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Artificial Intelligence in Adaptive Radiation Therapy
the delivered dose. There have been promising clinical trial data that demonstrate the
potential benefits of MRI-guided ART [29–31].
Nevertheless, the current approach to treatment adaptation in MR-linac systems
primarily relies on anatomical information to accommodate inter-fractional anatomical changes. It is important to note that the tumor response to radiation may
take several weeks or even months to manifest anatomically [32]. This time delay
presents a critical challenge in optimizing treatment plans using anatomical MRI.
Functional MRI, on the other hand, has the potential to detect early tumor
responses to radiation by assessing functional changes in the tumor microenvironment. This early detection capability opens a critical window for timely treatment
adaptation [33, 34].
In this chapter, we will examine different functional MRI techniques for radiation
treatment guidance, discuss current progress and practicalities of integrating functional MRI into the treatment adaptation, and explore the potential opportunity of
incorporating AI to enhance the ability to optimize radiation treatment strategy.
17.2.1 From anatomy to function: the power of functional MRI in radiation therapy
17.2.1.1 Diffusion-weighted imaging
Diffusion-weighted imaging (DWI) stands as one of the most extensively employed
functional MRI techniques. It uses dephasing and rephasing gradient pulses of
various strengths (quantified as b-values) to cause signal attenuation. The degree of
signal attenuation is related to the strength of gradient pulses as well as the diffusion
of water molecules within tissues. By fitting images acquired with different b-values,
an apparent diffusion coefficient (ADC) map is generated, which reflects the
magnitude of water molecule diffusion within the tissue. Regions with restricted
water diffusion, such as cellular structures or tumor, will display lower ADC values,
while regions with more free water diffusion will have higher ADC values. DWI’s
ability to capture cellularity information has made it a pivotal tool in the detection,
characterization, and monitoring of tumor response. DWI has shown great success
in response prediction for various disease sites including the brain, head and neck,
prostate, rectum, etc [34–36]. For most of the studies, it is observed that responders
will have an increase in ADC compared to non-responders [34]. Yang et al
demonstrated that the DWI images acquired on the low-field MR-linac had
sufficient quality to capture potential radiation treatment effects [37](figure 17.1).
17.2.1.2 Dynamic contrast-enhanced MRI
Dynamic contrast-enhanced MRI (DCE-MRI) is a semi-quantitative measurement
to study blood flow and vascular permeability in tissues. It involves injecting a
contrast agent, usually a gadolinium (Gd)-based contrast agent, into the bloodstream followed by the acquisition of a series of T1-weighted images. These images
provide a time-series visualization of how the contrast agent disperses and washes
out from the tissue, allowing for quantitative analysis of tissue characteristics such as
tissue vascularization, perfusion, capillary permeability, and composition of the
interstitial space. In general, malignant and aggressively growing tumors tend to
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Artificial Intelligence in Adaptive Radiation Therapy
Figure 17.1. Longitudinal diffusion data of a 51 year old head and neck cancer patient. The error bars indicate
standard deviations within the ROI. The average tumor ADC was relatively constant (∼1.5 × 10
during the first three weeks of radiotherapy, and decreased to 1 × 10
treatment. The ADC of the brainstem was relatively constant throughout the treatment with a nonsignificant
linear fit slope of −0.001 × 10
Sons. Copyright 2016 American Association of Physicists in Medicine.)
−3mm2s−1
per day. (Reproduced from [37] with permission from John Wiley &
−3mm2s−1
from week 4 until the end of
−3mm2s−1
exhibit a greater degree of vascularity to supply nutrients to the rapidly proliferating
cells. Therefore, DCE-MRI has been used as a promising tool for tumor diagnosis
and treatment response assessment for many sites such as the brain, breast, rectal,
and cervix [36, 38, 39]. In a phase 2 study, DWI and DCE-MRI were combined to
identify hypercellular and hyperperfused tumor volumes for dose intensification in
GBM patients [40]. They found patients treated with functional boost had promising
outcomes.
17.2.1.3 Intravoxel incoherent motion imaging
Unlike DCE-MRI, which relies on contrast injection to obtain tissue perfusion
information, intravoxel incoherent motion imaging (IVIM) is one special technique
that evaluates perfusion, or microcirculatory blood flow, without a contrast agent
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Artificial Intelligence in Adaptive Radiation Therapy
[41]. IVIM imaging is essentially a variant of DWI that incorporates multiple low
b-values (< 200 s mm
−2
). In the IVIM model, the microcirculation of the blood in
the capillary network would mimic a pseudo-diffusion process, and this perfusion
effect predominantly contributes to the overall signal loss at the low b-value region.
Therefore, bi-exponential fitting can be carried out for concurrent estimation of
diffusion and perfusion. Although IVIM faces several technical challenges, its
capability to concurrently assess diffusion and perfusion without an external
contrast agent makes it an appealing technique in the research setting [42, 43].
