axial dimension of 30 cm by 18 cm. Key structures involved in pancreas stereotactic body radiation therapy (SBRT) including PTV25, PTV33, C-Loop, stomach, bowel and liver were created within the phantom. Several simulation scenarios were created to mimic multiple scenarios of anatomical changes, including displacement, expansion, rotation and combination of three. The goal of treatment planning was to deliver 25 Gy to PTV25 and 33 Gy to PTV33 in 5 fractions in simultaneous integral boost (SIB) manner while limiting luminal organ-at-risk (OAR) max dose to be under 29 Gy. A previously developed deep learning based AI treatment planning tool for pancreas SBRT was identified as the validation object. For each scenario, the anatomy information was fed into the AI tool and the final fluence map associated to the plan was generated, which was subsequently sent to TPS for leaf sequencing and dose calculation. The final auto plan's quality was analyzed against the treatment planning constraint. The final plans' quality was further analyzed to evaluate potential correlation with anatomical changes using the Manhattan plot. RESULTS: A total of 32 scenarios were simulated in this study. For all scenarios, the mean PTV25 V25Gy of the AI based auto plans was 96.7% while mean PTV33 V33Gy was 82.2%. Large variation (16.3%) in PTV33 V33Gy was observed due to anatomical variations, a.k.a. proximity of luminal structure to PTV33. Mean max dose was 28.55, 27.68 and 24.63 Gy for C-Loop, bowel and stomach, respectively. Using D0.03cc as max dose surrogate, the value was 28.03, 27.12 and 23.84 Gy for C-Loop, bowel and stomach, respectively. Max dose constraint of 29 Gy was achieved for 81.3% cases for C-Loop and stomach, and 78.1% for bowel. Using D0.03cc as max dose surrogate, the passing rate was 90.6% for C-Loop, and 81.3% for bowel and stomach. Manhattan plot revealed high correlation between the OAR over dose and the minimal distance between the PTV33 and OAR. CONCLUSIONS: The results showed promising robustness of the pancreas SBRT AI tool, providing important evidence of its readiness for clinical implementation. The established workflow could guide the process of assuring clinical readiness of future AI based treatment planning tools.

Purpose.We have previously reported an artificial intelligence (AI) agent that automatically generates intensity-modulated radiation therapy (IMRT) plans via fluence map prediction, by-passing inverse planning. This AI agent achieved clinically comparable quality for prostate cases, but its performance on head-and-neck patients leaves room for improvement. This study aims to collect insights of the deep-learning-based (DL-based) fluence map prediction model by systematically analyzing its prediction errors.Methods.From the modeling perspective, the DL model's output is the fluence maps of IMRT plans. However, from the clinical planning perspective, the plan quality evaluation should be based on the clinical dosimetric criteria such as dose-volume histograms. To account for the complex and non-intuitive relationships between fluence map prediction errors and the corresponding dose distribution changes, we propose a novel error analysis approach that systematically examines plan dosimetric changes that are induced by varying amounts of fluence prediction errors. We investigated four decomposition modes of model prediction errors. The two spatial domain decompositions are based on fluence intensity and fluence gradient. The two frequency domain decompositions are based on Fourier-space banded frequency rings and Fourier-space truncated low-frequency disks. The decomposed error was analyzed for its impact on the resulting plans' dosimetric metrics. The analysis was conducted on 15 test cases spared from the 200 training and 16 validation cases used to train the model.Results.Most planning target volume metrics were significantly correlated with most error decompositions. The Fourier space disk radii had the largest Spearman's coefficients. The low-frequency region within a disk of ∼20% Fourier space contained most of errors that impact overall plan quality.Conclusions.This study demonstrates the feasibility of using fluence map prediction error analysis to understand the AI agent's performance. Such insights will help fine-tune the DL models in architecture design and loss function selection.

Deep learning algorithms for radiation therapy treatment planning automation require large patient datasets and complex architectures that often take hundreds of hours to train. Some of these algorithms require constant dose updating (such as with reinforcement learning) and may take days. When these algorithms rely on commerical treatment planning systems to perform dose calculations, the data pipeline becomes the bottleneck of the entire algorithm's efficiency. Further, uniformly accurate distributions are not always needed for the training and approximations can be introduced to speed up the process without affecting the outcome. These approximations not only speed up the calculation process, but allow for custom algorithms to be written specifically for the purposes of use in AI/ML applications where the dose and fluence must be calculated a multitude of times for a multitude of different situations. Here we present and investigate the effect of introducing matrix sparsity through kernel truncation on the dose calculation for the purposes of fluence optimzation within these AI/ML algorithms. The basis for this algorithm relies on voxel discrimination in which numerous voxels are pruned from the computationally expensive part of the calculation. This results in a significant reduction in computation time and storage. Comparing our dose calculation against calculations in both a water phantom and patient anatomy in Eclipse without heterogenity corrections produced gamma index passing rates around 99% for individual and composite beams with uniform fluence and around 98% for beams with a modulated fluence. The resulting sparsity introduces a reduction in computational time and space proportional to the square of the sparsity tolerance with a potential decrease in cost greater than 10 times that of a dense calculation allowing not only for faster caluclations but for calculations that a dense algorithm could not perform on the same system.

