Determining the impact of pre-radiation treatment verification simulation/dry run by analyzing intradepartmental reported incidents and surveying staff and patients

Gresswell, Steven; Renz, Paul; Hasan, Shaakir; Werts, Margaret Gessler; Fortunato, Missy; Werts, Day

doi:10.1016/j.prro.2018.05.007

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…In addition to the HCT simulation, all patients also underwent a verification simulation (i.e., a “dry run”) on the treatment machine the day before their first fraction of treatment. This verification session duplicates the steps of patient setup, imaging, and treatment without actually delivering the radiation, which allows staff time to verify the isocenter placement, check geometric clearance and troubleshoot potential problems in an organized team approach 10 . All patients were treated with a 4–5 fractions schedule that delivered over 1–2 weeks.…”

Section: Methodsmentioning

confidence: 99%

“…This verification session duplicates the steps of patient setup, imaging, and treatment without actually delivering the radiation, which allows staff time to verify the isocenter placement, check geometric clearance and troubleshoot potential problems in an organized team approach. 10 All patients were treated with a 4-5 fractions schedule that delivered over 1-2 weeks. 4D CBCT images were acquired for image guidance during the verification session and prior to each treatment delivery using the Elekta XVI Symmetry™ protocol.…”

Section: Patient Selection and Conventional Registration Methodsmentioning

confidence: 99%

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Using previously registered cone beam computerized tomography images to facilitate online computerized tomography to cone beam computerized tomography image registration in lung stereotactic body radiation therapy

Liang

Liu

Grills

et al. 2022

J Applied Clin Med Phys

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Purpose In our conventional image registration workflow, the four‐dimensional (4D) CBCT was directly registered to the reference helical CT (HCT) using a dual registration approach within the Elekta XVI software. In this study, we proposed a new HCT–CBCT auto‐registration strategy using a previously registered CBCT (CBCTpre) as the reference image and tested its clinical feasibility. Methods From a previous CBCT session, the registered average 4D CBCT was selected as CBCTpre and the HCT–CBCTpre registration vector from the clinician's manual registration result was recorded. In the new CBCT session, auto‐registration was performed between the new average 4D CBCT (CBCTtx) and CBCTpre (CBCTpre‐CBCTtx). The overall HCT–CBCTtx registration result was then derived by combing the results from two registrations (i.e., HCT–CBCTpre + CBCTpre–CBCTtx). The results from the proposed method were compared with clinician's manually adjusted HCT–CBCTtx registration results (“ground truth”) to evaluate its accuracy using a test dataset consisting of 32 challenging registration cases. Results The uncertainty of the proposed auto‐registration method was −0.1 ± 0.5, 0.1 ± 1.0, and −0.1 ± 0.7 mm in three translational directions (lateral, longitudinal, and vertical) and 0.0° ± 0.9°, 0.3° ± 0.9°, and 0.4° ± 0.7° in three rotation directions, respectively. Two patients (6.3%) had translational uncertainty > 2 mm (max = 3.1 mm) and both occurred in the longitudinal direction. Meanwhile, the uncertainty of the conventional direct HCT–CBCTtx auto‐registration was −0.4 ± 2.6, −0.2 ± 7.4, −1.4 ± 3.6 mm for translations and −0.3° ± 1.2°, 0.0° ± 1.6°, and 0.1 ± 1.1° for rotations. Eleven patients (34.4%) had translation uncertainty > 2 mm (max = 26.2 mm) in at least one direction. Accuracy in translation was improved with the new method, while rotation accuracy stayed in the same order. Conclusion We demonstrated the feasibility of incorporating prior clinical registration knowledge into the online HCT–CBCT registration process. The proposed auto‐registration method provides a quick and reliable starting solution for online HCT–CBCT registration.

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Section: Methodsmentioning

confidence: 99%

Section: Patient Selection and Conventional Registration Methodsmentioning

confidence: 99%

Using previously registered cone beam computerized tomography images to facilitate online computerized tomography to cone beam computerized tomography image registration in lung stereotactic body radiation therapy

Liang

Liu

Grills

et al. 2022

J Applied Clin Med Phys

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“…This process is performed with the help of computers to simulate the patient's anatomy and cancer location, for both practice and quality assurance purposes [11]. A study conducted in 2018 confirmed that this process resulted in reduction in the rate of incidents during treatment and anxiety levels of both patients and staff during treatment [12]. After patient setup and plan verification, the patient is finally ready to receive radiation treatment.…”

Section: The Radiotherapy Chainmentioning

confidence: 99%

Learning the Treatment Process in Radiotherapy Using an Artificial Intelligence–Assisted Chatbot: Development Study

