Performance/outcomes data and physician process challenges for practical big data efforts in radiation oncology

Infrastructure tools to support an effective Radiation Oncology Learning Health System

PURPOSE

Radiation Oncology Learning Health System (RO-LHS) is a promising approach to improve the quality of care by integrating clinical, dosimetry, treatment delivery, research data in real-time. This paper describes a novel set of tools to support the development of a RO-LHS and the current challenges they can address.

METHODS

We present a knowledge graph-based approach to map radiotherapy data from clinical databases to an ontology-based data repository using FAIR concepts. This strategy ensures that the data are easily discoverable, accessible, and can be used by other clinical decision support systems. It allows for visualization, presentation, and data analyses of valuable information to identify trends and patterns in patient outcomes. We designed a search engine that utilizes ontology-based keyword searching, synonym-based term matching that leverages the hierarchical nature of ontologies to retrieve patient records based on parent and children classes, connects to the Bioportal database for relevant clinical attributes retrieval. To identify similar patients, a method involving text corpus creation and vector embedding models (Word2Vec, Doc2Vec, GloVe, and FastText) are employed, using cosine similarity and distance metrics.

RESULTS

The data pipeline and tool were tested with 1660 patient clinical and dosimetry records resulting in 504 180 RDF (Resource Description Framework) tuples and visualized data relationships using graph-based representations. Patient similarity analysis using embedding models showed that the Word2Vec model had the highest mean cosine similarity, while the GloVe model exhibited more compact embeddings with lower Euclidean and Manhattan distances.

CONCLUSIONS

The framework and tools described support the development of a RO-LHS. By integrating diverse data sources and facilitating data discovery and analysis, they contribute to continuous learning and improvement in patient care. The tools enhance the quality of care by enabling the identification of cohorts, clinical decision support, and the development of clinical studies and machine learning programs in radiation oncology.

An End-to-End Natural Language Processing System for Automatically Extracting Radiation Therapy Events From Clinical Texts

PURPOSE

Real-world evidence for radiation therapy (RT) is limited because it is often documented only in the clinical narrative. We developed a natural language processing system for automated extraction of detailed RT events from text to support clinical phenotyping.

METHODS AND MATERIALS

A multi-institutional data set of 96 clinician notes, 129 North American Association of Central Cancer Registries cancer abstracts, and 270 RT prescriptions from HemOnc.org was used and divided into train, development, and test sets. Documents were annotated for RT events and associated properties: dose, fraction frequency, fraction number, date, treatment site, and boost. Named entity recognition models for properties were developed by fine-tuning BioClinicalBERT and RoBERTa transformer models. A multiclass RoBERTa-based relation extraction model was developed to link each dose mention with each property in the same event. Models were combined with symbolic rules to create a hybrid end-to-end pipeline for comprehensive RT event extraction.

RESULTS

Named entity recognition models were evaluated on the held-out test set with F1 results of 0.96, 0.88, 0.94, 0.88, 0.67, and 0.94 for dose, fraction frequency, fraction number, date, treatment site, and boost, respectively. The relation model achieved an average F1 of 0.86 when the input was gold-labeled entities. The end-to-end system F1 result was 0.81. The end-to-end system performed best on North American Association of Central Cancer Registries abstracts (average F1 0.90), which are mostly copy-paste content from clinician notes.

CONCLUSIONS

We developed methods and a hybrid end-to-end system for RT event extraction, which is the first natural language processing system for this task. This system provides proof-of-concept for real-world RT data collection for research and is promising for the potential of natural language processing methods to support clinical care.

Blood- and Imaging-Derived Biomarkers for Oncological Outcome Modelling in Oropharyngeal Cancer: Exploring the Low-Hanging Fruit

Aims: To assess whether CT-based radiomics and blood-derived biomarkers could improve the prediction of overall survival (OS) and locoregional progression-free survival (LRPFS) in patients with oropharyngeal cancer (OPC) treated with curative-intent RT. Methods: Consecutive OPC patients with primary tumors treated between 2005 and 2021 were included. Analyzed clinical variables included gender, age, smoking history, staging, subsite, HPV status, and blood parameters (baseline hemoglobin levels, neutrophils, monocytes, and platelets, and derived measurements). Radiomic features were extracted from the gross tumor volumes (GTVs) of the primary tumor using pyradiomics. Outcomes of interest were LRPFS and OS. Following feature selection, a radiomic score (RS) was calculated for each patient. Significant variables, along with age and gender, were included in multivariable analysis, and models were retained if statistically significant. The models’ performance was compared by the C-index. Results: One hundred and five patients, predominately male (71%), were included in the analysis. The median age was 59 (IQR: 52–66) years, and stage IVA was the most represented (70%). HPV status was positive in 63 patients, negative in 7, and missing in 35 patients. The median OS follow-up was 6.3 (IQR: 5.5–7.9) years. A statistically significant association between low Hb levels and poorer LRPFS in the HPV-positive subgroup (p = 0.038) was identified. The calculation of the RS successfully stratified patients according to both OS (log-rank p < 0.0001) and LRPFS (log-rank p = 0.0002). The C-index of the clinical and radiomic model resulted in 0.82 [CI: 0.80–0.84] for OS and 0.77 [CI: 0.75–0.79] for LRPFS. Conclusions: Our results show that radiomics could provide clinically significant informative content in this scenario. The best performances were obtained by combining clinical and quantitative imaging variables, thus suggesting the potential of integrative modeling for outcome predictions in this setting of patients.

