Validation of an automated contouring and treatment planning tool for pediatric craniospinal radiation therapy

PurposeTreatment planning for craniospinal irradiation (CSI) is complex and time-consuming, especially for resource-constrained centers. To alleviate demanding workflows, we successfully automated the pediatric CSI planning pipeline in previous work. In this work, we validated our CSI autosegmentation and autoplanning tool on a large dataset from St. Jude Children’s Research Hospital.MethodsSixty-three CSI patient CT scans were involved in the study. Pre-planning scripts were used to automatically verify anatomical compatibility with the autoplanning tool. The autoplanning pipeline generated 15 contours and a composite CSI treatment plan for each of the compatible test patients (n=51). Plan quality was evaluated quantitatively with target coverage and dose to normal tissue metrics and qualitatively with physician review, using a 5-point Likert scale. Three pediatric radiation oncologists from 3 institutions reviewed and scored 15 contours and a corresponding composite CSI plan for the final 51 test patients. One patient was scored by 3 physicians, resulting in 53 plans scored total.ResultsThe algorithm automatically detected 12 incompatible patients due to insufficient junction spacing or head tilt and removed them from the study. Of the 795 autosegmented contours reviewed, 97% were scored as clinically acceptable, with 92% requiring no edits. Of the 53 plans scored, all 51 brain dose distributions were scored as clinically acceptable. For the spine dose distributions, 92%, 100%, and 68% of single, extended, and multiple-field cases, respectively, were scored as clinically acceptable. In all cases (major or minor edits), the physicians noted that they would rather edit the autoplan than create a new plan.ConclusionsWe successfully validated an autoplanning pipeline on 51 patients from another institution, indicating that our algorithm is robust in its adjustment to differing patient populations. We automatically generated 15 contours and a comprehensive CSI treatment plan for each patient without physician intervention, indicating the potential for increased treatment planning efficiency and global access to high-quality radiation therapy

Automating the Radiation Therapy Treatment Planning Process for Pediatric Patients with Medulloblastoma

Over the past 50 years, pediatric cancer 5-year survival rates increased from 20% to 80% in high-income countries, however, these trends have not been mirrored in low-and-middle-income countries (LMICs). This is due in part to delayed diagnosis, higher rates of advanced disease at presentation and a growing lack of access to high quality medical personnel and technology necessary to deliver complex treatments. The long-term goal of this study was to alleviate demanding workflows and increase global access to high-quality pediatric radiation therapy by harnessing the power of artificial intelligence to automate the radiation therapy treatment planning process for pediatric patients with medulloblastoma. Radiation therapy for medulloblastoma consists of radiation to the craniospinal axis (CSI) and a boost of radiation to the post-operative tumor resection cavity. In this study we automated the treatment planning process for the primary course and boost treatment using deep learning and other automation approaches for autocontouring and autoplanning. First, we developed and tested a 3D conformal CSI autoplanning tool for varying patient sizes based on the recommendations from the International Society of Pediatric Oncology (SIOP). The autocontoured structures’ average Dice similarity coefficient (DSC) ranged from 0.65-0.98. Of the 18 plans tested, all were scored as clinically acceptable as-is or clinically acceptable with minor, time-efficient edits preferred or required. No plans were scored as clinically unacceptable. Next, we tested the autocontouring and autoplanning tools on 51 CSI CT scans provided from St. Jude Children’s Research Hospital to generate 15 autocontours and a composite CSI treatment plan. Three pediatric radiation oncologists from 3 institutions reviewed and scored each autocontour and plan. Of the 795 autocontours reviewed by 3 physicians, 97% of the autocontours were scored as clinically acceptable, with 92% of them requiring no edits. The clinically acceptability of the autoplans was divided by treatment field (brain and spine). For the brain field dose distributions, 100% were clinically acceptable. For the spine dose distributions, 92% of single field, 100% of extended field, and 68% of multiple field cases were scored as clinically acceptable. Most unacceptable cases were from the multiple field configuration, which is the most complex spine field configuration to plan. In all cases (major or minor edits), the physicians noted that they would rather edit the autoplan rather than create a new plan. In the second aim of the experiment, we set to automate the treatment planning process for the resection cavity boost which included automatically contouring the post-operative gross tumor volume (GTV) resection cavity and generating a 3D conformal treatment plan. To automatically contour the GTV, we trained a CT-based, MRI-based, and multi-modality based autocontouring model. DSC (Mean±1σ) scores were 0.75±0.16 for CT-only, 0.77±0.15 for MRI-only, and 0.80±0.12 for multi-modality models. Hausdorff distances for the MRI-only and multi-modality models were significantly lower than for the CT-only model (p\u3c0.001 and p=0.013, respectively). In clinical review, the MRI-only model achieved the best boundary detection. Finally, using the automatically contoured GTV volumes from each respective imaging modality, we designed a script to automatically generate 3D conformal boost treatment plans. We investigated the impact of adjusting planning parameters to design an optimization algorithm that could generate a patient-specific plan with a homogenous dose to the target and minimal dose to healthy tissues. We defined clinical acceptability as achieving 95% V95 to the clinical CTV volume for each plan generated. Each patient had 8 treatment plans (4 contours and 2 wedge angles) which gave a total of 104 treatment plans to analyze. Of these, 85% were clinically acceptable. We also we able to minimize dose to healthy tissues such as the cochlea. In conclusion, we successfully designed and tested a fully automated CSI autocontouring and treatment planning pipeline. Moreover, we successfully tested the tool on data from another institution, proving that our algorithms successfully accommodated different patient populations. Additionally, we successfully autocontoured the post-operative GTV volumes for patients using CT, MRI, or both images, and automated the boost treatment planning process to treat each respective target volume. Automating both aspects of radiation therapy for medulloblastoma has the potential to decrease demanding workflows and increase global access to high-quality pediatric radiation therapy

