VA-Radiation Oncology Quality Surveillance Program

Consensus Quality Measures and Dose Constraints for Lung Cancer From the Veterans Affairs Radiation Oncology Quality Surveillance Program and ASTRO Expert Panel

PURPOSE

For patients with lung cancer, it is critical to provide evidence-based radiation therapy to ensure high-quality care. The US Department of Veterans Affairs (VA) National Radiation Oncology Program partnered with the American Society for Radiation Oncology (ASTRO) as part of the VA Radiation Oncology Quality Surveillance to develop lung cancer quality metrics and assess quality of care as a pilot program in 2016. This article presents recently updated consensus quality measures and dose-volume histogram (DVH) constraints.

METHODS AND MATERIALS

A series of measures and performance standards were reviewed and developed by a Blue-Ribbon Panel of lung cancer experts in conjunction with ASTRO in 2022. As part of this initiative, quality, surveillance, and aspirational metrics were developed for (1) initial consultation and workup; (2) simulation, treatment planning, and treatment delivery; and (3) follow-up. The DVH metrics for target and organ-at-risk treatment planning dose constraints were also reviewed and defined.

RESULTS

Altogether, a total of 19 lung cancer quality metrics were developed. There were 121 DVH constraints developed for various fractionation regimens, including ultrahypofractionated (1, 3, 4, or 5 fractions), hypofractionated (10 and 15 fractionations), and conventional fractionation (30-35 fractions).

CONCLUSIONS

The devised measures will be implemented for quality surveillance for veterans both inside and outside of the VA system and will provide a resource for lung cancer-specific quality metrics. The recommended DVH constraints serve as a unique, comprehensive resource for evidence- and expert consensus-based constraints across multiple fractionation schemas.

Consensus Quality Measures and Dose Constraints for Prostate Cancer From the Veterans Affairs Radiation Oncology Quality Surveillance Program and American Society for Radiation Oncology Expert Panel

PURPOSE

There are no agreed upon measures to comprehensively determine the quality of radiation oncology (RO) care delivered for prostate cancer. Consequently, it is difficult to assess the implementation of scientific advances and adherence to best practices in routine clinical practice. To address this need, the US Department of Veterans Affairs (VA) National Radiation Oncology Program established the VA Radiation Oncology Quality Surveillance (VA ROQS) Program to develop clinical quality measures to assess the quality of RO care delivered to Veterans with cancer. This article reports the prostate cancer consensus measures.

METHODS AND MATERIALS

The VA ROQS Program contracted with the American Society for Radiation Oncology to commission a Blue Ribbon Panel of prostate cancer experts to develop a set of evidence-based measures and performance expectations. From February to June 2021, the panel developed quality, aspirational, and surveillance measures for (1) initial consultation and workup, (2) simulation, treatment planning, and delivery, and (3) follow-up. Dose-volume histogram (DVH) constraints to be used as quality measures for definitive and post-prostatectomy radiation therapy were selected. The panel also identified the optimal Common Terminology Criteria for Adverse Events, version 5.0 (CTCAE V5.0), toxicity terms to assess in follow-up.

RESULTS

Eighteen prostate-specific measures were developed (13 quality, 2 aspirational, and 3 surveillance). DVH metrics tailored to conventional, moderately hypofractionated, and ultrahypofractionated regimens were identified. Decision trees to determine performance for each measure were developed. Eighteen CTCAE V5.0 terms were selected in the sexual, urinary, and gastrointestinal domains as highest priority for assessment during follow-up.

CONCLUSIONS

This set of measures and DVH constraints serves as a tool for assessing the comprehensive quality of RO care for prostate cancer. These measures will be used for ongoing quality surveillance and improvement among veterans receiving care across VA and community sites. These measures can also be applied to clinical settings outside of those serving veterans.

Association between adherence to radiation therapy quality metrics and patient reported outcomes in prostate cancer

BACKGROUND

Prior studies have shown significant variability in the quality of prostate cancer care in the US with questionable associations between quality measures and patient reported outcomes. We evaluated the impact of compliance with nationally recognized radiation therapy (RT) quality measures on patient-reported health-related quality of life (HRQOL) outcomes in the Comparative Effectiveness Analysis of Surgery and Radiation (CEASAR) cohort.