Kooreman et al acquired daily IVIM imaging using the 1.5 T MR-linac system on
43 prostate cancer patients [44]. Despite high repeatability coefficients, IVIM
parameter changes caused by radiation were found on a group level.
17.2.1.4 Other functional MRI
There are many other functional MRI techniques that have shown promise in
assessing treatment response. One such technique is chemical exchange saturation
transfer (CEST) MRI. In CEST imaging, a frequency-specific saturation pulse was
first applied to selectively saturate protons on molecules of interest. These
saturated protons will exchange with water protons and cause a signal decrease
in the detected signal. By measuring the signal change in MRI, information on the
molecules of interest can be obtained. There have been promising early results
showing the capability of early treatment response assessment using CEST MRI
for glioblastoma and nasopharyngeal carcinoma [45, 46]. Blood oxygenation leveldependent (BOLD) MRI measures the changes in bold oxygenation as oxygenated
hemoglobin is less magnetic (diamagnetic) compared to deoxygenated hemoglobin
(paramagnetic). Therefore, it has been used to assess tumor oxygenation, hence
treatment response [47, 48]. MR spectroscopy diverges from traditional MRI by
focusing on the spectral profiles of specific isotopes, such as
1H,13
C, or31P, within
the specific voxel. As each m etabolite has a unique spectral fingerprint, MR
spectroscopy can identify and quantify various metabolites in the tissue. While it
has been primarily utilized in the study of brain tumors [49, 50], MR spectroscopy
is also being explored for its applicability in other areas such as the head and neck,
and breast [51, 52]. Despite the potential of CEST, BOLD, and MR spectroscopy
to enhance our understanding of cancer and its microenvironment, their use is
primarily confined to research settings. This is mainly due to the technical
complexity of these techniques and a pressing need for further validation to
establish their clinical utility.
17.2.2 Practicalities and clinical implications of functional MRI-guided adaptive
radiation therapy
While functional MRI has demonstrated significant potential b enefits for the
diagnosis and prognosis of various diseases, its routine clinical application to
enhance patient outcomes necessitates further validation through ran domized
clinical trials. In a randomized phase 3 trial with 571 patients with prostate
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Artificial Intelligence in Adaptive Radiation Therapy
cancer (NCT01168479), multiparametric MRI (T2-weighted, DWI, and DCE)
was used in initial treatment planning to design intraprostatic focal boost [53].
This trial reported that patients receiving fo cal boosts experienced improved
biochemical disease-free survival withou t an increase in toxicit y or a decrease in
quality of life.
Treatment adaptation during the course of treatment is resource-intensive and
time-consuming. Validating the clinical benefits and identifying the optimal time
point for treatment adaptation is important to justify the associated cost. The advent
of commercial MR-linac systems has simplified the logistics of treatment adaptation,
which is now being explored across a wide range of disease sites including the brain,
head and neck, lung, liver, pancreas, prostate, rectum, etc [54, 55]. Evidence from
several completed phase 2 and phase 3 clinical trials underscores the potential
advantages of using MR-linac for patient treatment. In pancreatic cancer, despite
data suggesting dose escalation may improve the local control and overall survival
[56, 57], it is usually not employed due to the association with increased gastrointestinal (GI) toxicity. In a multi-institutional phase 2 trial involving 136 patients
(NCT03621644), a dose escalation strategy of 50 Gy in five fractions was employed
for treating inoperable pancreatic ductal adenocarcinoma with the MR-linac system
[30]. Online treatment adaptation was found dosimetric beneficial and was
performed on 93.1% of the treatment fraction. Acute grade 3 or higher toxicity
possibly or probably attributed to radiation treatment was observed in 8.8% of
cases, with no cases definitively linked to the radiation, meeting the trial’s primary
endpoint. In a randomized phase 3 clinical trial involving 156 patients with prostate
cancer (NCT04384770), patients treated on the MR-linac with a reduced planning
margin had significantly reduced acute GI and genitourinary (GU) toxicity,
although this trial did not implement treatment adaptation [29]. The potential
benefits of incorporating treatment adaptation and simultaneous boost are currently
being explored in several other phase 2 clinical trials (NCT04845503,
NCT05183074).
However, most ART clinical trials on MR-linac systems have primarily focused
on anatomical MRI information, with functional data often under-utilized in study
designs. In an ongoing MR-ADAPTOR trial (NCT03224000) to study dose
adaptation in HPV-positive oropharyngeal cancer, weekly DWI is acquired on the
MR-linac to correlate the ADC changes with the final patient outcome [58]. Outside
MR-linac, a randomized phase 2 study for head and neck cancer with poor
prognosis (NCT02031250) utilized pre-treatment and mid-treatment DCE-MRI
on a diagnostic MRI system to identify tumor subregions with poor perfusion for
targeted focal boost.
