Learning healthcare systems and rapid learning in radiation oncology: Where are we and where are we going?

Automation of ePROMs in radiation oncology and its impact on patient response and bias

PURPOSE

This study evaluates the impact of integrating a novel, in-house developed electronic Patient-Reported Outcome Measures (ePROMs) tool with a commercial Oncology Information System (OIS) on patient response rates and potential biases in real-world data science applications.

MATERIALS AND METHODS

We designed an ePROMs tool using the NodeJS web application framework, automatically sending e-mail questionnaires to patients based on their treatment schedules in the OIS. The tool is used across various treatment sites to collect PROMs data in a real-world setting. This research examined the effects of increasing automation levels on both recruitment and response rates, as well as potential biases across different patient cohorts. Automation was implemented in three escalating levels, from telephone reminders for missing reports to minimal intervention from study nurses.

RESULTS

From August 2020 to December 2023, 1,944 patients participated in the PROMs study. Our findings indicate that automating the workflows substantially reduced the patient management workload. However, higher levels of automation led to lower response rates, particularly in collecting late-phase symptoms in breast and head-and-neck cancer cohorts. Additionally, email-based PROMs introduced an age bias when recruiting new patients for the ePROMs study. Nevertheless, age was not a significant predictor of early dropout or missing symptom reports among patients participating. Notably, increased automation was significantly correlated with lower response rates in breast (p = 0.026) and head-and-neck cancer patients (p < 0.001).

CONCLUSION

Integrating ePROMs within the OIS can significantly reduce workload and personnel resources. However, this efficiency may compromise patient responses in certain groups. A balance must be achieved between workload, resource allocation, and the sensitivity needed to detect clinically significant effects. This may necessitate customized automation levels tailored to specific cancer groups, highlighting a fundamental trade-off between operational efficiency and data quality.

An Overview of Real-World Data Infrastructure for Cancer Research

AIMS

There is increasing interest in the opportunities offered by Real World Data (RWD) to provide evidence where clinical trial data does not exist, but access to appropriate data sources is frequently cited as a barrier to RWD research. This paper discusses current RWD resources and how they can be accessed for cancer research.

MATERIALS AND METHODS

There has been significant progress on facilitating RWD access in the last few years across a range of scales, from local hospital research databases, through regional care records and national repositories, to the impact of federated learning approaches on internationally collaborative studies. We use a series of case studies, principally from the UK, to illustrate how RWD can be accessed for research and healthcare improvement at each of these scales.

RESULTS

For each example we discuss infrastructure and governance requirements with the aim of encouraging further work in this space that will help to fill evidence gaps in oncology.

CONCLUSION

There are challenges, but real-world data research across a range of scales is already a reality. Taking advantage of the current generation of data sources requires researchers to carefully define their research question and the scale at which it would be best addressed.

Artificial Intelligence-Based Management of Adult Chronic Myeloid Leukemia: Where Are We and Where Are We Going?

Artificial intelligence (AI) is emerging as a discipline capable of providing significant added value in Medicine, in particular in radiomic, imaging analysis, big dataset analysis, and also for generating virtual cohort of patients. However, in coping with chronic myeloid leukemia (CML), considered an easily managed malignancy after the introduction of TKIs which strongly improved the life expectancy of patients, AI is still in its infancy. Noteworthy, the findings of initial trials are intriguing and encouraging, both in terms of performance and adaptability to different contexts in which AI can be applied. Indeed, the improvement of diagnosis and prognosis by leveraging biochemical, biomolecular, imaging, and clinical data can be crucial for the implementation of the personalized medicine paradigm or the streamlining of procedures and services. In this review, we present the state of the art of AI applications in the field of CML, describing the techniques and objectives, and with a general focus that goes beyond Machine Learning (ML), but instead embraces the wider AI field. The present scooping review spans on publications reported in Pubmed from 2003 to 2023, and resulting by searching "chronic myeloid leukemia" and "artificial intelligence". The time frame reflects the real literature production and was not restricted. We also take the opportunity for discussing the main pitfalls and key points to which AI must respond, especially considering the critical role of the 'human' factor, which remains key in this domain.

Dysphagia at 1 Year is Associated With Mean Dose to the Inferior Section of the Brain Stem

PURPOSE

Dysphagia is a common toxicity after head and neck (HN) radiation therapy that negatively affects quality of life. We explored the relationship between radiation therapy dose to normal HN structures and dysphagia 1 year after treatment using image-based datamining (IBDM), a voxel-based analysis technique.