<h4>Purpose/objective(s)</h4>With many deep learning (DL) models being developed for clinical applications, it is important to understand their behavior and clinical consequence. This study aims to collect insights of the relationship between fluence map prediction error and its dosimetric impacts in a DL-based AI agent for H&N IMRT planning.<h4>Materials/methods</h4>An AI agent has been implemented to generate IMRT plans via fluence map prediction, bypassing inverse optimization. While the prostate IMRT plans generated by the agent were comparable to clinical plans in quality, the application into H&N patients exhibited large variations in the plan quality due to higher anatomy complexity. As the DL model's output is fluence maps of an IMRT plan, standard error analyses were focused on the differences between the predicted and ground truth fluence maps, i.e., prediction error. However, the ultimate plan evaluation is based on clinical criteria such as DVHs and dose distributions. Therefore, the AI agent's performance in clinics is subjected to complex and non-intuitive relationships between fluence map prediction error and corresponding dose distribution changes, and warrants thorough investigation. In this study, a series of tests were designed to collect insights of the impact of DL model performance on plan's dosimetric quality. The fluence map prediction error was analyzed for its dosimetric effects using five error decomposition modes:1) ground truth fluence intensity bands in 5 threshold levels, 2) predicted fluence intensity bands in 5 threshold levels, 3) ground truth fluence gradient bands (high and low), 4) Fourier space bands (frequency bands) in 8 threshold levels, and 5) Fourier space circles (below certain frequency) in 8 threshold levels. The DL model was trained with 216 cases and tested with 15 additional cases. PTV and OAR dosimetric metrics were analyzed by Spearman's rank tests (P = 0.05).<h4>Results</h4>Most PTV-related metrics were significantly correlated with the error components. Among the different decomposition modes, the Fourier space circle radii have large Spearman's coefficients with PTV metrics, suggesting that they were best able to extract error components that reveal plan quality impacts. The low-frequency error within a Fourier space circle of radius = 32 pixels (20% of Fourier space) had the most significant impact on overall plan quality and PTV heterogeneity.<h4>Conclusion</h4>The fluence map prediction error analysis is critical to evaluate the AI agent performance. Such insight will help with fine-tuning the DL models in architecture design and loss function selection.

<h4>Purpose/objective(s)</h4>To develop a tutoring program to help physician and physics residents to learn pancreas stereotactic body radiation therapy (SBRT) treatment planning via carefully collected cases and a series of specially designed knowledge models as teaching aid. Such programs are especially important during the Covid pandemic when most traditional hands-on medical teaching had to be moved online and asynchronous.<h4>Materials/methods</h4>The pilot tutoring program was composed of 5 teaching cases (1 benchmark case and 4 teaching cases), an interactive knowledge module (IKM) and a performance grading system. The tutoring program started with the benchmark case completed by each trainee independently to benchmark the baseline skill level. This same case was re-tested to evaluate the performance and clinical planning readiness after the trainee completes the tutoring program. 4 teaching cases were included: simple (1), intermediate (2) and complex (1). All 5 cases have simultaneous integrated boost prescription of 25Gy to the elective volume and 33Gy to the gross tumor volume. The IKM included a dose-volume prediction knowledge model for pancreas SBRT and is integrated via a graphic user interface in the treatment planning system. The trainee can seek reference and guidance from the IKM during learning - mimicking the hands-on tutoring of human expert. The grading system summarize the plan quality by weighing key dosimetric endpoints and their relative importance. Grade point average (GPA) was introduced to qualitatively appraise the plan quality into A, B and C (within 3%, 3-10% and > 10% difference of clinical plan score, corresponding to 4, 3, 2 point respectively). 5 trainees with minimum planning experience completed the teaching course.<h4>Results</h4>Trainees achieved an average of 65.1% of total points (3.6 GPA) with 84 minutes planning time for the benchmark case pre-teaching, and improved to an average of 75.7% (4.2 GPA) using 48 minutes post-teaching. The clinical plan scored 72.7% of total points. All trainees improved their teaching plans' scores after taking the virtual tutoring program. Post-teaching, all trainees received the GPA of A (clinical quality level) on the benchmark case planning. The total teaching time for each trainee ranged between 5 and 7 hours.<h4>Conclusion</h4>The tutoring program with knowledge support modules provides encouraging learning outcomes in pancreas SBRT planning for inexperienced planners. This AI-enabled virtual teaching tool could provide valuable addition to the traditional human resource heavy in-person teaching of IMRT and SBRT treatment planning.