Assessment of Electronic Health Record for Cancer Research and Patient Care Through a Scoping Review of Cancer Natural Language Processing

PURPOSE

The advancement of natural language processing (NLP) has promoted the use of detailed textual data in electronic health records (EHRs) to support cancer research and to facilitate patient care. In this review, we aim to assess EHR for cancer research and patient care by using the Minimal Common Oncology Data Elements (mCODE), which is a community-driven effort to define a minimal set of data elements for cancer research and practice. Specifically, we aim to assess the alignment of NLP-extracted data elements with mCODE and review existing NLP methodologies for extracting said data elements.

METHODS

Published literature studies were searched to retrieve cancer-related NLP articles that were written in English and published between January 2010 and September 2020 from main literature databases. After the retrieval, articles with EHRs as the data source were manually identified. A charting form was developed for relevant study analysis and used to categorize data including four main topics: metadata, EHR data and targeted cancer types, NLP methodology, and oncology data elements and standards.

RESULTS

A total of 123 publications were selected finally and included in our analysis. We found that cancer research and patient care require some data elements beyond mCODE as expected. Transparency and reproductivity are not sufficient in NLP methods, and inconsistency in NLP evaluation exists.

CONCLUSION

We conducted a comprehensive review of cancer NLP for research and patient care using EHRs data. Issues and barriers for wide adoption of cancer NLP were identified and discussed.

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.

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.

Breast cancer radiotherapy: What physicians need to know in the era of the precision medicine

Breast cancer is the most common cancer in women worldwide and encompasses a broad spectrum of diseases in one with significant epidemiological, clinical, and biological heterogeneity, which determines a different natural history and prognostic profile. Although classical tumour staging (TNM) still provides valuable information, the current reality is that the clinicians must consider other biological and molecular factors that directly influence treatment decision-making. The management of breast cancer has changed radically in the last 15 years due to significant advances in our understanding of these tumours. This knowledge has brought with it a major impact regarding surgical and systemic management and has been practice-changing, but it has also created significant uncertainties regarding how best integrate the radiotherapy treatment into the therapeutic scheme. In parallel, radiotherapy itself has also experienced major advances, new radiobiological concepts have emerged, and genomic data and other patient-specific factors must now be integrated into individualised treatment approaches. In this context, "precision medicine" seeks to provide an answer to these open questions and uncertainties. The aim of the present review is to clarify the meaning of this term and to critically evaluate its role and impact on contemporary breast cancer radiotherapy.

Insights into the theranostic value of precision medicine on advanced radiotherapy to breast cancer

Breast cancer is the most common cancer in women worldwide. "Breast cancer" encompasses a broad spectrum of diseases (i.e., subtypes) with significant epidemiological, clinical, and biological heterogeneity. Each of these subtypes has a different natural history and prognostic profile. Although tumour staging (TNM classification) still provides valuable information in the overall management of breast cancer, the current reality is that clinicians must consider other biological and molecular factors that directly influence treatment decision-making, including extent of surgery, indication for chemotherapy, hormonal therapy, and even radiotherapy (and treatment volumes). The management of breast cancer has changed radically in the last 15 years due to significant advances in our understanding of these tumours. While these changes have been extremely positive in terms of surgical and systemic management, they have also created significant uncertainties concerning integration of local and locoregional radiotherapy into the therapeutic scheme. In parallel, radiotherapy itself has also experienced major advances. Beyond the evident technological advances, new radiobiological concepts have emerged, and genomic data and other patient-specific factors must now be integrated into individualized treatment approaches. In this context, "precision medicine" seeks to provide an answer to these open questions and uncertainties. Although precision medicine has been much discussed in the last five years or so, the concept remains somewhat ambiguous, and it often appear to be used as a "catch-all" term. The present review aims to clarify the meaning of this term and, more importantly, to critically evaluate the role and impact of precision medicine on breast cancer radiotherapy. Finally, we will discuss the current and future of precision medicine in radiotherapy.