Rebelo¹,

Sanders²,

Li³

et al. 2022

JMIR Form Res

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Background In knowledge transfer for educational purposes, most cancer hospital or center websites have existing information on cancer health. However, such information is usually a list of topics that are neither interactive nor customized to offer any personal touches to people facing dire health crisis and to attempt to understand the concerns of the users. Patients with cancer, their families, and the general public accessing the information are often in challenging, stressful situations, wanting to access accurate information as efficiently as possible. In addition, there is seldom any comprehensive information specifically on radiotherapy, despite the large number of older patients with cancer, to go through the treatment process. Therefore, having someone with professional knowledge who can listen to them and provide the medical information with good will and encouragement would help patients and families struggling with critical illness, particularly during the lingering pandemic. Objective This study created a novel virtual assistant, a chatbot that can explain the radiation treatment process to stakeholders comprehensively and accurately, in the absence of any similar software. This chatbot was created using the IBM Watson Assistant with artificial intelligence and machine learning features. The chatbot or bot was incorporated into a resource that can be easily accessed by the general public. Methods The radiation treatment process in a cancer hospital or center was described by the radiotherapy process: patient diagnosis, consultation, and prescription; patient positioning, immobilization, and simulation; 3D-imaging for treatment planning; target and organ contouring; radiation treatment planning; patient setup and plan verification; and treatment delivery. The bot was created using IBM Watson (IBM Corp) assistant. The natural language processing feature in the Watson platform allowed the bot to flow through a given conversation structure and recognize how the user responds based on recognition of similar given examples, referred to as intents during development. Therefore, the bot can be trained using the responses received, by recognizing similar responses from the user and analyzing using Watson natural language processing. Results The bot is hosted on a website by the Watson application programming interface. It is capable of guiding the user through the conversation structure and can respond to simple questions and provide resources for requests for information that was not directly programmed into the bot. The bot was tested by potential users, and the overall averages of the identified metrics are excellent. The bot can also acquire users’ feedback for further improvements in the routine update. Conclusions An artificial intelligence–assisted chatbot was created for knowledge transfer regarding radiation treatment process to the patients with cancer, their families, and the general public. The bot that is supported by machine learning was tested, and it was found that the bot can provide information about radiotherapy effectively.

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Unforeseen Computed Tomography Resimulation for Initial Radiation Planning: Associated Factors and Clinical Impact

Metzger

Renz

Hasan

et al. 2019

Advances in Radiation Oncology

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PurposeRepeat computed tomography (CT) simulation is problematic because of additional expense of clinic resources, patient inconvenience, additional radiation exposure, and treatment delay. We investigated the factors and clinical impact of unplanned CT resimulations in our network.Methods and MaterialsWe used the billing records of 18,170 patients treated at 5 clinics. A total of 213 patients were resimulated before their first treatment. The disease site, location, use of 4-dimensional CT (4DCT), contrast, image fusion, and cause for resimulation were recorded. Odds ratios determined statistical significance.ResultsOur total rate of resimulation was 1.2%. Anal/colorectal (P < .001) and head and neck (P < .001) disease sites had higher rates of resimulation. Brain (P = .001) and lung/thorax (P = .008) had lower rates of resimulation. The most common causes for resimulation were setup change (11.7%), change in patient anatomy (9.8%), and rectal filling (8.5%). The resimulation rate for 4DCTs was 3.03% compared with 1.0% for non-4DCTs (P < .001). Median time between simulations was 7 days.ConclusionsThe most common sites for resimulation were anal/colorectal and head and neck, largely because of change in setup or changes in anatomy. The 4DCT technique correlated with higher resimulation rates. The resimulation rate was 1.2%, and median treatment delay was 7 days. Further studies are warranted to limit the rate of resimulation.

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Determining the impact of pre-radiation treatment verification simulation/dry run by analyzing intradepartmental reported incidents and surveying staff and patients

Abstract: A VS helps identify and correct incidents before the administration of radiation therapy and reduces patient anxiety.

Cited by 6 publications

References 12 publications

Using previously registered cone beam computerized tomography images to facilitate online computerized tomography to cone beam computerized tomography image registration in lung stereotactic body radiation therapy

Using previously registered cone beam computerized tomography images to facilitate online computerized tomography to cone beam computerized tomography image registration in lung stereotactic body radiation therapy

Learning the Treatment Process in Radiotherapy Using an Artificial Intelligence–Assisted Chatbot: Development Study

Unforeseen Computed Tomography Resimulation for Initial Radiation Planning: Associated Factors and Clinical Impact

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