Total Body Irradiation Aluminum Compensators

We propose the utilization of waterjet-fabricated aluminum compensators in total body irradiation (TBI) treatments using the technique known as AP/PA. Lead was substituted with aluminum to create a safer and more efficient compensator design, which was then constructed using SOLIDWORKS. The Engineering Innovation Center at Texas A&M University used a ShopBot Auto Router to fabricate the final prototype. In future directions, the prototype’s performance will be analyzed by measuring surface dose information using an anthropomorphic phantom. From this information the uniformity of dose distribution will be assessed and compared to that of current lead compensators to determine the efficiency of the proposed substitution

Total Body Irradiation Aluminum Compensators

We propose the utilization of waterjet-fabricated aluminum compensators in total body irradiation (TBI) treatments using the technique known as AP/PA. Lead was substituted with aluminum to create a safer and more efficient compensator design, which was then constructed using SOLIDWORKS. The Engineering Innovation Center at Texas A&M University used a ShopBot Auto Router to fabricate the final prototype. In future directions, the prototype’s performance will be analyzed by measuring surface dose information using an anthropomorphic phantom. From this information the uniformity of dose distribution will be assessed and compared to that of current lead compensators to determine the efficiency of the proposed substitution

Chemical Design of Both a Glutathione-Sensitive Dimeric Drug Guest and a Glucose-Derived Nanocarrier Host to Achieve Enhanced Osteosarcoma Lung Metastatic Anticancer Selectivity

Although nanomedicines have been pursued for nearly 20 years, fundamental chemical strategies that seek to optimize both the drug and drug carrier together in a concerted effort remain uncommon yet may be powerful. In this work, two block polymers and one dimeric prodrug molecule were designed to be coassembled into degradable, functional nanocarriers, where the chemistry of each component was defined to accomplish important tasks. The result is a poly­(ethylene glycol) (PEG)-protected redox-responsive dimeric paclitaxel (diPTX)-loaded cationic poly­(d-glucose carbonate) micelle (diPTX@CPGC). These nanostructures showed tunable sizes and surface charges and displayed controlled PTX drug release profiles in the presence of reducing agents, such as glutathione (GSH) and dithiothreitol (DTT), thereby resulting in significant selectivity for killing cancer cells over healthy cells. Compared to free PTX and diPTX, diPTX@CPGC exhibited improved tumor penetration and significant inhibition of tumor cell growth toward osteosarcoma (OS) lung metastases with minimal side effects both in vitro and in vivo, indicating the promise of diPTX@CPGC as optimized anticancer therapeutic agents for treatment of OS lung metastases