METHODS AND MATERIALS

We used data from 104 patients with oropharyngeal cancer treated with definitive (chemo)radiation therapy. Swallow function was assessed pretreatment and 1 year posttreatment using 3 validated measures: MD Anderson Dysphagia Inventory (MDADI), performance status scale for normalcy of diet (PSS-HN), and water swallowing test (WST). For IBDM, we spatially normalized all patients' planning dose matrices to 3 reference anatomies. Regions where the dose was associated with dysphagia measures at 1 year were found by performing voxel-wise statistics and permutation testing. Clinical factors, treatment variables, and pretreatment measures were used in multivariable analysis to predict each dysphagia measure at 1 year. Clinical baseline models were found using backward stepwise selection. Improvement in model discrimination after adding the mean dose to the identified region was quantified using the Akaike information criterion. We also compared the prediction performance of the identified region with a well-established association: mean doses to the pharyngeal constrictor muscles.

RESULTS

IBDM revealed highly significant associations between dose to distinct regions and the 3 outcomes. These regions overlapped around the inferior section of the brain stem. All clinical models were significantly improved by including mean dose to the overlap region (P ≤ .006). Including pharyngeal dosimetry significantly improved WST (P = .04) but not PSS-HN or MDADI (P ≥ .06).

CONCLUSIONS

In this hypothesis-generating study, we found that mean dose to the inferior section of the brain stem is strongly associated with dysphagia 1 year posttreatment. The identified region includes the swallowing centers in the medulla oblongata, providing a possible mechanistic explanation. Further work including validation in an independent cohort is required.

Voxel-based analysis: Roadmap for clinical translation

Voxel-based analysis (VBA) allows the full, 3-dimensional, dose distribution to be considered in radiotherapy outcome analysis. This provides new insights into anatomical variability of pathophysiology and radiosensitivity by removing the need for a priori definition of organs assumed to drive the dose response associated with patient outcomes. This approach may offer powerful biological insights demonstrating the heterogeneity of the radiobiology across tissues and potential associations of the radiotherapy dose with further factors. As this methodological approach becomes established, consideration needs to be given to translating VBA results to clinical implementation for patient benefit. Here, we present a comprehensive roadmap for VBA clinical translation. Technical validation needs to demonstrate robustness to methodology, where clinical validation must show generalisability to external datasets and link to a plausible pathophysiological hypothesis. Finally, clinical utility requires demonstration of potential benefit for patients in order for successful translation to be feasible. For each step on the roadmap, key considerations are discussed and recommendations provided for best practice.

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.

On the sensitivity of PROMs during breast radiotherapy

Purpose

To investigate the sensitivity of patient-reported outcome measures (PROMs) to detect treatment-related side effects in patients with breast cancer undergoing external beam photon radiotherapy.

Methods

As part of daily clinical care, an in-house developed PROM tool was used to assess side effects in patients during a) whole-breast irradiation (WBI) to 40 Gy, b) WBI with a sequential boost of 10 Gy, and c) partial-breast irradiation (PBI) to 40 Gy.

Results

414 patients participated in this prospective study between October 2020 and January 2022, with 128 patients (31 %) receiving WBI, 241 (58 %) receiving WBI followed by a sequential boost, and 50 patients (12 %) receiving PBI. Significant differences in the reported toxicities (itching, radiation skin reaction, skin darkening, and tenderness and swelling) were reported between the WBI cohorts with and without boost (p < 0.001, p < 0.001, p < 0.001, and p = 0.002, respectively). The comparison of PBI with WBI (no-boost) yielded significant differences for radiation skin reaction (p < 0.001).

Conclusion

The results highlight the high sensitivity of PROMs to detect treatment-related side effects in patients with breast cancer. Thus, PROMs may be a valuable tool for quality control and may support evidence-based learning from real-world data originating from daily routine care.

Role of Real-World Data in Assessing Cardiac Toxicity After Lung Cancer Radiotherapy

Radiation-induced heart disease (RIHD) is a recent concern in patients with lung cancer after being treated with radiotherapy. Most of information we have in the field of cardiac toxicity comes from studies utilizing real-world data (RWD) as randomized controlled trials (RCTs) are generally not practical in this field. This article is a narrative review of the literature using RWD to study RIHD in patients with lung cancer following radiotherapy, summarizing heart dosimetric factors associated with outcome, strength, and limitations of the RWD studies, and how RWD can be used to assess a change to cardiac dose constraints.