Early detection of potential errors during patient treatment planning

Development and benchmarking diffusion magnetic resonance imaging analysis for integration into radiation treatment planning

PURPOSE

The rising promise in the utility of advanced multi-parametric magnetic resonance (MR) imaging in radiotherapy (RT) treatment planning creates a necessity for testing and enhancing the accuracy of quantitative imaging analysis. Standardizing the analysis of diffusion weighted imaging (DWI) and diffusion tensor imaging (DTI) to generate meaningful and reproducible apparent diffusion coefficient (ADC) and fractional anisotropy (FA) lays the requisite needed for clinical integration. The aim of the demonstrated work is to benchmark the generation of the ADC and FA parametric map analyses using integrated tools in a commercial treatment planning system against the currently used ones.

METHODS

Three software packages were used for generating ADC and FA maps in this study; one tool was developed within a commercial treatment planning system, another by the Functional Magnetic Resonance Imaging of the Brain (FMRIB) Software Library FSL (Analysis Group, FMRIB, Oxford, United Kingdom), and an in-house tool developed at the M.D. Anderson Cancer Center. The ADC and FA maps generated by all three packages for 35 subjects were subtracted from one another, and the standard deviation of the images' differences was used to compare the reproducibility. The reproducibility of the ADC maps was compared with the Quantitative Imaging Biomarkers Alliance (QIBA) protocol, while that of the FA maps was compared to data in published literature.

RESULTS

Results show that the discrepancies between the ADC maps calculated for each patient using the three different software algorithms are less than 2% which meets the 3.6% recommended QIBA requirement. Except for a small number of isolated points, the majority of differences in FA maps for each patient produced by the three methods did not exceed 0.02 which is 10 times lower than the differences seen in healthy gray and white matter. The results were also compared to the maps generated by existing MR Imaging consoles and showed that the robustness of console generated ADC and FA maps is largely dependent on the correct application of scaling factors, that only if correctly placed; the differences between the three tested methods and the console generated values were within the recommended QIBA guidelines.

CONCLUSIONS

Cross-comparison difference maps demonstrated that quantitative reproducibility of ADC and FA metrics generated using our tested commercial treatment planning system were comparable to in-house and established tools as benchmarks. This integrated approach facilitates the clinical utility of diffusion imaging in radiation treatment planning workflow.

Automating Electronic Health Record Data Quality Assessment

Information systems such as Electronic Health Record (EHR) systems are susceptible to data quality (DQ) issues. Given the growing importance of EHR data, there is an increasing demand for strategies and tools to help ensure that available data are fit for use. However, developing reliable data quality assessment (DQA) tools necessary for guiding and evaluating improvement efforts has remained a fundamental challenge. This review examines the state of research on operationalising EHR DQA, mainly automated tooling, and highlights necessary considerations for future implementations. We reviewed 1841 articles from PubMed, Web of Science, and Scopus published between 2011 and 2021. 23 DQA programs deployed in real-world settings to assess EHR data quality (n = 14), and a few experimental prototypes (n = 9), were identified. Many of these programs investigate completeness (n = 15) and value conformance (n = 12) quality dimensions and are backed by knowledge items gathered from domain experts (n = 9), literature reviews and existing DQ measurements (n = 3). A few DQA programs also explore the feasibility of using data-driven techniques to assess EHR data quality automatically. Overall, the automation of EHR DQA is gaining traction, but current efforts are fragmented and not backed by relevant theory. Existing programs also vary in scope, type of data supported, and how measurements are sourced. There is a need to standardise programs for assessing EHR data quality, as current evidence suggests their quality may be unknown.

Errors detected during physics plan review for external beam radiotherapy

Background and purpose

Risk management in radiotherapy is of high importance. There is not much data published on errors occurring in the treatment planning process of external beam techniques. The aim of this study was to investigate errors occurring during physics plan review in external beam radiotherapy.

Materials and methods

Over a period of 14 months errors observed during the physical review process are reported. The errors were grouped and evaluated regarding treatment machine, technique, and treatment site. In addition, a correlation between frequency of errors and staff shortage was analyzed.

Results

Subgroups of grave errors (g-errors) and slight errors (s-errors) were defined to consider the different impact on the patient and clinical workflow of the errors. In 1056 plans reviewed, 110 errors (41 g-errors, 69 s-errors) were detected. The most common g-errors and s-errors were “Wrong gantry angle at setup field” (n = 19) and “Wrong field label” (n = 24), respectively. A correlation of number of errors and treatment machine, technique, or anatomical site could not be found. No correlation between staff shortage and number of errors was observed.

