End-to-end test for fractionated online adaptive MR-guided radiotherapy using a deformable anthropomorphic pelvis phantom

2021 ◽  
Author(s):  
Alina Elter ◽  
Carolin Rippke ◽  
Wibke Johnen ◽  
Philipp Mann ◽  
Emily Hellwich ◽  
...  
Keyword(s):  
Treatment Plan ◽  
Clinical Importance ◽  
Polymer Gel ◽  
Cancer Treatments ◽  
Passing Rates ◽  
End To End ◽  
Pre Treatment ◽  
Prostate Cancer Treatments ◽  
Treatment Procedures ◽  
Dose Measurements

Abstract Objective: In MR-guided radiotherapy (MRgRT) for prostate cancer treatments inter-fractional anatomy changes such as bladder and rectum fillings may be corrected by an online adaption of the treatment plan. To clinically implement such complex treatment procedures, however, specific end-to-end tests are required that are able to validate the overall accuracy of all treatment steps from pre-treatment imaging to dose delivery. Approach: In this study, an end-to-end test of a fractionated and online adapted MRgRT prostate irradiation was performed using the so-called ADAM-PETer phantom. The phantom was adapted to perform 3D polymer gel (PG) dosimetry in the prostate and rectum. Furthermore, thermoluminescence detectors (TLDs) were placed at the center and on the surface of the prostate for additional dose measurements as well as for an external dose renormalization of the PG. For the end-to-end test, a total of five online adapted irradiations were applied in sequence with different bladder and rectum fillings, respectively. Main results: A good agreement of measured and planned dose was found represented by high γ-index passing rates (3 %⁄ 3 mm criterion) of the PG evaluation of 98.9 % in the prostate and 93.7 % in the rectum. TLDs used for PG renormalization at the center of the prostate showed a deviation of -2.3 %. Significance: The presented end-to-end test, which allows for 3D dose verification in the prostate and rectum, demonstrates the feasibility and accuracy of fractionated and online-adapted prostate irradiations in presence of inter-fractional anatomy changes. Such tests are of high clinical importance for the commissioning of new image-guided treatment procedures such as online adaptive MRgRT.

Anomaly Detection in Healthcare: Detecting Erroneous Treatment Plans in Time Series Radiotherapy Data

2014 ◽  
Vol 08 (03) ◽  
pp. 257-278 ◽  
Author(s):  
Tamara Sipes ◽  
Steve Jiang ◽  
Kevin Moore ◽  
Nan Li ◽  
Homa Karimabadi ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Medical Errors ◽  
Predictive Analytics ◽  
Treatment Plan ◽  
Cancer Treatments ◽  
Treatment Data ◽  
Treatment Plans ◽  
New Treatment ◽  
Prostate Cancer Treatments ◽  
Radiotherapy Treatment

Adverse events in healthcare and medical errors result in thousands of accidental deaths and over one million excess injuries each year. Anomaly detection in medicine is an important task, especially in the area of radiation oncology where errors are very rare, but can be extremely dangerous, and even deadly. To avoid medical errors in radiation cancer treatment, careful attention needs to be made to ensure accurate implementation of the intended treatment plan. In this paper, we describe the work that resulted in a valuable predictive analytics tool for automatic detection of catastrophic errors in cancer radiotherapy, adding an important safeguard for patient safety. We designed a method for Dynamic Modeling and Prediction of Radiotherapy Treatment Deviations from Intended Plans (SmartTool) to automatically detect and highlight potential errors in a radiotherapy treatment plan, based on the data from several thousand prostate cancer treatments that were used to build the model. SmartTool determines if the treatment parameters are valid, against a previously built Predictive Model of a Medical Error (PMME). SmartTool communicates with a radiotherapy treatment management system, checking all the treatment parameters in the background prior to execution, and after the human expert QA is completed. Any anomalous treatment parameters are detected using an innovative intelligent algorithm in a completely automatic and unsupervised manner, and it flags the operator by highlighting the suspect parameter(s) for human intervention. Furthermore, the system is self-learning and constantly evolving, and the model is dynamically updated with the new treatment data.

