Evaluating machine learning enhanced intelligent-optimization-engine (IOE) performance for ethos head-and-neck (HN) plan generation.

PURPOSE: Varian Ethos utilizes novel intelligent-optimization-engine (IOE) designed to automate the planning. However, this introduced a black box approach to plan optimization and challenge for planners to improve plan quality. This study aims to evaluate machine-learning-guided initial reference plan generation approaches for head & neck (H&N) adaptive radiotherapy (ART). METHODS: Twenty previously treated patients treated on C-arm/Ring-mounted were retroactively re-planned in the Ethos planning system using a fixed 18-beam intensity-modulated radiotherapy (IMRT) template. Clinical goals for IOE input were generated using (1) in-house deep-learning 3D-dose predictor (AI-Guided) (2) commercial knowledge-based planning (KBP) model with universal RTOG-based population criteria (KBP-RTOG) and (3) an RTOG-based constraint template only (RTOG) for in-depth analysis of IOE sensitivity. Similar training data was utilized for both models. Plans were optimized until their respective criteria were achieved or DVH-estimation band was satisfied. Plans were normalized such that the highest PTV dose level received 95% coverage. Target coverage, high-impact organs-at-risk (OAR) and plan deliverability was assessed in comparison to clinical (benchmark) plans. Statistical significance was evaluated using a paired two-tailed student t-test. RESULTS: AI-guided plans were superior to both KBP-RTOG and RTOG-only plans with respect to clinical benchmark cases. Overall, OAR doses were comparable or improved with AI-guided plans versus benchmark, while they increased with KBP-RTOG and RTOG plans. However, all plans generally satisfied the RTOG criteria. Heterogeneity Index (HI) was on average <1.07 for all plans. Average modulation factor was 12.2 ± 1.9 (p = n.s), 13.1 ± 1.4 (p = <0.001), 11.5 ± 1.3 (p = n.s.) and 12.2 ± 1.9 for KBP-RTOG, AI-Guided, RTOG and benchmark plans, respectively. CONCLUSION: AI-guided plans were the highest quality. Both KBP-enabled and RTOG-only plans are feasible approaches as clinics adopt ART workflows. Similar to constrained optimization, the IOE is sensitive to clinical input goals and we recommend comparable input to an institution's planning directive dosimetric criteria.

The accuracy of artificial intelligence deformed nodal structures in cervical online cone-beam-based adaptive radiotherapy.

Background and Purpose: Online cone-beam-based adaptive radiotherapy (ART) adjusts for anatomical changes during external beam radiotherapy. However, limited cone-beam image quality complicates nodal contouring. Despite this challenge, artificial-intelligence guided deformation (AID) can auto-generate nodal contours. Our study investigated the optimal use of such contours in cervical online cone-beam-based ART. Materials and Methods: From 136 adaptive fractions across 21 cervical cancer patients with nodal disease, we extracted 649 clinically-delivered and AID clinical target volume (CTV) lymph node boost structures. We assessed geometric alignment between AID and clinical CTVs via dice similarity coefficient, and 95% Hausdorff distance, and geometric coverage of clinical CTVs by AID planning target volumes by false positive dice. Coverage of clinical CTVs by AID contour-based plans was evaluated using D100, D95, V100%, and V95%. Results: Between AID and clinical CTVs, the median dice similarity coefficient was 0.66 and the median 95 % Hausdorff distance was 4.0 mm. The median false positive dice of clinical CTV coverage by AID planning target volumes was 0. The median D100 was 1.00, the median D95 was 1.01, the median V100% was 1.00, and the median V95% was 1.00. Increased nodal volume, fraction number, and daily adaptation were associated with reduced clinical CTV coverage by AID-based plans. Conclusion: In one of the first reports on pelvic nodal ART, AID-based plans could adequately cover nodal targets. However, physician review is required due to performance variation. Greater attention is needed for larger, daily-adapted nodes further into treatment.

Impact of manual contour editing on plan quality for online adaptive radiotherapy for head and neck cancer.

