Rong, Yi

AbstractBackgroundBolus materials have been used for decades in radiotherapy. Most frequently, these materials are utilized to bring dose closer to the skin surface to cover superficial targets optimally. While cavity filling, such as nasal cavities, is desirable, traditional commercial bolus is lacking, requiring other solutions. Recently, investigators have worked on utilizing 3D printing technology, including commercially available solutions, which can overcome some challenges with traditional bolus.PurposeTo utilize failure modes and effects analysis (FMEA) to successfully implement a comprehensive 3D printed bolus solution to replace commercial bolus in our clinic using a series of open‐source (or free) software products.Methods3D printed molds for bespoke bolus were created by exporting the DICOM structures of the bolus designed in the treatment planning system and manipulated to create a multipart mold for 3D printing. A silicone (Ecoflex 00–30) mixture is poured into the mold and cured to form the bolus. Molds for sheet bolus of five thicknesses were also created. A comprehensive FMEA was performed to guide workflow adjustments and QA steps.ResultsThe process map identified 39 and 30 distinct steps for the bespoke and flat sheet bolus workflows, respectively. The corresponding FMEA highlighted 119 and 86 failure modes, with 69 shared between the processes. Misunderstanding of plan intent was a potential cause for most of the highest‐scoring failure modes, indicating that physics and dosimetry involvement early in the process is paramount.ConclusionFMEA informed the design and implementation of QA steps to guarantee a safe and high‐quality comprehensive implementation of silicone bolus from 3D printed molds. This approach allows for greater adaptability not afforded by traditional bolus, as well as potential dissemination to other clinics due to the open‐source nature of the workflow.

AbstractPurposeTo assess the practicality of employing a commercial knowledge‐based planning tool (RapidPlan) to generate adapted intact prostate and prostate bed volumetric modulated arc therapy (VMAT) plans on iterative cone‐beam computed tomography (iCBCT) datasets.Methods and materialsIntact prostate and prostate bed RapidPlan models were trained utilizing planning data from 50 and 44 clinical cases, respectively. To ensure that refined models were capable of producing adequate clinical plans with a single optimization, models were tested with 50 clinical planning CT datasets by comparing dose‐volume histogram (DVH) and plan quality metric (PQM) values between clinical and RapidPlan‐generated plans. The RapidPlan tool was then used to retrospectively generate adapted VMAT plans on daily iCBCT images for 20 intact prostate and 15 prostate bed cases. As before, DVH and PQM metrics were utilized to dosimetrically compare scheduled (iCBCT Verify) and adapted (iCBCT RapidPlan) plans. Timing data was collected to further evaluate the feasibility of integrating this approach within an online adaptive radiotherapy workflow.ResultsModel testing results confirmed the models were capable of producing VMAT plans within a single optimization that were overall improved upon or dosimetrically comparable to original clinical plans. Direct application of RapidPlan on iCBCT datasets produced satisfactory intact prostate and prostate bed plans with generally improved target volume coverage/conformality and rectal sparing relative to iCBCT Verify plans as indicated by DVH values, though bladder metrics were marginally increased on average. Average PQM values for iCBCT RapidPlans were significantly improved compared to iCBCT Verify plans. The average time required [in mm:ss] to generate adapted plans was 06:09 ± 02:06 (intact) and 07:12 ± 01:04 (bed).ConclusionThis study demonstrated the feasibility of leveraging RapidPlan to expeditiously generate adapted VMAT intact prostate and prostate bed plans on iCBCT datasets. In general, adapted plans were dosimetrically improved relative to scheduled plans, emphasizing the practicality of the proposed approach.

