Clinical Associate Professor, Radiation Oncology - Radiation Physics
During the unprecedented workplace disruption from the corona virus disease 2019 (COVID-19) pandemic, health care workers have been particularly vulnerable to increased work-related stress and anxiety. This may have a negative effect on job performance and personal well-being. Personal safety, job security, and childcare needs are essential concerns that must be addressed by health care organizations to ensure stability of its workforce. In addition, workplace morale is also damaged by the many daily changes brought about by social distancing. Thus, opportunities exist for departments to address the loss of social bonding and cohesiveness needed for successful team building. In this report, we describe the efforts of our departmental workplace culture committee during this pandemic.
View details for DOI 10.1016/j.adro.2020.07.002
View details for PubMedID 32838068
View details for PubMedCentralID PMC7368646
Reproductive toxicity is common following total body irradiation and has major quality of life implications for patients. In that context, this is the first report of gonadal-sparing VMAT TBI, successfully delivered in a boy and a girl with aplastic anemia. Both patients' VMAT TBI plans demonstrated improved gonadal sparing versus simulated conventional 2D approach (mean testes dose 0.45 Gy VMAT versus 0.72 Gy 2D; mean ovary dose 0.64 Gy VMAT versus 1.47 Gy 2D). PTV coverage was also improved for both cases with the VMAT plan versus conventional 2D plan (2 Gy D90% versus 1.9 Gy D90%, respectively). Given these dosimetric advantages, the present study can serve as a proof-of-concept for further prospective studies evaluating this technique for wider applications in populations receiving TBI.
View details for DOI 10.1016/j.prro.2020.07.006
View details for PubMedID 32795616
Survivors of childhood cancer are at risk of long-term sequelae that arise as a consequence of cancer treatment. Radiation and chemotherapy treatment in pediatric female patients can have detrimental impacts on fertility, particularly in those with pelvic tumor involvement. We report 2 successful natural full-term pregnancies with vaginal delivery in a woman 12 years after biopsy, irradiation (55.5Gy), and multi-agent chemotherapy for treatment of pelvic Ewing sarcoma. Both children were born healthy, with no complications in pregnancy or delivery. Fertility preservation and risk assessment following chemotherapy/radiation therapy is evolving, providing new data to effectively counsel and treat young women.
View details for DOI 10.1097/MPH.0000000000001581
View details for PubMedID 31415018
An accurate prediction of achievable dose distribution on a patient specific basis would greatly improve IMRT/VMAT planning in both efficiency and quality. Recently machine learning techniques have been proposed for IMRT dose prediction based on patient's contour information from planning CT. In these existing prediction models geometric/anatomic features were learned for building the dose prediction models and few features that characterize the dosimetric properties of the patients were utilized. In this study we propose a method to incorporate the dosimetric features in the construction of a more reliable dose prediction model based on the deep convolutional neural network (CNN). In addition to the contour information, the dose distribution from a PTV-only plan (i.e., the plan with the best PTV coverage by sacrificing the OARs sparing) is also employed as the model input to build a deep learning based dose prediction model. A database of 60 volumetric modulated arc therapy (VMAT) plans for the prostate cancer patients was used for training. The trained prediction model was then tested on a cohort of 10 cases. Dose difference maps, DVHs, dosimetric endpoints and statistical analysis of the sum of absolute residuals (SARs) were used to evaluate the proposed method. Our results showed that the mean SARs for the PTV, bladder and rectum using our method were 0.007±0.003, 0.035±0.032 and 0.067±0.037 respectively, lower than the SARs obtained with the contours-based method, indicating the potential of the proposed approach in accurately predicting dose distribution.
View details for PubMedID 31082805
PURPOSE: One of the promising options for motion management in radiation therapy (RT) is the use of Linac-compatible robotic-arm-mounted ultrasound imaging system due to its high soft tissue contrast, real-time capability, absence of ionizing radiation and low cost. The purpose of this work is to develop a novel deep learning-based real-time motion tracking strategy for ultrasound image-guided RT.METHODS: The proposed tracker combined the attention-aware Fully Convolutional Neural Network (FCNN) and the Convolutional Long Short-Term Memory network (CLSTM) that is end-to-end trainable. The glimpse sensor module was built inside the attention-aware FCNN to discard majority of background by focusing on a region containing the object of interest. FCNN extracted discriminating spatial features of glimpse to facilitate temporal modeling for CLSTM. The saliency mask computed from CLSTM refined the features particular to the tracked landmarks. Moreover, the multi-task loss strategy including bounding box loss, localization loss, saliency loss, and adaptive loss weighting term was utilized to facilitate training convergence and avoid over/under-fitting. The tracker was tested on the databases provided by MICCAI 2015 challenges, and the ground truth data was obtained with the help of brute force-based template matching technology.RESULTS: The mean tracking error of 0.97 ± 0.52 mm and maximum tracking error of 1.94 mm were observed for 85 point-landmarks across 39 ultrasound cases compared to the ground truth annotations. The tracking speed per frame per landmark with the GPU implementation ranged from 66 and 101 frames per second, which largely exceeded the ultrasound imaging rate.CONCLUSION: The results demonstrated the robustness and accuracy of the proposed deep learning-based motion estimation, despite of the existence of some known shortcomings of ultrasound imaging such as speckle noise. The tracking speed of the system was found to be remarkable, sufficiently fast for real-time applications in RT environment. The approach provides a valuable tool to guide RT treatment with beam gating or multi leaf collimator (MLC) tracking in real time. This article is protected by copyright. All rights reserved.