PURPOSE: Online adaptive radiotherapy (oART) has high resource costs especially for head-and-neck (H&N) cancer, which requires recontouring complex targets and numerous organs-at-risk (OARs). ART systems provide auto-contours to help, we aim to explore the optimal level of editing automatic contours to maintain plan quality in a cone-beam-computed-tomography (CBCT)-based oART system for H&N. In this system influencer OAR contours are generated and reviewed first, which then drives the auto-contouring of the remaining OARs and targets. METHODS AND MATERIALS: Three-hundred-and-forty-nine adapted fractions of forty-four H&N patients were retrospectively analyzed, with physician-edited OARs and targets. These contours and associated online adapted plans served as the gold standard for comparison. We simulated three contour editing workflows: (1) no editing of contours, (2) only editing the influencers, (3) editing the influencers and targets. The geometric difference was quantified with Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD). The dosimetric differences in target coverage and OAR doses were calculated between the gold standard and these three simulated workflows. RESULTS: Workflow 1 resulted in significantly inferior contour quality for all OARs (mean DSC 0.85±0.17 and HD95 3.10±5.80mm), dosimetric data was hence not calculated for workflow 1. In workflow (2), the frequency of physician editing targets and remaining OARs were 80.8%-95.7% and 2.3% (brachial plexus)-67.7% (oral cavity) respectively, where the OAR differences were geometrically minor (mean DSC>0.95 with std≤0.09). However, due to the unedited target contours of workflow 2 (mean DSC 0.86-0.92 and mean HD95 2.56-3.30mm versus the ground-truth targets), plans were inadequate with insufficient coverage. In workflow (3) when both targets and influencers were edited (non-influencer OARs were unedited), over 95.5% of the adapted plans achieved the patient-specific dosimetry goals. CONCLUSION: The CBCT-based H&N oART workflow can be meaningfully accelerated by only editing the influencers and targets while omitting the remaining OARs without compromising the quality of the adaptive plans.

Automated Lung Cancer Segmentation Using a PET and CT Dual-Modality Deep Learning Neural Network.

PURPOSE: To develop an automated lung tumor segmentation method for radiation therapy planning based on deep learning and dual-modality positron emission tomography (PET) and computed tomography (CT) images. METHODS AND MATERIALS: A 3-dimensional (3D) convolutional neural network using inputs from diagnostic PETs and simulation CTs was constructed with 2 parallel convolution paths for independent feature extraction at multiple resolution levels and a single deconvolution path. At each resolution level, the extracted features from the convolution arms were concatenated and fed through the skip connections into the deconvolution path that produced the tumor segmentation. Our network was trained/validated/tested by a 3:1:1 split on 290 pairs of PET and CT images from patients with lung cancer treated at our clinic, with manual physician contours as the ground truth. A stratified training strategy based on the magnitude of the gross tumor volume (GTV) was investigated to improve performance, especially for small tumors. Multiple radiation oncologists assessed the clinical acceptability of the network-produced segmentations. RESULTS: The mean Dice similarity coefficient, Hausdorff distance, and bidirectional local distance comparing manual versus automated contours were 0.79 ± 0.10, 5.8 ± 3.2 mm, and 2.8 ± 1.5 mm for the unstratified 3D dual-modality model. Stratification delivered the best results when the model for the large GTVs (>25 mL) was trained with all-size GTVs and the model for the small GTVs (<25 mL) was trained with small GTVs only. The best combined Dice similarity coefficient, Hausdorff distance, and bidirectional local distance from the 2 stratified models on their corresponding test data sets were 0.83 ± 0.07, 5.9 ± 2.5 mm, and 2.8 ± 1.4 mm, respectively. In the multiobserver review, 91.25% manual versus 88.75% automatic contours were accepted or accepted with modifications. CONCLUSIONS: By using an expansive clinical PET and CT image database and a dual-modality architecture, the proposed 3D network with a novel GTVbased stratification strategy generated clinically useful lung cancer contours that were highly acceptable on physician review.

Radiotherapy-Compatible Robotic System for Multi-Landmark Positioning in Head and Neck Cancer Treatments.

The spine flexibility creates one of the most significant challenges to proper positioning in radiation therapy of head and neck cancers. Even though existing immobilization techniques can reduce the positioning uncertainty, residual errors (2-3 mm along the cervical spine) cannot be mitigated by single translation-based approaches. Here, we introduce a fully radiotherapy-compatible electro-mechanical robotic system, capable of positioning a patient's head with submillimeter accuracy in clinically acceptable spatial constraints. Key mechanical components, designed by finite element analysis, are fabricated with 3D printing and a cyclic loading test of the printed materials captures a great mechanical robustness. Measured attenuation of most printed components is lower than analytic estimations and radiographic imaging shows no visible artifacts, implying full radio-compatibility. The new system evaluates the positioning accuracy with an anthropomorphic skeletal phantom and optical tracking system, which shows a minimal residual error (0.7 ± 0.3 mm). This device also offers an accurate assessment of the post correction error of aligning individual regions when the head and body are individually positioned. Collectively, the radiotherapy-compatible robotic system enables multi-landmark setup to align the head and body independently and accurately for radiation treatment, which will significantly reduce the need for large margins in the lower neck.