Comparative Effectiveness of Proton vs Photon Therapy as Part of Concurrent Chemoradiotherapy for Locally Advanced Cancer

Importance

Concurrent chemoradiotherapy is the standard-of-care curative treatment for many cancers but is associated with substantial morbidity. Concurrent chemoradiotherapy administered with proton therapy might reduce toxicity and achieve comparable cancer control outcomes compared with conventional photon radiotherapy by reducing the radiation dose to normal tissues.

Objective

To assess whether proton therapy in the setting of concurrent chemoradiotherapy is associated with fewer 90-day unplanned hospitalizations (Common Terminology Criteria for Adverse Events, version 4 [CTCAEv4], grade ≥3) or other adverse events and similar disease-free and overall survival compared with concurrent photon therapy and chemoradiotherapy.

Design, Setting, and Participants

This retrospective, nonrandomized comparative effectiveness study included 1483 adult patients with nonmetastatic, locally advanced cancer treated with concurrent chemoradiotherapy with curative intent from January 1, 2011, through December 31, 2016, at a large academic health system. Three hundred ninety-one patients received proton therapy and 1092, photon therapy. Data were analyzed from October 15, 2018, through February 1, 2019.

Interventions

Proton vs photon chemoradiotherapy.

Main Outcomes and Measures

The primary end point was 90-day adverse events associated with unplanned hospitalizations (CTCAEv4 grade ≥3). Secondary end points included Eastern Cooperative Oncology Group (ECOG) performance status decline during treatment, 90-day adverse events of at least CTCAEv4 grade 2 that limit instrumental activities of daily living, and disease-free and overall survival. Data on adverse events and survival were gathered prospectively. Modified Poisson regression models with inverse propensity score weighting were used to model adverse event outcomes, and Cox proportional hazards regression models with weighting were used for survival outcomes. Propensity scores were estimated using an ensemble machine-learning approach.

Results

Among the 1483 patients included in the analysis (935 men [63.0%]; median age, 62 [range, 18-93] years), those receiving proton therapy were significantly older (median age, 66 [range, 18-93] vs 61 [range, 19-91] years; P < .01), had less favorable Charlson-Deyo comorbidity scores (median, 3.0 vs 2.0; P < .01), and had lower integral radiation dose to tissues outside the target (mean [SD] volume, 14.1 [6.4] vs 19.1 [10.6] cGy/cc × 107; P < .01). Baseline grade ≥2 toxicity (22% vs 24%; P = .37) and ECOG performance status (mean [SD], 0.62 [0.74] vs 0.68 [0.80]; P = .16) were similar between the 2 cohorts. In propensity score weighted-analyses, proton chemoradiotherapy was associated with a significantly lower relative risk of 90-day adverse events of at least grade 3 (0.31; 95% CI, 0.15-0.66; P = .002), 90-day adverse events of at least grade 2 (0.78; 95% CI, 0.65-0.93; P = .006), and decline in performance status during treatment (0.51; 95% CI, 0.37-0.71; P < .001). There was no difference in disease-free or overall survival.

Conclusions and Relevance

In this analysis, proton chemoradiotherapy was associated with significantly reduced acute adverse events that caused unplanned hospitalizations, with similar disease-free and overall survival. Prospective trials are warranted to validate these results.

Initial experience with introducing national guidelines for CT- and MRI-based delineation of organs at risk in radiotherapy

A fundamental problem in radiotherapy is the variation of organ at risk (OAR) volumes. Here we present our initial experience in engaging a large Radiation Oncology (RO) community to agree on national guidelines for OAR delineations. Our project builds on associated standardization initiatives and invites professionals from all radiotherapy departments nationwide. Presently, one guideline (rectum) has successfully been agreed on by a majority vote. Reaching out to all relevant parties in a timely manner and motivating funding agencies to support the work represented early challenges. Population-based data and a scalable methodological approach are major strengths of the proposed strategy.

Head and Neck Cancer Adaptive Radiation Therapy (ART): Conceptual Considerations for the Informed Clinician

For nearly 2 decades, adaptive radiation therapy (ART) has been proposed as a method to account for changes in head and neck tumor and normal tissue to enhance therapeutic ratios. While technical advances in imaging, planning and delivery have allowed greater capacity for ART delivery, and a series of dosimetric explorations have consistently shown capacity for improvement, there remains a paucity of clinical trials demonstrating the utility of ART. Furthermore, while ad hoc implementation of head and neck ART is reported, systematic full-scale head and neck ART remains an as yet unreached reality. To some degree, this lack of scalability may be related to not only the complexity of ART, but also variability in the nomenclature and descriptions of what is encompassed by ART. Consequently, we present an overview of the history, current status, and recommendations for the future of ART, with an eye toward improving the clarity and description of head and neck ART for interested clinicians, noting practical considerations for implementation of an ART program or clinical trial. Process level considerations for ART are noted, reminding the reader that, paraphrasing the writer Elbert Hubbard, "Art is not a thing, it is a way."