While functional MRI holds significant promise for enhancing clinical treatment
adaptation, the integration of functional MRI into clinical treatment adaptation
workflows faces several significant challenges. These include the requirement for
optimized and standardized imaging sequences, the need for thorough evaluation
and validation of imaging biomarkers, and the imperative to develop accurate
predictive models. These challenges will be discussed in section 17.2.4.
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Artificial Intelligence in Adaptive Radiation Therapy
17.2.3 Artificial intelligence in functional MRI-guided adaptive radiation therapy
AI has emerged as a transformative tool in medicine and has the potential to
optimize functional MRI acquisition, improve MRI imaging and parameter map
generation, automate tumor segmentation, and provide valuable insights for treatment response prediction.
17.2.3.1 AI for improved imaging
One big disadvantage of function MRI is its prolonged acquisition times. In DWI, a
large number of averages is usually needed for high b-value images to maintain a
good signal-to-noise ratio (SNR) for diagnosis. AI holds the promise to accelerate
the acquisition and improve the imaging efficiency. For example, AI can enable
image reconstruction from significantly fewer repetitions. Variational networks have
successfully reconstructed images for prostate and liver scans using only half or a
third of the typical data [59, 60]. The quality of AI-reconstructed images, despite
fewer averages, matches that of conventional acquisitions. Another strategy for
acceleration involves denoising images that have been reconstructed with a minimal
number of repetitions [61, 62]. Different denoising networks have been developed for
different organs demonstrating that the image quality from 1 to 2 repetitions can be
comparable to that of traditional DWI protocols, which generally need 10–16
repetitions. Additionally, Hong et al introduced a novel approach using a graph
convolutional neural network for super-resolution (SR) in the slice direction [63].
This method enables faster acquisition by allowing slice-undersampling without
compromising image quality.
Some other issues associated with DWI include its low SNR, low resolution, and
strong spatial distortion associated with the single-shot echo-planar imaging (EPI)
readout. AI can play a significant role in enhancing the image quality for improved
image interpretation. Studies have shown that using deep learning could significantly
boost image SNR and contrast-to-noise ratio (CNR) without impacting the ADC
value [64]. To improve the image resolution, different SR networks have been
proposed to reconstruct higher-resolution images from lower-resolution inputs
[65, 66]. To mitigate spatial distortion, Hu et al proposed a 2D U-Net based
network to correct DWI distortion, which showed reduced distortion compared to
conventional methods such as field-mapping or top-up [67]. Their group later
proposed a generative adversarial network to simultaneously improve the image
resolution as well as reduce the spatial distortion [68].
17.2.3.2 AI for enhanced quantitative mapping
AI’s impact extends to the estimation of functional MRI parameters, offering a more
accurate and reliable analysis. In DCE-MRI, a parametric pharmacokinetic (PK)
model is typically used to fit the time-series MRI and extract physiological parameters
related to perfusion. However, the fitting is usually time-consuming and the obtained
parameter maps may be noisy due to non-convexity of the cost function. To address
these challenges, neural network models have been deployed to streamline the
estimation process, achieving faster speeds and greater accuracy [69–72]. In particular,
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Artificial Intelligence in Adaptive Radiation Therapy
Ottens et al implemented and compared several neural networks in analysing DCE
data, and demonstrated the proposed gated recurrent unit (GRU) method had the
best performance considering test–retest repeatability and robustness of avoiding
potential systematic errors [72].
In IVIM imaging, voxel-wise non-linear least square fitting is the most common
method for parameter fitting. Similar to DCE-MRI, the parameter fitting suffers
from poor precision and noise. Simple artificial neural networks (ANNs), deep
neural networks (DNNs), and convolutional neural networks (CNN) have been
explored to achieve more robust parameter estimation [73–76]. In addition, IVIM
imaging also presents the challenge of requiring a substantial number of b-value
images for accurate fitting, compounded by the lack of a clear scheme for selecting
optimized b-values. Lee et al introduced a DNN framework designed to optimize
b-values and generate IVIM parameter maps simultaneously [77]. Their research
revealed that the selection of optimized b-values is influenced by the level of noise
present in the images.
17.2.3.3 AI for streamlined ART workflow
AI can streamline the adaptation process by automating image segmentation.
Numerous studies have explored the application of AI models to automate
segmentation processes using DWI [78–80] and DCE-MRI [81, 82], demonstrating
the technology’s potential to match, and in some cases, surpass human-level
accuracy. For example, Trebeschi et al showed that combining DWI with other
multiparametric MRI techniques enables more accurate rectal cancer segmentation
using CNN [78]. Chen et al showed that the deep learning based model could detect
and segment lesion that may potentially be missed by human expert [80](figure 17.2).