Synthetic Megavoltage Cone Beam Computed Tomography Image Generation for Improved Contouring Accuracy of Cardiac Pacemakers

In this study, we aimed to enhance the contouring accuracy of cardiac pacemakers by improving their visualization using deep learning models to predict MV CBCT images based on kV CT or CBCT images. Ten pacemakers and four thorax phantoms were included, creating a total of 35 combinations. Each combination was imaged on a Varian Halcyon (kV/MV CBCT images) and Siemens SOMATOM CT scanner (kV CT images). Two generative adversarial network (GAN)-based models, cycleGAN and conditional GAN (cGAN), were trained to generate synthetic MV (sMV) CBCT images from kV CT/CBCT images using twenty-eight datasets (80%). The pacemakers in the sMV CBCT images and original MV CBCT images were manually delineated and reviewed by three users. The Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and mean surface distance (MSD) were used to compare contour accuracy. Visual inspection showed the improved visualization of pacemakers on sMV CBCT images compared to original kV CT/CBCT images. Moreover, cGAN demonstrated superior performance in enhancing pacemaker visualization compared to cycleGAN. The mean DSC, HD95, and MSD for contours on sMV CBCT images generated from kV CT/CBCT images were 0.91 ± 0.02/0.92 ± 0.01, 1.38 ± 0.31 mm/1.18 ± 0.20 mm, and 0.42 ± 0.07 mm/0.36 ± 0.06 mm using the cGAN model. Deep learning-based methods, specifically cycleGAN and cGAN, can effectively enhance the visualization of pacemakers in thorax kV CT/CBCT images, therefore improving the contouring precision of these devices

Automated contouring and statistical process control for plan quality in a breast clinical trial

Background and purpose: Automatic review of breast plan quality for clinical trials is time-consuming and has some unique challenges due to the lack of target contours for some planning techniques. We propose using an auto-contouring model and statistical process control to independently assess planning consistency in retrospective data from a breast radiotherapy clinical trial. Materials and methods: A deep learning auto-contouring model was created and tested quantitatively and qualitatively on 104 post-lumpectomy patients’ computed tomography images (nnUNet; train/test: 80/20). The auto-contouring model was then applied to 127 patients enrolled in a clinical trial. Statistical process control was used to assess the consistency of the mean dose to auto-contours between plans and treatment modalities by setting control limits within three standard deviations of the data’s mean. Two physicians reviewed plans outside the limits for possible planning inconsistencies. Results: Mean Dice similarity coefficients comparing manual and auto-contours was above 0.7 for breast clinical target volume, supraclavicular and internal mammary nodes. Two radiation oncologists scored 95% of contours as clinically acceptable. The mean dose in the clinical trial plans was more variable for lymph node auto-contours than for breast, with a narrower distribution for volumetric modulated arc therapy than for 3D conformal treatment, requiring distinct control limits. Five plans (5%) were flagged and reviewed by physicians: one required editing, two had clinically acceptable variations in planning, and two had poor auto-contouring. Conclusions: An automated contouring model in a statistical process control framework was appropriate for assessing planning consistency in a breast radiotherapy clinical trial

Automated Contouring and Planning in Radiation Therapy: What Is ‘Clinically Acceptable’?

Developers and users of artificial-intelligence-based tools for automatic contouring and treatment planning in radiotherapy are expected to assess clinical acceptability of these tools. However, what is ‘clinical acceptability’? Quantitative and qualitative approaches have been used to assess this ill-defined concept, all of which have advantages and disadvantages or limitations. The approach chosen may depend on the goal of the study as well as on available resources. In this paper, we discuss various aspects of ‘clinical acceptability’ and how they can move us toward a standard for defining clinical acceptability of new autocontouring and planning tools