scholarly journals The “black box” of treatment: Patients’ perspective on what works in opioid maintenance treatment for opioid dependence

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Teresa C. Silva ◽  
Fredrik B. Andersson
Keyword(s):  
Opioid Dependence ◽  
Maintenance Treatment ◽  
Treatment Plan ◽  
Black Box ◽  
Opioid Maintenance Treatment ◽  
Convenience Sample ◽  
Problematic Use ◽  
Treatment Stage ◽  
Pre Treatment ◽  
Patients Perspective

Abstract Background A lack of conceptual modeling of how the components of opioid maintenance treatment (OMT) for opioid dependence (OD) work causes it to occasionally be labeled the “black-box” of treatment. This study had a two-fold objective: First, to analyze which factors related to OMT for OD contribute to the abstinence of problematic use of non-prescribed opioids and sustain recovery, from the patients’ perspective; second, to understand which changes OMT produced in the individuals’ lives might significantly contribute to relapse prevention. Methods We used qualitative methods of design, inquiry, and analysis from a convenience sample of 19 individuals in a Swedish treatment setting. Results All the participants reported previous cycles of problematic use of non-prescribed opioids and other non-prescribed psychoactive substances, treatment, abstinence, recovery, and relapse before starting the current OMT program. During the pre-treatment stage, specific events, internal processes, and social environments enhanced motivation toward abstinence and seeking treatment. During the treatment stage, participants perceived the quality of the human relationships established with primary social groups as important as medication and the individual plan of care in sustaining recovery. From the participants’ perspective, OMT was a turning point in their life course, allowing them a sense of self-fulfillment and the reconstruction of personal and social identity. However, they still struggled with the stigmatization produced by a society that values abstinence-oriented over medication-assisted treatments. Conclusion OMT is not an isolated event in individuals’ lives but rather a process occurring within a specific social context. Structural factors and the sense of acceptance and belonging are essential in supporting the transformation. Treatment achievements and the risk for relapse vary over time, so the objectives of the treatment plan must account for characteristics of the pre-treatment stage and the availability and capacity of individuals to restructure their social network, besides the opioid maintenance treatment and institutional social care.

scholarly journals End-to-End, Pixel-Wise Vessel-Specific Coronary and Aortic Calcium Detection and Scoring Using Deep Learning

Diagnostics ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 215
Author(s):  
Gurpreet Singh ◽  
Subhi Al’Aref ◽  
Benjamin Lee ◽  
Jing Lee ◽  
Swee Tan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Coronary Artery ◽  
Clinical Importance ◽  
Learning Model ◽  
Mean Difference ◽  
Interquartile Range ◽  
History Of ◽  
End To End ◽  
Artery Disease ◽  
Deep Learning Model

Conventional scoring and identification methods for coronary artery calcium (CAC) and aortic calcium (AC) result in information loss from the original image and can be time-consuming. In this study, we sought to demonstrate an end-to-end deep learning model as an alternative to the conventional methods. Scans of 377 patients with no history of coronary artery disease (CAD) were obtained and annotated. A deep learning model was trained, tested and validated in a 60:20:20 split. Within the cohort, mean age was 64.2 ± 9.8 years, and 33% were female. Left anterior descending, right coronary artery, left circumflex, triple vessel, and aortic calcifications were present in 74.87%, 55.82%, 57.41%, 46.03%, and 85.41% of patients respectively. An overall Dice score of 0.952 (interquartile range 0.921, 0.981) was achieved. Stratified by subgroups, there was no difference between male (0.948, interquartile range 0.920, 0.981) and female (0.965, interquartile range 0.933, 0.980) patients (p = 0.350), or, between age <65 (0.950, interquartile range 0.913, 0.981) and age ≥65 (0.957, interquartile range 0.930, 0.9778) (p = 0.742). There was good correlation and agreement for CAC prediction (rho = 0.876, p < 0.001), with a mean difference of 11.2% (p = 0.100). AC correlated well (rho = 0.947, p < 0.001), with a mean difference of 9% (p = 0.070). Automated segmentation took approximately 4 s per patient. Taken together, the deep-end learning model was able to robustly identify vessel-specific CAC and AC with high accuracy, and predict Agatston scores that correlated well with manual annotation, facilitating application into areas of research and clinical importance.