Figure 17.2. MB-U-Net segmentation of intraprostatic lesions in two cases in the testing set (shown in (a)–(e)
and (f)–(j), respectively). (a) and (f): T2W; (b) and (g): ADC; (c) and (h): DWI (b = 1200 s mm
T2W images overlaid with output probability maps of the MB-U-Net for the lesion class; (e) and (j): T2W
images overlaid with contours of the lesion (the red is ground truth, and the blue is predicted by the MB-UNet). The lower lesion in figure (j) indicated by an orange arrow was not identified in the radiology report and
thus not manually contoured as the ground truth, but it was predicted by the MB-U-Net and agreed with the
corresponding pathological biopsy result. (Reproduced from [
Copyright 2020 American Association of Physicists in Medicine.)
80] with permission from John Wiley & Sons.
−2
); (d) and (i):
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Artificial Intelligence in Adaptive Radiation Therapy
Liang et al developed a square-window based architecture for pancreatic GTV
segmentation based on DCE-MRI acquired on a 1.5 T MR-linac, and showed the
model performance was comparable to human expert [81].
17.2.3.4 AI for treatment response modeling
Lastly, the combination of AI with functional MRI offers substantial potential in
predicting patient-specific responses and is another crucial domain to which AI can
significantly contribute. Various deep learning models have been employed to
predict treatment responses for different diseases using DWI, DCE-MRI, or other
multiparametric MRI techniques [83–85]. Research by Jie et al demonstrated that
pre-treatment DWI imaging features obtained from deep learning had superior
prediction capability than handcrafted features for locally advanced rectal cancer
radiation response [86]. In one particular study conducted on a low-field MR-linac,
longitudinal DWI was acquired pre-, mid-, and post-treatment course for sarcoma
patients [87]. A deep learning network was developed for predicting treatment
response and achieved 97.1% accuracy for patient-based prediction. Yoon et al
investigated the added value of DCE-MRI for local recurrence prediction for grade
4 adult-type diffuse glioma [88]. Their findings revealed that incorporating DCEMRI data significantly enhanced the model’s sensitivity, without affecting
specificity.
17.2.4 Conclusion and future prospects
In summary, functional MRI offers a non-invasive method to capture early
physiological changes within tumors long before these alterations manifest anatomically. This attribute renders functional MRI an invaluable asset in adaptive
radiotherapy, offering the prospect of personalized radiation treatment.
Despite its potential, the transition of functional MRI into routine clinical ART
practice is at a nascent stage, necessitating extensive groundwork to validate its
efficacy and reliability. One first important step is imaging protocol standardization
and optimization. This involves not only refining the protocols to improve the
quality but also ensuring that biomarkers are reproducible across studies and
institutions [89]. Such efforts require a concerted push towards standardization,
which would not only bolster the accuracy of treatment response monitoring but
also enhance collaborative opportunities among research institutions globally.
Second, a key element in the successful integration of functional MRI into ART
is the precise identification and timing of imaging biomarkers. Determining which
biomarkers most accurately reflect the tumor’s response to therapy, and pinpointing
the optimal time points for the acquisition, are crucial steps in ensuring that
monitoring is both efficient and impactful. Furthermore, the development of robust
predictive models based on functional MRI, along with other imaging or clinical
data, is paramount. Such models could significantly enhance the ability of clinicians
to foresee treatment outcomes, enabling proactive adjustments to treatment plans.
This predictive capacity is essential for the realization of truly adaptive radiotherapy. Finally, the formulation of adaptive treatment strategies must be rigorously
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Artificial Intelligence in Adaptive Radiation Therapy
evaluated through clinical trials. Such trials are vital for confirming the clinical
benefits of ART, setting the stage for its broader implementation.
Artificial intelligence could play a pivotal role in enhancing various aspects of this
process, including improving image quality and biomarker generation, selecting the
best biomarkers, optimizing imaging schedules, developing accurate response
prediction models, and automating the treatment adaptation process. By leveraging
AI, the process of integrating functional MRI into ART can be significantly
accelerated, paving the way for a more personalized approach to cancer treatment.
17.3 Summary
This chapter delves into the transformative role of AI in refining adaptive radiation
therapy through advanced functional imaging techniques, specifically PET and
MRI. AI’ s integration into PET and MRI has unlocked new dimensions in
treatment planning and execution, allowing for unprecedented precision in targeting
tumors while sparing healthy tissue. AI algorithms excel in analysing the rich,
complex data provided by PET and MRI, facilitating adaptations to therapy plans
based on the metabolic and biological changes within tumors. This synergy enhances
the capability to predict treatment responses, tailor interventions to individual
patient needs, and ultimately improve clinical outcomes. The confluence of AI with
PET and MRI imaging signifies a major leap towards personalized, dynamic cancer
care, promising a future where radiation therapy is not only more effective but also
significantly safer.
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