METHODS

CEASAR is a population-based, prospective cohort study of men with localized prostate cancer from which we identified 649 who received primary RT and completed HRQOL surveys for inclusion. Eight quality measures were identified based on national guidelines. We analyzed the impact of compliance with these measures on HRQOL assessed by the 26-item Expanded Prostate Index Composite at pre-specified intervals up to 5 years after treatment. Multivariable analysis was performed controlling for demographic and clinicopathologic features.

RESULTS

Among eligible participants, 566 (87%) patients received external beam radiation therapy and 83 (13%) received brachytherapy. Median age was 69 years (interquartile range: 64-73), 33% had low-, 43% intermediate-, and 23% high-risk disease. 28% received care non-compliant with at least one measure. In multivariable analyses, while some statistically significant associations were identified, there were no clinically significant associations between compliance with evaluated RT quality measures and patient reported urinary irritative, urinary incontinence, bowel, sexual or hormonal function.

CONCLUSIONS

Compliance with RT quality measures was not meaningfully associated with patient-reported outcomes after prostate cancer treatment. Further work is needed to identify patient-centered quality measures of prostate cancer care.

Consensus Quality Measures and Dose Constraints for Head and Neck Cancer with an emphasis on Oropharyngeal and Laryngeal Cancer from the Veterans Affairs Radiation Oncology Quality Surveillance Program and American Society for Radiation Oncology Expert Panel

PURPOSE

Safeguarding high-quality care using evidence-based radiation therapy for patients with head and neck cancer is crucial to improving oncologic outcomes, including survival and quality of life.

METHODS AND MATERIALS

The Veterans Administration (VA) National Radiation Oncology Program established the VA Radiation Oncology Quality Surveillance Program (VAROQS) to develop clinical quality measures (QM) in head and neck cancer. As part of the development of QM, the VA commissioned, along with the American Society for Radiation Oncology, a blue-ribbon panel comprising experts in head and neck cancer, to develop QM.

RESULTS

We describe the methods used to develop QM and the final consensus QM, as well as aspirational and surveillance QM, which capture all aspects of the continuum of patient care from initial patient work-up, radiation treatment planning and delivery, and follow-up care, as well as dose volume constraints.

CONCLUSION

These QM are intended for use as part of ongoing quality surveillance for veterans receiving radiation therapy throughout the VA as well as outside the VA. They may also be used by the non-VA community as a basic measure of quality care for head and neck cancer patients receiving radiation.

Consensus Quality Measures and Dose Constraints for Rectal Cancer From the Veterans Affairs Radiation Oncology Quality Surveillance Program and American Society for Radiation Oncology (ASTRO) Expert Panel

PURPOSE

Ensuring high quality, evidence-based radiation therapy for patients with cancer is of the upmost importance. To address this need, the Veterans Affairs (VA) Radiation Oncology Program partnered with the American Society for Radiation Oncology and established the VA Radiation Oncology Quality Surveillance program. As part of this ongoing effort to provide the highest quality of care for patients with rectal cancer, a blue-ribbon panel comprised of rectal cancer experts was formed to develop clinical quality measures.

METHODS AND MATERIALS

The Rectal Cancer Blue Ribbon panel developed quality, surveillance, and aspirational measures for (a) initial consultation and workup, (b) simulation, treatment planning, and treatment, and (c) follow-up. Twenty-two rectal cancer specific measures were developed (19 quality, 1 aspirational, and 2 surveillance). In addition, dose-volume histogram constraints for conventional and hypofractionated radiation therapy were created.

CONCLUSIONS

The quality measures and dose-volume histogram for rectal cancer serves as a guideline to assess the quality of care for patients with rectal cancer receiving radiation therapy. These quality measures will be used for quality surveillance for veterans receiving care both inside and outside the VA system to improve the quality of care for these patients.

Veterans Affairs Radiation Oncology Quality Surveillance Program and American Society for Radiation Oncology Quality Measures Initiative

INTRODUCTION

Ensuring high quality, evidence-based radiation therapy for patients is of the upmost importance. As a part of the largest integrated health system in America, the Department of Veterans Affairs National Radiation Oncology Program (VA-NROP) established a quality surveillance initiative to address the challenge and necessity of providing the highest quality of care for veterans treated for cancer.

METHODS

As part of this initiative, the VA-NROP contracted with the American Society for Radiation Oncology (ASTRO) to commission five Blue-Ribbon Panels for lung, prostate, rectal, breast, and head & neck cancers experts. This group worked collaboratively with the VA-NROP to develop consensus quality measures. In addition to the site-specific measures, an additional Blue-Ribbon Panel comprised of the chairs and other members of the disease sites was formed to create 18 harmonized quality measures for all five sites (13 quality, 4 surveillance, and 1 aspirational).