Conclusions

The process of reviewing treatment plans is a relevant topic to consider in risk analysis of the radiotherapy workflow. The review process could be improved by enhancements in the treatment planning systems, use of digital dose prescription, and treatment planning templates.

Toward automation of initial chart check for photon/electron EBRT: the clinical implementation of new AAPM task group reports and automation techniques

Purpose

The recently published AAPM TG‐275 and the public review version of TG‐315 list new recommendations for comprehensive and minimum physics initial chart checks, respectively. This article addresses the potential development and benefit of initial chart check automation when these recommendations are implemented for clinical photon/electron EBRT.

Methods

Eight board‐certified physicists with 2–20 years of clinical experience performed initial chart checks using checklists from TG‐275 and TG‐315. Manual check times were estimated for three types of plans (IMRT/VMAT, 3D, and 2D) and for prostate, whole pelvis, lung, breast, head and neck, and brain cancers. An expert development team of three physicists re‐evaluated the automation feasibility of TG‐275 checklist based on their experience of developing and implementing the in‐house and the commercial automation tools in our institution. Three levels of initial chart check automation were simulated: (1) Auto_UMMS_tool (which consists of in‐house program and commercially available software); (2) Auto_TG275 (with full and partial automation as indicated in TG‐275); and (3) Auto_UMMS_exp (with full and partial automation as determined by our experts’ re‐evaluation).

Results

With no automation of initial chart checks, the ranges of manual check times were 29–56 min (full TG‐315 list) and 102–163 min (full TG‐275 list), which varied significantly with physicists but varied little at different tumor sites. The 69 of 71 checks which were considered as “not fully automated” in TG‐275 were re‐evaluated with more automation feasibility. Compared to no automation, the higher levels of automation yielded a great reduction in both manual check times (by 44%–98%) and potentially residual detectable errors (by 15–85%).

Conclusion

The initial chart check automation greatly improves the practicality and efficiency of implementing the new TG recommendations. Revisiting the TG reports with new technology/practice updates may help develop and utilize more automation clinically.

The Fusion of Incident Learning and Failure Mode and Effects Analysis for Data-Driven Patient Safety Improvements

PURPOSE

Incident learning is a critical part of the quality improvement process for all radiation therapy clinics. Failure mode and effects analysis has also been adopted as a hazard analysis method within the field of radiation oncology based on the recommendations of American Association of Physicists in Medicine Task Group 100. In this work, we demonstrate a fusion of these techniques that is efficient and transferrable to all types of clinics and that allows data-driven targeting of the highest risk error types.

METHODS AND MATERIALS

Four clinical physicists recorded safety events detected during physics treatment plan quality assurance over a 27-month period. Events were sorted into the broad categories of either a documentation or plan construction error. Events were further stratified into subcategories until sufficiently discriminated against for analysis. Event risks were quantified using reduced-resolution TG-100 severity scores combined with observed occurrence rates. The highest risk categories were examined for intervention strategies.

RESULTS

A total of 871 events were identified over the study period. Of these, 652 (74.9%) were classified as low severity, 178 (20.4%) as medium severity, and 41 (4.7%) as high severity. Four of the top 5 ranked categories could be targeted by a preplanning chart rounds. Several of the categories could be targeted by additional automation in the planning and QA processes.

CONCLUSIONS

The retrospective classification and risk analysis of safety events allows clinics to design targeted workflow and quality assurance changes aimed at reducing the occurrence of high-risk events. The method presented here leverages incident learning efforts that many clinics are already performing, allows the severity of events to be efficiently assigned, and generates actionable results without requiring a complete failure mode and effects analysis.

Treatment planning for proton therapy: what is needed in the next 10 years?

Treatment planning is the process where the prescription of the radiation oncologist is translated into a deliverable treatment. With the complexity of contemporary radiotherapy, treatment planning cannot be performed without a computerized treatment planning system. Proton therapy (PT) enables highly conformal treatment plans with a minimum of dose to tissues outside the target volume, but to obtain the most optimal plan for the treatment, there are a multitude of parameters that need to be addressed. In this review areas of ongoing improvements and research in the field of PT treatment planning are identified and discussed. The main focus is on issues of immediate clinical and practical relevance to the PT community highlighting the needs for the near future but also in a longer perspective. We anticipate that the manual tasks performed by treatment planners in the future will involve a high degree of computational thinking, as many issues can be solved much better by e.g. scripting. More accurate and faster dose calculation algorithms are needed, automation for contouring and planning is required and practical tools to handle the variable biological efficiency in PT is urgently demanded just to mention a few of the expected improvements over the coming 10 years.