scholarly journals Causal inference in continuous time: an example on prostate cancer therapy

Biostatistics ◽  
2018 ◽  
Vol 21 (1) ◽  
pp. 172-185 ◽  
Author(s):  
Pål Christie Ryalen ◽  
Mats Julius Stensrud ◽  
Sophie Fosså ◽  
Kjetil Røysland
Keyword(s):  
Prostate Cancer ◽  
Continuous Time ◽  
Causal Effect ◽  
Structural Models ◽  
Marginal Structural Models ◽  
Cancer Treatments ◽  
Discrete Parameter ◽  
Inverse Probability Weights ◽  
Using Data ◽  
Prostate Cancer Treatments

Abstract In marginal structural models (MSMs), time is traditionally treated as a discrete parameter. In survival analysis on the other hand, we study processes that develop in continuous time. Therefore, Røysland (2011. A martingale approach to continuous-time marginal structural models. Bernoulli 17, 895–915) developed the continuous-time MSMs, along with continuous-time weights. The continuous-time weights are conceptually similar to the inverse probability weights that are used in discrete time MSMs. Here, we demonstrate that continuous-time MSMs may be used in practice. First, we briefly describe the causal model assumptions using counting process notation, and we suggest how causal effect estimates can be derived by calculating continuous-time weights. Then, we describe how additive hazard models can be used to find such effect estimates. Finally, we apply this strategy to compare medium to long-term differences between the two prostate cancer treatments radical prostatectomy and radiation therapy, using data from the Norwegian Cancer Registry. In contrast to the results of a naive analysis, we find that the marginal cumulative incidence of treatment failure is similar between the strategies, accounting for the competing risk of other death.

scholarly journals An Integrated Framework Based on Full Monte Carlo Simulations for Double-Scattering Proton Therapy

2019 ◽  
Vol 6 (2) ◽  
pp. 31-41
Author(s):  
Jiankui Yuan ◽  
David Mansur ◽  
Min Yao ◽  
Tithi Biswas ◽  
Yiran Zheng ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Proton Therapy ◽  
Treatment Plan ◽  
Planning System ◽  
Treatment Planning System ◽  
Patient Specific ◽  
Double Scattering ◽  
Integrated Framework ◽  
End To End ◽  
Clinical Cases

ABSTRACT Purpose: We developed an integrated framework that employs a full Monte Carlo (MC) model for treatment-plan simulations of a passive double-scattering proton system. Materials and Methods: We have previously validated a virtual machine source model for full MC proton-dose calculations by comparing the percentage of depth-dose curves, spread-out Bragg peaks, and lateral profiles against measured commissioning data. This study further expanded our previous work by developing an integrate framework that facilitates its clinical use. Specifically, we have (1) constructed patient-specific applicator and compensator numerically from the plan data and incorporated them into the beamline, (2) created the patient anatomy from the computed tomography image and established the transformation between patient and machine coordinate systems, and (3) developed a graphical user interface to ease the whole process from importing the treatment plan in the Digital Imaging and Communications in Medicine format to parallelization of the MC calculations. End-to-end tests were performed to validate the functionality, and 3 clinical cases were used to demonstrate clinical utility of the framework. Results: The end-to-end tests demonstrated that the framework functioned correctly for all tested functionality. Comparisons between the treatment planning system calculations and MC results in 3 clinical cases revealed large dose difference up to 17%, especially in the beam penumbra and near the end of beam range. The discrepancy likely originates from a variety of sources, such as the dose algorithms, modeling of the beamline, and the dose metric. The agreement for other regions was acceptable. Conclusion: An integrated framework was developed for full MC simulations of double-scattering proton therapy. It can be a valuable tool for dose verification and plan evaluation.

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