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Artificial Intelligence in Adaptive Radiation Therapy
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IOP Publishing
Artificial Intelligence in Adaptive Radiation Therapy
Yi Wang and X. Sharon Qi
Chapter 5
Big data for artificial intelligence in
radiation oncology
Jie Fu*, Sunan Cui* and X. Sharon Qi
This chapter explores the role of big data in advancing artificial intelligence (AI)
applications within radiation oncology. With the increasing volume, variety, and velocity
of data in healthcare, radiation oncology has embraced big data to enhance patient care,
streamline workflows, and drive innovations in precision oncology. The chapter begins
by introducing the foundational concept of big data and identifying common data
sources in radiation oncology. It then describes a typical big data lifecycle and illustrates
how AI is normally used for big data analysis. Examples are presented to demonstrate the
use of big data in AI-driven medical image segmentation, treatment planning, treatment
response prediction, quality assurance (QA), and clinical decision support. These
applications have demonstrated the potential to improve accuracy, efficiency, and
personalized treatment in radiation oncology. The chapter concludes with discussion
of the challenges and future perspectives of big data in radiation oncology.
5.1 Introduction to big data in radiation oncology
5.1.1 Overview of big data
With the rapid advancement of digital technologies, more people worldwide are
gaining easier access to the Internet. This expanded connectivity generates vast
amounts of data daily across our digitalized society. The term ‘big data’ refers to
datasets that are too large and complex to be managed using conventional data
management systems and techniques [1].
Big data is often characterized by several critical dimensions, commonly known
as the ‘Vs’ shown in figure 5.1. Initially conceptualized by Doug Laney in 2001, the
three primary characteristics were encapsulated by the terms: volume, velocity, and
variety [1, 2]. Volume refers to the enormous amount of data produced. Velocity
* Both authors contributed equally to this book chapter.
doi:10.1088/978-0-7503-6119-4ch5 5-1 ª IOP Publishing Ltd 2025. All rights,
including for text and data mining (TDM), artificial intelligence (AI) training, and similar technologies, are reserved.
Artificial Intelligence in Adaptive Radiation Therapy
Figure 5.1. The 5 Vs (volume, velocity, variety, veracity, and value) of big data.
describes the rapid rate at which data are generated and need to be processed, often
in real-time. Additionally, variety addresses the myriad forms of data, such as text,
images, and videos, adding to the complexity of data management and analysis.
As the field of big data has evolved, additional dimensions have been introduced to
address the broader challenges. Veracity focuses on the reliability and accuracy of
data, recognizing that data can be affected by inconsistencies, biases, and anomalies
[3]. The goal of big data collection and analysis is to derive value, i.e., to distill
meaningful and actionable insights from large and complex datasets [4, 5]. This
expanded framework of the Vs offers a comprehensive understanding of big data.
Addressing the challenges of big data necessitates the advancement and adoption
of innovative technologies. The key elements of big data analysis include parallel
and distributed computing, which enable data processing across multiple computing
nodes, and scalable AI models that can maintain or improve performance as data
volume expands [6, 7]. Additionally, the capability for real-time querying allows the
instantaneous retrieval and analysis of data, which is particularly important when
time-sensitive decisions are required [8]. The infrastructures supporting the processing of big data include distributed file systems that allow for data to be stored across
multiple locations, computing clusters that aggregate the processing power of
numerous computers, and cloud computing that offers scalable and on-demand
computing resources [9, 10]. Furthermore, it is essential to design scalable and userfriendly workflows that process data consistently and enable the application of
analytical methods across different datasets.
The advent of big data has not only transformed the landscape of data analytics but
also revolutionized how we use information in the twenty-first century. Analysing big
data holds the potential to unlock insights that were previously unattainable and
facilitatepeopleto make data-drivendecisions[11, 12]. Withinthe healthcaresector, big
data has the potential to help physicians personalize treatment for patients through the
integration of patient-specific data such as genomics, medical history, etc [13]. It also
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Artificial Intelligence in Adaptive Radiation Therapy
drivesinnovationby uncovering new treatment regimensand guiding adaptivetherapy.