CONCLUSION

The VA-NROP and ASTRO collaboration have created quality measures spanning five disease sites to help improve patient outcomes. These will be used for the ongoing quality surveillance of veterans receiving radiation therapy through the VA and its community partners.

ETHICS BOARD APPROVAL

N/A - No human subjects were required.

Quality indicators for radiation oncology

Quality Indicators, based on clinical practice guidelines, have been used in medicine and within oncology to measure quality of care for over twenty years. However, radiation oncology quality indicators are sparse. This article describes the background to the development of current national and international, general and tumour site‐specific radiation oncology quality indicators in use. We explore challenges and opportunities to expand their routine prospective collection and feedback to help drive improvements in the quality of care received by people undergoing radiation therapy.

Automated data abstraction for quality surveillance and outcome assessment in radiation oncology

Rigorous radiotherapy quality surveillance and comprehensive outcome assessment require electronic capture and automatic abstraction of clinical, radiation treatment planning, and delivery data. We present the design and implementation framework of an integrated data abstraction, aggregation, and storage, curation, and analytics software: the Health Information Gateway and Exchange (HINGE), which collates data for cancer patients receiving radiotherapy. The HINGE software abstracts structured DICOM‐RT data from the treatment planning system (TPS), treatment data from the treatment management system (TMS), and clinical data from the electronic health records (EHRs). HINGE software has disease site‐specific “Smart” templates that facilitate the entry of relevant clinical information by physicians and clinical staff in a discrete manner as part of the routine clinical documentation. Radiotherapy data abstracted from these disparate sources and the smart templates are processed for quality and outcome assessment. The predictive data analyses are done on using well‐defined clinical and dosimetry quality measures defined by disease site experts in radiation oncology. HINGE application software connects seamlessly to the local IT/medical infrastructure via interfaces and cloud services and performs data extraction and aggregation functions without human intervention. It provides tools to assess variations in radiation oncology practices and outcomes and determines gaps in radiotherapy quality delivered by each provider.

Multi-View Data Integration Methods for Radiotherapy Structure Name Standardization

Simple Summary

Structure names associated with radiotherapy treatments need standardization to develop data pipelines enabling personalized treatment plans. Automatic classification of structure names based on the currently available TG-263 nomenclature can help with data aggregation from both retrospective and future data sources. The aim of our proposed machine learning-based data integration methods is to achieve highly accurate structure name classification to automate the data aggregation process. Our multi-view models can overcome the challenges of integrating different data types associated with radiotherapy structures, such as the physician-given text labels and geometric or image data. The models exhibited high accuracy when tested on multi-center and multi-institutional lung and prostate cancer patients data and outperformed the models built on any single data type. This highlights the importance of combining different types of data in building generalizable models for structure name standardization.

Abstract

Standardization of radiotherapy structure names is essential for developing data-driven personalized radiotherapy treatment plans. Different types of data are associated with radiotherapy structures, such as the physician-given text labels, geometric (image) data, and Dose-Volume Histograms (DVH). Prior work on structure name standardization used just one type of data. We present novel approaches to integrate complementary types (views) of structure data to build better-performing machine learning models. We present two methods, namely (a) intermediate integration and (b) late integration, to combine physician-given textual structure name features and geometric information of structures. The dataset consisted of 709 prostate cancer and 752 lung cancer patients across 40 radiotherapy centers administered by the U.S. Veterans Health Administration (VA) and the Department of Radiation Oncology, Virginia Commonwealth University (VCU). We used randomly selected data from 30 centers for training and ten centers for testing. We also used the VCU data for testing. We observed that the intermediate integration approach outperformed the models with a single view of the dataset, while late integration showed comparable performance with single-view results. Thus, we demonstrate that combining different views (types of data) helps build better models for structure name standardization to enable big data analytics in radiation oncology.

Strategic Initiatives for Veterans with Lung Cancer

Background

Lung cancer is a leading cause of cancer-related mortality among veterans-as well as the US population-despite veterans' access to advanced medical technologies within the Veterans Health Administration (VHA). To improve outcomes, the VHA launched 3 lung cancer treatment initiatives in 2016 and 2017.