View details for PubMedID 30912590
To develop and implement an automated plan check (APC) tool using a Six Sigma methodology with the aim of improving safety and efficiency in external beam radiotherapy.The Six Sigma define-measure-analyze-improve-control (DMAIC) framework was used by measuring defects stemming from treatment planning that were reported to the departmental incidence learning system (ILS). The common error pathways observed in the reported data were combined with our departmental physics plan check list, and AAPM TG-275 identified items. Prioritized by risk priority number (RPN) and severity values, the check items were added to the APC tool developed using Varian Eclipse Scripting Application Programming Interface (ESAPI). At 9 months post-APC implementation, the tool encompassed 89 check items, and its effectiveness was evaluated by comparing RPN values and rates of reported errors. To test the efficiency gains, physics plan check time and reported error rate were prospectively compared for 20 treatment plans.The APC tool was successfully implemented for external beam plan checking. FMEA RPN ranking re-evaluation at 9 months post-APC demonstrated a statistically significant average decrease in RPN values from 129.2 to 83.7 (P < .05). After the introduction of APC, the average frequency of reported treatment-planning errors was reduced from 16.1% to 4.1%. For high-severity errors, the reduction was 82.7% for prescription/plan mismatches and 84.4% for incorrect shift note. The process shifted from 4σ to 5σ quality for isocenter-shift errors. The efficiency study showed a statistically significant decrease in plan check time (10.1 ± 7.3 min, P = .005) and decrease in errors propagating to physics plan check (80%).Incorporation of APC tool has significantly reduced the error rate. The DMAIC framework can provide an iterative and robust workflow to improve the efficiency and quality of treatment planning procedure enabling a safer radiotherapy process.
View details for DOI 10.1002/acm2.12678
View details for PubMedID 31423729
PURPOSE: Few features characterizing the dosimetric properties of the patients are included in currently available dose-volume histogram (DVH) prediction models, making it intractable to build a correlative relationship between the input and output parameters. Here we use PTV-only treatment plans of the patients (i.e., the achievable dose distribution in the absence of organs-at-risks (OARs) constraints) to estimate the potentially achievable quality of treatment plans and establish a machine learning-based DVH prediction framework with the use of the dosimetric metric as model input parameters.METHODS: A support vector regression (SVR) approach was used as the backbone of our machine learning model. A database containing volumetric modulated arc therapy (VMAT) plans of 63 prostate cancer patients were used. For each patient, the PTV-only plan was generated first. A correlative relationship between the OAR DVH of the PTV-only plan (model input) and the corresponding DVH of the clinical treatment plan (CTP) (model output) was then established with the 53 training cases. The prediction model was tested by the validation cohort of 10 cases.RESULTS: For the training cohort, the checks of dosimetric endpoints (DEs) indicated that 52 out of 53 plans (98%) were within 10% error bound for bladder, and 45 out of 53 plans (85%) were within 10% error bound for rectum. In the validation tests, 92% and 96% of the DEs were within the 10% error bounds for bladder and rectum respectively, and 8 out of 10 validation plans (80%) were within 10% error bound for both bladder and rectum. The sum of absolute residuals (SAR) achieved mean 0.034 ± 0.028 and 0.046 ± 0.021 for the bladder and rectum, respectively.CONCLUSIONS: A novel dosimetric features-driven machine learning model with the use of PTV-only plan has been established for DVH prediction. The framework is capable of efficiently generating best achievable DVHs for VMAT planning. This article is protected by copyright. All rights reserved.
View details for PubMedID 30536442
Here, we present the case of a pediatric patient with newly diagnosed hepatocellular carcinoma causing central biliary obstruction and persistently elevated bilirubin of 3.0-4.3 mg/dl despite placement of bilateral internal-external biliary drains. The tumor was not resectable, and the patient was not a candidate for liver transplant due to nodal disease, for chemotherapy due to hyperbilirubinemia, or for local therapies aside from stereotactic body radiotherapy (SBRT). In this report, we discuss the successful use of SBRT in the management of this patient, and its role in allowing the patient to become a candidate for additional therapies.
View details for DOI 10.1002/pbc.26330
View details for PubMedID 28436210
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