Furthermore, it can optimize hospital operations by streamlining workflows, staff
allocation,and resource utilization.Big data also enhancespatient engagement through
customized communication, educational materials, and treatment plans, thereby
increasing adherenceto medical advice. Ultimately, big data could revolutionize cancer
care by enhancing precision, fostering innovation,improving efficiency, and increasing
patient involvement [14–16].
5.1.2 Sources of big data in radiation oncology
Radiation oncologystands at the forefrontof accumulating digitalpatient data, pivotal
for enhancing patient care through novel big data initiatives [16]. The discipline’slongstanding tradition of data collection makes it an ideal landscape for exploring big data.
Collectingand analyzingdatafromlarge cohortsof patientscouldopen new avenuesfor
improved safety, more efficient workflow, and precision oncology [17–19].
Radiation oncology uniquely amalgamates various types of data from patients
and relies heavily on various computer systems to operate [20]. In the conventional
workflow shown in figure 5.2, physicians would retrieve patient data from electronic
health records (EHRs) to assist in disease diagnosis and clinical decision-making.
These data include patient demographics, medical notes, diagnostic images, lab and
test results, drug prescriptions, etc. For example, pathology and genetic tests are
sometimes requested for cancer diagnosis and tailoring radiation treatments. Once
physicians decide to treat patients with radiation therapy, medical images such as
CT, MRI, PET, or other modalities are acquired using the imaging device software.
These images are then sent to the picture archiving and communication system
Figure 5.2. Typical workflow for radiation therapy along with the acquired data. Abbreviations: EHRs =
electronic health records; DVHs = dose-volume histograms.
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Artificial Intelligence in Adaptive Radiation Therapy
(PACS) using digital imaging and communications in medicine (DICOM) formats
for data storage and image review. The acquired images are transferred from PACS
to the treatment planning system (TPS), where the contours of treatment targets and
organs-at-risk (OARs) are delineated, and radiation treatment plans are created
based on the contours and clinical goals. Dose distributions of the generated plans
are calculated for plan evaluation.
After the plan is approved and reviewed, its machine parameters are transferred
from TPS to the radiation oncology information system (ROIS), which records and
verifies every treatment fraction. ROIS also supports patient scheduling, charting,
image review, etc. Treatment plans also require patient-specifi c QA before treatment
delivery. The whole treatment may be delivered via a single fraction in one day or
tens of fractions across several weeks. Before each treatment fraction, the treatment
delivery system (TDS) retrieves the plan parameters from ROIS to treat patients.
Daily set-up images, such as x-rays, CBCT, or MRI, are acquired to assist in patient
positioning. These daily images and the delivered treatment data, such as machine
parameters, are sent from TDS to ROIS for recording and verification. Additionally,
follow-up exams could be conducted between treatment fractions or months after
the treatment completion to evaluate treatment response and patient outcome. All of
these acquired data contribute to radiation oncology big data that can be
characterized using the aforementioned five Vs.
Big data in radiation oncology can be categorized into three types. First,
structured data refers to any data that can be stored in a relational database in
table format with rows and columns. These include patient information such as
gender and age, prescriptions, plan parameters, delivery records, billing codes, etc.
Second, semi-structured data have a structure but do not fit into the relational
database. These consist of clinical notes, diagnosis reports, patient feedback, etc.
JSON and XML are common types of semi-structured data. Lastly, unstructured
data are unorganized and do not fit into the relational database system. Examples
include medical images, text files, audio, video, etc. Integrating these various data
types into a cohesive analytical framework presents significant challenges, necessitating advanced computational technologies and sophisticated data management
systems.
5.1.3 Big data and AI in radiation oncology
The relationship between big data and AI is both symbiotic and integral for
advancing radiation oncology. Big data serves as essential fuel for AI models, in
particular those based on deep learning, to learn and evolve. The more diverse data
these algorithms are exposed to, the more accurate and effective they become. On
the other hand, big data is often too complex for traditional data processing
methods. AI models can process, analyse, and extract meaningful insights from big
data efficiently. For example, natural language processing (NLP) can be leveraged
to extract relevant information from medical notes [21], and AI models can be
trained to identify anomalies and correlations within big data that would be difficult
for humans to identify.