Observations

This article summarizes the VHA lung cancer initiatives and discusses future programs aimed to improve care for veterans. The US Department of Veterans Affairs (VA) Partnership to Increase Access to Lung Screening aims to reduce lung cancer mortality among veterans at risk by increasing access to low-dose computed tomography lung screening scans. The VALOR study is a randomized phase 3 clinical trial that evaluates optimal treatment for participants with operable early stage non-small cell lung cancer (NSCLC). This trial plans to enroll veterans with stage I NSCLC who will be randomly assigned to treatment with either surgical lobectomy or stereotactic body radiation therapy. Researchers will follow each participant for at least 5 years to evaluate which treatment, if either, results in a higher overall survival rate. The VA Radiation Oncology Quality Surveillance program compares treatment of veterans with lung cancer in the VHA with quality standards recommended by nationally recognized experts in lung cancer care.

Conclusions

The VHA continues to prioritize resources to improve and assure optimal outcomes for veterans with lung cancer. Future efforts include creating a national network of lung cancer centers of excellence to ensure that treatment decisions for veterans with lung cancer are based on all available molecular information, including data on pharmacogenomic profiles.

A Machine Learning method for relabeling arbitrary DICOM structure sets to TG-263 defined labels

PURPOSE

To present a Machine Learning pipeline for automatically relabeling anatomical structure sets in the Digital Imaging and Communications in Medicine (DICOM) format to a standard nomenclature that will enable data abstraction for research and quality improvement.

METHODS

DICOM structure sets from approximately 1200 lung and prostate cancer patients across 40 treatment centers were used to build predictive models to automate the relabeling of clinically specified structure labels to standardized labels as defined by the American Association of Physics in Medicine's (AAPM) Task Group 263 (TG-263). Volumetric bitmaps were created based on the delineated volumes and were combined with associated bony anatomy data to build feature vectors. Feature reduction was performed with singular value decomposition and the resulting vectors were used for predicting the label of each structure using five different classifier algorithms on the Apache Spark platform with 5-fold cross-validation. Undersampling methods were used to deal with underlying class imbalance that hindered the performance of classifiers. Experiments were performed on both a curated version of the data, which included only annotated structures, and the non-curated data that included all structures from the original treatment plans.

RESULTS

Random Forest provided the highest accuracies with F₁ scores of 98.77 for lung and 95.06 for prostate on the curated data sets. Scores were lower with 95.67 for lung and 90.22 for prostate on the non-curated data sets, highlighting some of the challenges of classifying real clinical data. Including bony anatomy data and pooling information from all structures for the same patient both increased accuracies. In some cases, undersampling with k-Means clustering for class balancing improved classifier accuracy but in all experiments it significantly reduced run time compared to random undersampling.

CONCLUSION

This work shows that structure sets can be relabeled using our approach with accuracies over 95% for many structure types when presented with curated data. Although accuracies dropped when using the full non-curated data sets, some structure types were still correctly labeled over 90% of the time. With similar results obtained on an external test data set, we can infer that the proposed models are likely to work on other clinical data sets.

Integrated Natural Language Processing and Machine Learning Models for Standardizing Radiotherapy Structure Names

The lack of standardized structure names in radiotherapy (RT) data limits interoperability, data sharing, and the ability to perform big data analysis. To standardize radiotherapy structure names, we developed an integrated natural language processing (NLP) and machine learning (ML) based system that can map the physician-given structure names to American Association of Physicists in Medicine (AAPM) Task Group 263 (TG-263) standard names. The dataset consist of 794 prostate and 754 lung cancer patients across the 40 different radiation therapy centers managed by the Veterans Health Administration (VA). Additionally, data from the Radiation Oncology department at Virginia Commonwealth University (VCU) was collected to serve as a test set. Domain experts identified as anatomically significant nine prostate and ten lung organs-at-risk (OAR) structures and manually labeled them according to the TG-263 standards, and remaining structures were labeled as Non_OAR. We experimented with six different classification algorithms and three feature vector methods, and the final model was built with fastText algorithm. Multiple validation techniques are used to assess the robustness of the proposed methodology. The macro-averaged F 1 score was used as the main evaluation metric. The model achieved an F 1 score of 0.97 on prostate structures and 0.99 for lung structures from the VA dataset. The model also performed well on the test (VCU) dataset, achieving an F 1 score of 0.93 for prostate structures and 0.95 on lung structures. In this work, we demonstrate that NLP and ML based approaches can used to standardize the physician-given RT structure names with high fidelity. This standardization can help with big data analytics in the radiation therapy domain using population-derived datasets, including standardization of the treatment planning process, clinical decision support systems, treatment quality improvement programs, and hypothesis-driven clinical research.