AbstractPurposeTo present a modified calibration method to reduce signal drift due to table sagging in Respiratory Gating for Scanner (RGSC) systems with a wall‐mounted camera.Materials and MethodsApproximately 70 kg of solid water phantoms were evenly distributed on the CT couch, mimicking the patient's weight. New calibration measurements were performed at 9 points at the combination of three lateral positions, the CT isocenter and ±10 cm laterally from the isocenter, and three longitudinal locations, the CT isocenter and ±30 cm or ±40 cm from the isocenter. The new calibration was tested in two hospitals.ResultsImplementing the new weighed calibration method at the extended distance yielded improved results during the DIBH scan, reducing the drift to within 1 from 3 mm. The extended calibration positions exhibited similarly reduced drift in both hospitals, reinforcing the method's robustness and its potential applicability across all centers.ConclusionThis proposed solution aims to minimize the systematic error in radiation delivery for patients undergoing motion management with wall‐mounted camera RGSC systems, especially in conjunction with a bariatric CT couchtop.

Purpose: To evaluate the accuracy of deep-learning-based auto-segmentation of the superior constrictor, middle constrictor, inferior constrictor, and larynx in comparison with a traditional multi-atlas-based method. Methods and Materials: One hundred and five computed tomography image datasets from 83 head and neck cancer patients were retrospectively collected and the superior constrictor, middle constrictor, inferior constrictor, and larynx were analyzed for deep-learning versus multi-atlas-based segmentation. Eighty-three computed tomography images (40 diagnostic computed tomography and 43 planning computed tomography) were used for training the convolutional neural network, and for atlas-based model training. The remaining 22 computed tomography datasets were used for validation of the atlas-based auto-segmentation versus deep-learning-based auto-segmentation contours, both of which were compared with the corresponding manual contours. Quantitative measures included Dice similarity coefficient, recall, precision, Hausdorff distance, 95th percentile of Hausdorff distance, and mean surface distance. Dosimetric differences between the auto-generated contours and manual contours were evaluated. Subjective evaluation was obtained from 3 clinical observers to blindly score the autosegmented structures based on the percentage of slices that require manual modification. Results: The deep-learning-based auto-segmentation versus atlas-based auto-segmentation results were compared for the superior constrictor, middle constrictor, inferior constrictor, and larynx. The mean Dice similarity coefficient values for the 4 structures were 0.67, 0.60, 0.65, and 0.84 for deep-learning-based auto-segmentation, whereas atlas-based auto-segmentation has Dice similarity coefficient results at 0.45, 0.36, 0.50, and 0.70, respectively. The mean 95th percentile of Hausdorff distance (cm) for the 4 structures were 0.41, 0.57, 0.59, and 0.54 for deep-learning-based auto-segmentation, but 0.78, 0.95, 0.96, and 1.23 for atlas-based auto-segmentation results, respectively. Similar mean dose differences were obtained from the 2 sets of autosegmented contours compared to manual contours. The dose–volume discrepancies and the average modification rates were higher with the atlas-based auto-segmentation contours. Conclusion: Swallowing-related structures are more accurately generated with DL-based versus atlas-based segmentation when compared with manual contours.

Abstract Background. Quantitative radiomic features of medical images could provide clinical significance in assisting decision-making, but the existing feature selection and modeling methods are usually parameter-dependent. We aim to develop and validate a generalized radiomic method applicable to a variety of clinical outcomes. Methods and materials. A generalized methodology for radiomic feature selection and modeling (‘GRFM’ for short), including two-step feature selection and logistic regression, was proposed for studying clinical outcomes correlations. The two-step feature selection consists of Pearson correlation analysis followed by a sequential forward floating selection algorithm to identify robust feature subsets. We also applied an adaptive searching strategy to systematically determine globally optimal parameters, rather than relying on preset parameters. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of three outcomes: lymph node metastasis of gastric cancer (GC), the five-year survival status of high-grade osteosarcoma (HOS), and the pathological grade of pancreatic neuroendocrine tumors (pNETs). Results. The optimal Pearson thresholds were 0.85, 0.80 and 0.75, and the optimal feature numbers were 11, 14 and 8 in GC, HOS and pNETs, respectively. The AUC values of the three predictive models combined with the corresponding parameters were 0.9017 versus 0.9026, 0.7652 versus 0.7113, and 0.8438 versus 0.8212 for the training and validation cohorts, showing promissing generality and classifier performance . Conclusion. The proposed method was helpful in predicting different clinical outcomes, and has potential application as a general and noninvasive prediction tool to guide clinical decision-making in various cancer sites.