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Artificial Intelligence in Adaptive Radiation Therapy
The integration of big data and AI allows us to improve clinical workflow and
personalize treatment in radiation oncology. For example, AI models could be
trained to improve the quality of diagnostic images and treatment set-up images [22,
23]. AI-based segmentation models could be used to minimize inter-observer
variations and improve the contour delineation efficiency [24]. AI-based automatic
planning also allows us to standardize and shorten the treatment planning workflow
[25]. Furthermore, AI-based predictive modeling could forecast clinical outcomes,
guiding informed clinical decision-making [26].
5.2 Big data lifecycle in radiation oncology
The big data lifecycle refers to the stages through which big data progresses, from its
generation to its utilization and disposal. Figure 5.3 shows the typical big data
lifecycle in radiation oncology, which includes five stages consisting of data
aggregation and storage, data sharing and security, data visualization, knowledge
creation and implementation, and data archive and deletion.
5.2.1 Data aggregation and storage
Data aggregation is a complex process involving the collection of diverse raw data from
various sources, transforming these raw data into a format suitable for advanced
statistical or machine learning analysis. In the realm of radiation oncology, data
originate from multiple channels such as TPS, TDS, PACS, EMR, ROIS, QA software,
simple CSV files, and spreadsheets [27]. Furthermore, breakthroughs in laboratory
technology have amplified the availability of genomic and proteomic information,
adding valuable resources to the field of radiation oncology. There are several
Figure 5.3. Big data lifecycle in radiation oncology: data aggregation and storage, data sharing and security,
data visualization, knowledge creation and implementation, and data archive and deletion. Abbreviations:
HIPAA = health insurance portability and accountability act; PCA = principal component analysis; t-SNE =
t-distributed stochastic neighbor embedding.
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Artificial Intelligence in Adaptive Radiation Therapy
considerations in the process of data collection. Many systems contain a mix of useful
and irrelevant data. Filtering out pertinent information becomes crucial. For instance,
among numerous plans in a TPS, only specific approved or delivered plans might be
relevant for analysis. Data may not always be stored in convenient formats for analysis.
For instance, images exported from PACS systems are often in DICOM format,
necessitating additional processing to obtain 3D images suitable for analysis. Data from
various sources require establishing unified identifiers.Forexample,toderiveanOAR
dose distribution, linking DICOM CT images, RT structure, and RT dose files becomes
essential for future reference and analysis.
The selection of data to collect is tailored to each specific project. Ideally, a
unified data infrastructure should cater to the diverse needs of projects, such as
prospective/retrospective clinical trials, research documentation, and routine clinical
record-keeping. It is crucial to find a balance between making data retrieval easy,
ensuring storage efficiency, and maintaining a comprehensive dataset. This decision
plays a pivotal role in the data collection process.
Once data are gathered, it is important for them undergo further processing to
guarantee their integrity before saving them into the database. Key aspects to focus
on include accuracy, consistency, and completeness. First, the extracted data should
undergo a validation process to confirm their accuracy. Second, if the same variable
exists in different sources, it is prudent to cross-verify for consistency and rectify any
discrepancies encountered. Lastly, ensuring that all essential information is
extracted is vital. In cases where missing data are encountered, exhaustive efforts
should be made to locate them. If unsuccessful, they should be marked as missing
data. This meticulous approach safeguards the reliability of the stored information,
ensuring its usefulness and accuracy for future analyses.
Data storage serves critical functions for archival and future use. It is imperative
to select a storage solution that boasts scalability, enabling seamless accommodation
of escalating data volumes and ensuring efficient data retrieval even with extensive
datasets. Equally vital is the presence of accurate documentation and metadata. This
documentation acts as a guide, aiding in comprehending the stored information for
future applications. Moreover, ensuring data reliability is paramount. This is
achieved through implementing redundancies within the database and adopting
reliable storage solutions. These redundancies serve as safeguards, protecting against
unforeseen database corruption resulting from unexpected hardware failures.
Additionally, regular archiving of the database reinforces these redundancies,
providing an added layer of protection and ensuring data integrity over time.
5.2.2 Data sharing and security
Data sharing plays a pivotal role in maximizing the utility of existing data sources
for several reasons. First, it ensures the verification and reproducibility of research,
fostering transparency within the scientific community. Second, data sharing
facilitates informed decision-making not only within the originating institution
but also across various entities and systems. Additionally, making data accessible to
a wider audience of researchers allows us to accelerate scientific progress and
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